text:
Implementation of Cloud-based Virtual Labs for Educational Purposes
- Eugene Lamb
- 1 years ago
- Views:
1 IJCSNS International Journal of Computer Science and Network Security, VOL.14 No.7, July Implementation of Cloud-based Virtual Labs for Educational Purposes H. A. Ali 1 and Haitham A. El-Ghareeb 2 1 Computers Engineering and Systems Department, Faculty of Engineering, University of Mansoura, Egypt 2 Information Systems Department, Faculty of Computers and Information Sciences, University of Mansoura, Egypt Summary Cloud computing is used to provide users with computer resources on-demand any time over the Internet. elearning systems usually require many hardware and software resources. There are numerous educational institutions that cannot afford such investments, and cloud computing is the best solution for them. For educational purposes lecturers and researchers can leverage cloud computing to enhance their e-learning experience. The main objective of this paper is to present how the cloud computing provides on-demand virtual desktops for problem solving, on-demand virtual labs for special courses. The focus is how cloud services can be used, how they can be integrated into the existing infrastructure, and how new didactic models could look. The proposed solution helps in significant savings when compared to the cost of physical infrastructure procurement and maintenance for the user institution as well as the entire science and higher learning community. Key words: Cloud Computing, IaaS, E-Learning, Virtual Lab 1. Introduction Over the past few years, the idea of cloud computing, a popular trend in IT, has gained much impetus and has become a more popular phrase in information technology [1, 2, 3]. Generally cloud computing may be defined as, a model for enabling convenient, on-demand network access to a shared pool of configurable computing resources (e.g. networks, servers, storage, applications, and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction [4, 5]. Many groups have started implementing these new technologies to further decrease costs through improved machine utilization, reduced administration time and infrastructure costs [6]. Cloud computing is the environment that enables customers to use applications on the Internet such as storing and protecting data while providing a service [7]. So, Cloud computing had been become a convenient to realize instructional principles in learning environments that follow the constructivist view of learning, supporting on demand, self-controlled learning environments. With cloud computing, several service delivery models can be realized, delivering Infrastructure, Platform and Software as a Service to support learning environments and supports an optimized utilization of the university s IT resources, using load balancing and even over-provisioning [8]. Recent studies in the Learning Future of Education in Computer Sector in Egypt highlights two main challenges in ICT Education in Faculties of Computers and Information Sciences in Egypt: (I) Rarity of ICT Faculty members; (II) Lacking to the high performance infrastructure. So, utilizing Cloud Computing in Egyptian Universities can facilitate different scenarios for overcoming this challenge. Being able to provide lab setups for students outside labs, and after lab times is really an interesting point. Students might access their lab scenarios from home, during breaks, and from everywhere. The main objective of this paper is to introduce an approach for cloud computing based solutions to the development of virtual labs. In the proposed approach, Infrastructure as a Service (IaaS) provides virtual machines (VMs) on demand for students of the. These machines are customized for courses and laboratory exercises and provisioned to build virtual laboratories. Platform as a Service (PaaS) goes a step further and offers the students a framework to deploy their developed programming exercises on a well-defined environment. Finally, Software as a Service (SaaS) makes software services, like lecturing assist tools, or development tools, available for multiple users. In summary, cloud computing allows a flexible and adaptive use of computing resources on demand and thus supports powerful learning environments in terms of a constructivist conception of learning in an effective way. Our cloud management system provide different services that are categorized into the well-known cloud service model SaaS, PaaS and IaaS. Obviously, the more specialized (e.g. SaaS) the less customizable by the student [4,7]. This paper goes as follows: Section two introduces the problem definition, section three presents our proposed architecture, section four highlights different research activities we went through during proposed system implementation, finally we present conclusion, and paper ends with references. Manuscript received July 5, 2014 Manuscript revised July 20, 2014
2 46 IJCSNS International Journal of Computer Science and Network Security, VOL.14 No.7, July Problem Definition There is no doubt that the middle-east region suffers from many educational problems in technical fields, especially in ICT (Information and Communication Technologies) domain. The traditionally available solution for enriching the society with high caliber ICT professionals has been provided by international training centers and academies providing proprietary courses at prices greatly exceeding the average income of the developing country. The high cost comes from several factors: Price of the course materials, Cost of properties (office, power, labs, machines, etc.), Value of the training service, and the Small number of attendees per course for higher quality training. Most of these factors are unavoidable and they form the dilemma of ICT training not only in the Middle East or MENA regions but also worldwide elearning solutions that the educational sector is trying to embrace are very traditional old-fashion stored courses or live course streaming systems, such systems provide ugly desktop or Web 1.0 interface at high cost to the educational institution and to the end user. Students in the field of ICT education need to learn problem solving techniques and those cannot be learned without interaction with real-life computer problems on training labs. Those skills include troubleshooting skills, which are considered one of the most problematic skills in terms of requirements from the lab perspective as the instructor need to put the machine into a broken state to test or train the student the capabilities of troubleshooting. The need of a virtualized lab solution is crucial in this particular case as an example. Current solutions are not exciting from the user perspective; statistics show that students tend to spend incomparable time on Facebook and similar social media websites than on elearning and educational online systems, this has taught us that the blend of social media and education is crucial for incomparable user experience and ultimate efficiency of the educational process [5, 8]. 2.1 The Proposed Solution The first part of the solution is Virtual Lab concept, a virtual lab takes programs running on college hardware and beams the images to any computer desktop across the internet, giving students the ability to create and save work as though the programs were running on their own hard drives [8, 9, 10]. The software s performance depends on the strength of the student s Internet connection as opposed to the processing power of their computer, so even students with older computers can use advanced software without difficulty. Such concepts also, need a high performance infrastructure [11]. The second part of the solution is the cloud computing; A cloud is a powerful combination of cloud computing, networking, storage, management solutions, and business applications that facilitate a new generation of IT and consumer services [9]. These services are available on demand and are delivered economically without compromising security or functionality [12]. To take full advantage of cloud computing, enterprises need to evolve their IT strategy to achieve greater business value through the improved cost-savings and productivity that cloud services offer. Service providers need to offer and monitor cloud-computing services to serve their customers even more effectively while improving profitability Cloud computing trend is the key behind an innovative solution to the current problems illustrated earlier and a proper design and implementation might be a lifechanging solution to technology education around the world. Cloud computing model incorporates technologies like Virtualization, Software-as-a-Service, that is a brilliant solution to the current problems we are facing in the traditional technology training methodology and the sub-optimal elearning solutions in common use today. Utilizing cloud-computing model for elearning provides several advantages. First and foremost, an Internet scale solution that can easily scale to thousands or millions of online concurrent system users. Enabling wide scale revolutionary deployment to large masses of students. The proposed solution is to provide an elearning solution that leverages current IT infrastructure, and presents enhanced pedagogical aspects of the e-courses. E Learning is far deeper than e-courses. Our solution includes Pedagogical Objectives and Technical Objectives. 2.2 Technical Objectives Proposed solution incorporates different technical objectives to meet the required goal. Our technical objectives include: Build a web-scale cloud-based learning management system with extremely modular design and multilingual, highly customizable, front-end Web-based UI with cutting edge web technologies like HTML5/CSS3 and AJAX. Integrate Cloud-based virtual training labs with the learning management system to provide interactive intelligent virtual training environment for ICT training that include computer networking, computer programming, Server Administration, database administration, and advanced high-end technical ICT courses. Offer on-demand cloud hosted virtual labs with support for snapshots. Snapshots allow students to easily take snapshots at any given moment during the lab time, in effect freezing the lab and providing a point-in-time that they can return to anytime later, or share via the built-in collaboration platform with their instructor or peers. Virtual training labs that include complete virtually isolated network with multiple machines to demonstrate given deliverable by course designer / instructor.
3 IJCSNS International Journal of Computer Science and Network Security, VOL.14 No.7, July Build the Intelligent Lab Advisor embedded in the virtual labs. The Intelligent Lab Advisor intelligently detects the student achieving the required training goals, as well as guides the student along the training path, effectively offering pre-packaged top-notch instructor experience and allowing for a much enhanced self-study (or group-study) experience. Utilize the cloud computing Pay-as-you-go business model so users only pay for the time they use on virtual labs, thus providing cost-effective access to the large number of computing resources during ICT training Build a content delivery and hosting network for true infinite scaling; federated content hosting might also be added on the platform later. Building mobile support through standard web implementation; that leverages the capabilities of smartphones available in the market these days. 3. The Proposed Architecture The proposed architecture is intended for designing and configuring a cloud computing system that; serve the educational of the university in a very economical and cost efficient manner. It delivers a range of functionalities and services that map well onto the cloud computing requirements and expectations. There are few principal components in proposed architecture as shown in Figure 1. Figure 1: General overview of the proposed solution Architecture In the proposed solution the virtualized labs are hosted on a publicly accessible cloud-based computer infrastructure that is connected through high-speed high-throughput redundant Internet connection. The on-demand instances of virtual labs run on cluster of physical servers that are hosted in remote or local datacenter, the physical infrastructure is designed for high-availability, redundancy and future scalability. Virtualized Labs can be accessed from many internet-enabled devices, which include smartphones (iphone, Android, etc.) and any computer. Figure 2 depicts a high level overview of the various technologies and architectural choices that are used. Figure 2: High level overview of the propose architecture 3.1 Proposed Architecture Components The proposed architecture is composed of the following components: Web and Application Servers: The Web and Application servers serve the primary web interface for the cloud application. Distributed Document-Based Database: Distributed document-based databases have recently gained wide adoption on Internet scale applications. A Distributed document-based Database does not store its information in rigidly defined tables similar to the more traditional RDBMS systems; however, each record is stored as a document. Each document consists of multiple fields of data that serve a similar role to columns in the traditional RDBMS world. Cloud Middle-Ware: The Cloud Middle-Ware is the internal plumbing layer underneath the cloud. It is what makes the whole system one unified entity. In the proposed solution we take the elearning solution from the cutting-edge SOA (Service Oriented Architecture) to the bleeding-edge Cloud Computing Architecture. The Cloud Middle-Ware is an essential component being the core layer that connects various subsystems together. The messaging bus connects together various sub-systems such as: (I) Central Authentication Service, (II) Monitoring and Auditing Framework, (III) Cloud Manager, (IV) Configuration Management Framework, (V) Logging Framework, and (VI) and Billing System. 3.2 Middleware Cloud Design Designing the Midlleware Cloud include the following activities: 1. Researching the best technique for synchronous / asynchronous scalable message-driven bus system. 2. Design of groundbreaking logging technology that is decentralized for maximum throughput and asynchronous to ensure the maximum efficiency and accuracy of the logging system.
5 IJCSNS International Journal of Computer Science and Network Security, VOL.14 No.7, July locally. There are various professional third party, open source and freeware remote desktop applications, some of which are cross platform across various versions of Windows, Mac, and UNIX / Linux / BSD. The main remote desktop protocols foreseen to be utilized in proposed architecture are Virtual Network Computing (VNC) a cross platform protocol and Remote Desktop Protocol (RDP) a Windows specific protocol. This kind of component interface is intended for human interaction allowing remote administration, observation, debugging, configuration of components in a comfortable manner. Communication between Host and guest operating system on LXC virtualization environment is to be done with internal UNIX sockets and named pipes. Performance is proved to be at the maximum because of the shared memory nature of LXC. Emerging Cloud Platforms: Industry analysts have made bullish projections on how Cloud computing will transform the entire computing industry. Table 1, compare six representative Cloud platforms with industrial linkages Table 1: Comparison of Some Representative Cloud Platforms 5. Conclusion The development of elearning solution cannot ignore the cloud computing trends. On Demand cloud based virtual lab provides completely customizable training solutions for the needs of your organization. From a simple instructor-led virtual classroom to hosted labs employing cloud technology, to a total training package using our very own Learning Management System; we work with individual clients to tailor a reliable custom solution for each client's specific needs. Gone are the days of trying to configure software for an entire computer lab or expensive travel costs associated with on-site training. Let On Demand virtual lab ' affordable and flexible solutions take the headaches out of corporate training. References [1] H.E. Schaffer, S.F. Averitt, M.I. Hoit, A. Peeler, E.D. Sills, M.A. Vouk, NCSU s Virtual Computing Lab: A Cloud Computing Solution, Computer, vol.42, no.7,pp.94-97, July [2] D. Hamilton. 'Cloud computing' seen as next wave for technology investors. Financial Post, 4 June [3] R. Buyya, C. S. Yeo, and S. Venugopal. Market-Oriented Cloud Computing: Vision, Hype, and Reality for Delivering IT Services as Computing Utilities. In Proceedings of the 10th IEEE International Conference on High Performance Computing and Communications (HPCC 2008), Dalian, China, Sept [4] D. Kasi Viswanath, S. Kusuma and Saroj Kumar Gupta, Cloud Computing Issues and Benefits Modern Education, Global Journal of Computer Science and Technology: Cloud and Distributed, Volume 12 Issue 10 Version 1.0, July [5] M. Nagappan, M.A. Vouk, Adaptive Logging: A Case Study of Logs from a Cloud Computing Environment. In the Fast Abstracts track of International Symposium on Software Reliability Engineering, 1-4 Nov, 2010, San Jose, California. [6] A. Fernandez, D. Peralta, F. Herrera and J.M. Benitez, An Overview of E-Learning in Cloud Computing, L. Uden et al. (Eds.): Workshop on LTEC 2012, AISC 173, pp Springer- Verlag Berlin Heidelberg [7] Thongchai Kaewkiriya and Nattavee Utakrit, A Model of an ELearning Management System Based on Cloud Computing and Web Service, Information Technology Journal, Vol.8, No.1, January-June [8] H.E. Schaffer, S.F. Averitt, M.I. Hoit, A. Peeler, E.D. Sills, M.A. Vouk, NCSU s Virtual Computing Lab: A Cloud Computing Solution, Computer, vol.42, no.7, pp.94-97, July [9] M. Vouk, A. Rindos, S. Averitt, J. Bass, M. Bugaev, A. Peeler, H. Schaffer, E. Sills, S. Stein, J. Thompson, M.P. Valenzisi, Using VCL Technology to Implement Distributed Reconfigurable Data Centers and Computational Services for Educational Institutions. IBM Journal of Research and Development, Vol. 53, No. 4, pp. 2:1-18, [10] Rajkumar Buyya, Chee Shin Yeo, Srikumar Venugopal, James Broberg, and Ivona Brandic, " Cloud Computing and Emerging IT Platforms:Vision, Hype, and Reality for Delivering Computing as the 5th Utility", Future Generation Computer Systems. Volume 25, Issue 6, June [11] Lijun Mei,W.K. Chang, T.H. Tse. A Tale of Clouds: Paradigm Comparisons and Some Thoughts on Research Issues. Proceedings of the 2008 IEEE Asia-Pacific Services Computing Conference (APSCC 2008), IEEE Computer Society Press, Los Alamitos, CA (2008).
Keywords: Cloudsim, MIPS, Gridlet, Virtual machine, Data center, Simulation, SaaS, PaaS, IaaS, VM. Introduction
Vol. 3 Issue 1, January-2014, pp: (1-5), Impact Factor: 1.252, Available online at: www.erpublications.com Performance evaluation of cloud application with constant data center configuration and variableMore information
A Survey on Cloud Computing
A Survey on Cloud Computing Poulami dalapati* Department of Computer Science Birla Institute of Technology, Mesra Ranchi, India email@example.com G. Sahoo Department of Information Technology BirlaMore information
AN IMPLEMENTATION OF E- LEARNING SYSTEM IN PRIVATE CLOUD M. Lawanya Shri 1, Dr. S. Subha 2 1 Assistant Professor,School of Information Technology and Engineering, Vellore Institute of Technology, Vellore-632014More information
E-LEARNING DEVELOPMENT AS PUBLIC INFRASTRUCTURE OF CLOUD COMPUTING 1 DANNY MANONGGA, 2 WIRANTO HERRY UTOMO, 3 HENDRY 1 Information System Department, Satya Wacana Christian University 2 Information SystemMore information
Cloud Computing in Higher Education: Impact and Challenges
Cloud Computing in Higher Education: Impact and Challenges Anju Gautam Research Scholar, Jagannath University, Jaipur Abstract: Cloud computing is a slogan at present days. It has altered the entire stateMore information
Role of Spatial Database in Virtual Networking - Cloud Computing
Role of Spatial Database in Virtual Networking - Cloud Computing DR. NEERAJ BHARGAVA Associate Professor, Department of Computer Science School of Engineering & System Sciences, MDS University, Ajmer email@example.com information
Applications' of Cloud Computing in Academic Institutions
Applications' of Cloud Computing in Academic Institutions Muhammad Mannir Ahmad Getso University of East London Kuala Lumpur Malaysia email@example.com Riyaz Ahmed A. H. School of Engineering & ComputingMore information
International Journal of Advanced Technology in Engineering and Science www.ijates.com DESIGN OF AGENT BASED SYSTEM FOR MONITORING AND CONTROLLING SLA IN CLOUD ENVIRONMENT Sarwan Singh 1, Manish AroraMore information
Role of Cloud Computing in Education
Role of Cloud Computing in Education Kiran Yadav Assistant Professor, Dept. of Computer Science. Govt. College for Girls, Gurgaon, India ABSTRACT: Education plays an important role in maintaining the economicMore information
A Study on Analysis and Implementation of a Cloud Computing Framework for Multimedia Convergence Services
A Study on Analysis and Implementation of a Cloud Computing Framework for Multimedia Convergence Services Ronnie D. Caytiles and Byungjoo Park * Department of Multimedia Engineering, Hannam UniversityMore information
Cloud Computing : Concepts, Types and Research Methodology
Cloud Computing : Concepts, Types and Research Methodology S. Muthulakshmi Bangalore,Karnataka India- 560068 Abstract: Cloud -computing is a very popular term in this modern and computer world in IT solutionMore information
Sistemi Operativi e Reti. Cloud Computing
1 Sistemi Operativi e Reti Cloud Computing Facoltà di Scienze Matematiche Fisiche e Naturali Corso di Laurea Magistrale in Informatica Osvaldo Gervasi email@example.com 2 Introduction TechnologiesMore information
An Extension of Virtual Computing Laboratories: Scalable Virtual Desktop Infrastructure on Cloud Computing
, pp.85-92 http://dx.doi.org/10.14257/ijmue.2014.9.4.09 An Extension of Virtual Computing Laboratories: Scalable Virtual Desktop Infrastructure on Cloud Computing Dongho Song 1, Ji-Ae Shin 2 and YeonjinMore information
BigData Management and Analytics in the Cloud** In the Contenxt of the Materials Genome Initiative
BigData Management and Analytics in the Cloud** In the Contenxt of the Materials Genome Initiative Mladen A. Vouk* North Carolina State University, Raleigh, NC (*) Professor and Head of Computer ScienceMore information
Cloud Computing. Course: Designing and Implementing Service Oriented Business Processes
Cloud Computing Supplementary slides Course: Designing and Implementing Service Oriented Business Processes 1 Introduction Cloud computing represents a new way, in some cases a more cost effective way,More information
Software-Defined Networks Powered by VellOS
WHITE PAPER Software-Defined Networks Powered by VellOS Agile, Flexible Networking for Distributed Applications Vello s SDN enables a low-latency, programmable solution resulting in a faster and more flexibleMore information
CLOUD COMPUTING. Keywords: Cloud Computing, Data Centers, Utility Computing, Virtualization, IAAS, PAAS, SAAS.
CLOUD COMPUTING Mr. Dhananjay Kakade CSIT, CHINCHWAD, Mr Giridhar Gundre CSIT College Chinchwad Abstract: Cloud computing is a technology that uses the internet and central remote servers to maintain dataMore information
Mr Dinesh G Umale Saraswati College,Shegaon (Department of MCA) CLOUD COMPUTING IN HIGHER EDUCATION Abstract Technology has grown rapidly with scientific advancement over the world in recent decades. Therefore,More information
Getting Familiar with Cloud Terminology. Cloud Dictionary
Getting Familiar with Cloud Terminology Cloud computing is a hot topic in today s IT industry. However, the technology brings with it new terminology that can be confusing. Although you don t have to knowMore information
Cloud Based E-Government: Benefits and Challenges
Cloud Based E-Government: Benefits and Challenges Saleh Alshomrani 1 and Shahzad Qamar 2 1 Faculty of Computing and IT, King Abdulaziz University, Jeddah, Saudi Arabia 2 Faculty of Computing and IT, NorthMore information
Service-Oriented Architecture for Cloud Computing
Service-Oriented Architecture for Cloud Computing V.E.Unnamalai, J.R.Thresphine Department of Computer Science and Engineering, PRIST University Pondicherry, India. Abstract----- Cloud computing is a significantMore information
Education Developmental Transformation (EDT)
Cloud Computing and Sustainable Development in Higher Education 1 Sunday A. Idowu, 2 Adenike.O.Osofisan 1 Babcock University, Ilishan-Remo, Nigeria 2 University of Ibadan, Ibadan, Nigeria ABSTRACT SustainableMore information
Cloud Computing Architecture: A Survey
Cloud Computing Architecture: A Survey Abstract Now a day s Cloud computing is a complex and very rapidly evolving and emerging area that affects IT infrastructure, network services, data management andMore information
Powered by VCL - Using Virtual Computing Laboratory (VCL) Technology to Power Cloud Computing
Powered by VCL - Using Virtual Computing Laboratory (VCL) Technology to Power Cloud Computing Mladen A.Vouk, Sam Averitt, Michael Bugaev, Andy Kurth, Aaron Peeler, Henry Shaffer, Eric Sills, Sarah Stein,More information
An Overview on Important Aspects of Cloud Computing
An Overview on Important Aspects of Cloud Computing 1 Masthan Patnaik, 2 Ruksana Begum 1 Asst. Professor, 2 Final M Tech Student 1,2 Dept of Computer Science and Engineering 1,2 Laxminarayan InstituteMore information
Profit Maximization Of SAAS By Reusing The Available VM Space In Cloud Computing
www.ijecs.in International Journal Of Engineering And Computer Science ISSN: 2319-7242 Volume 4 Issue 8 Aug 2015, Page No. 13822-13827 Profit Maximization Of SAAS By Reusing The Available VM Space In CloudMore information
Analysis of Cloud Solutions for Asset Management
ICT Innovations 2010 Web Proceedings ISSN 1857-7288 345 Analysis of Cloud Solutions for Asset Management Goran Kolevski, Marjan Gusev Institute of Informatics, Faculty of Natural Sciences and Mathematics,More information
Cloud Computing For Distributed University Campus: A Prototype Suggestion
Cloud Computing For Distributed University Campus: A Prototype Suggestion Mehmet Fatih Erkoç, Serhat Bahadir Kert firstname.lastname@example.com, firstname.lastname@example.com Yildiz Technical University (Turkey) AbstractMore information
A PERFORMANCE ANALYSIS of HADOOP CLUSTERS in OPENSTACK CLOUD and in REAL SYSTEM
A PERFORMANCE ANALYSIS of HADOOP CLUSTERS in OPENSTACK CLOUD and in REAL SYSTEM Ramesh Maharjan and Manoj Shakya Department of Computer Science and Engineering Dhulikhel, Kavre, Nepal firstname.lastname@example.com,More information
Virtualizing the Private Cloud for Maximum Resource Utilization
Virtualizing the Private Cloud for Maximum Resource Utilization C.Shreeharsha, Prof.ManasiKulkarni Computer Engineering Department, VJTI, Matunga, Mumbai, India, E-mail:email@example.com. AbstractMore information
A Load Balancing Model Based on Cloud Partitioning for the Public Cloud
International Journal of Information & Computation Technology. ISSN 0974-2239 Volume 4, Number 16 (2014), pp. 1605-1610 International Research Publications House http://www. irphouse.com A Load BalancingMore information
IJRSET 2015 SPL Volume 2, Issue 11 Pages: 29-33
CLOUD COMPUTING NEW TECHNOLOGIES 1 Gokul krishnan. 2 M, Pravin raj.k, 3 Ms. K.M. Poornima 1, 2 III MSC (software system), 3 Assistant professor M.C.A.,M.Phil. 1, 2, 3 Department of BCA&SS, 1, 2, 3 SriMore information
Cloud Computing. Karan Saxena * & Kritika Agarwal**
Page29 Cloud Computing Karan Saxena * & Kritika Agarwal** *Student, Sir M. Visvesvaraya Institute of Technology **Student, Dayananda Sagar College of Engineering ABSTRACT: This document contains basicMore information
Lecture 02b Cloud Computing II
Mobile Cloud Computing Lecture 02b Cloud Computing II 吳 秀 陽 Shiow-yang Wu T. Sridhar. Cloud Computing A Primer, Part 2: Infrastructure and Implementation Topics. The Internet Protocol Journal, Volume 12,More information
GigaSpaces Real-Time Analytics for Big Data
GigaSpaces Real-Time Analytics for Big Data GigaSpaces makes it easy to build and deploy large-scale real-time analytics systems Rapidly increasing use of large-scale and location-aware social media andMore information
Virtualization: A Concept Implementation for Cloud
International Journal of Engineering and Technical Research (IJETR) ISSN: 2321-0869, Volume-2, Issue-3, March 2014 Virtualization: A Concept Implementation for Cloud Mayank Aggrawal, Nishant Kumar, ApoorvMore information
CHAPTER 8 CLOUD COMPUTING SE 458 SERVICE ORIENTED ARCHITECTURE Assist. Prof. Dr. Volkan TUNALI Faculty of Engineering and Natural Sciences / Maltepe University Topics 2 Cloud Computing Essential CharacteristicsMore information
IT Game Changers: How Cloud, Mobile, Social and Big Data are Redefining Your IT Environment
cecmg Annual Meeting / March, 2015 IT Game Changers: How Cloud, Mobile, Social and Big Data are Redefining Your IT Environment Glenn Anderson IBM Lab Services and Training 2013 IBM Corporation 2 (c) CopyrightMore information
20 th Year of Publication. A monthly publication from South Indian Bank. www.sib.co.in
To kindle interest in economic affairs... To empower the student community... Open YAccess www.sib.co.in firstname.lastname@example.com A monthly publication from South Indian Bank 20 th Year of Publication ExperienceMore information
Proposal for a Cloud Computing solution and application in a pedagogical virtual organization
Proposal for a Cloud Computing solution and application in a pedagogical virtual organization Amadou Dahirou Gueye 1, Ibrahima Sanogo 2, Samuel Ouya 3, Hamadou Saliah-Hassane 4 and Claude Lishou 5 1 UniversityMore information
Expert Reference Series of White Papers. Understanding Data Centers and Cloud Computing
Expert Reference Series of White Papers Understanding Data Centers and Cloud Computing 1-800-COURSES www.globalknowledge.com Understanding Data Centers and Cloud Computing Paul Stryer, Global KnowledgeMore information
THE IMPACT OF CLOUD COMPUTING ON ENTERPRISE ARCHITECTURE. Johan Versendaal
THE IMPACT OF CLOUD COMPUTING ON ENTERPRISE ARCHITECTURE Johan Versendaal HU University of Applied Sciences Utrecht Nijenoord 1, 3552 AS Utrecht, Netherlands, firstname.lastname@example.com Utrecht UniversityMore information
A Survey on Build Private Cloud Computing implementation tools 1 Rana M Pir, 2 Rumel M S Pir, 3 Imtiaz U Ahmed 1 Lecturer, 2 Assistant Professor, 3 Lecturer 1 Leading University, Sylhet Bangladesh, 2 LeadingMore information
Business Intelligence (BI) Cloud. Prepared By: Pavan Inabathini
Business Intelligence (BI) Cloud Prepared By: Pavan Inabathini Summary Federal Agencies currently maintain Business Intelligence (BI) solutions across numerous departments around the enterprise with individualMore information
Towards Cloud Computing at IS Department, King Abdulaziz University
4 Towards Cloud Computing at IS Department, King Abdulaziz University Muhammad Haleem Information Systems Department Faculty of Computing and Information Technology King Abdulaziz University, Jeddah, SaudiMore information
Cloud Computing Essentials Course Outline
Cloud Computing Essentials Course Outline Find out everything you need to know about cloud computing on this 4-day interactive course. About CYBERMODO: CYBERMODO is a professional Education Center thatMore information
CLOUD COMPUTING ARCHITECTURE FOR HIGHER EDUCATION IN THE THIRD WORLD COUNTRIES (REPUBLIC OF THE SUDAN AS MODEL) Mohmed Sirelkhtem Adrees 1, Majzoob Kamal Aldein Omer 2 and Osama E. Sheta 3 1 DepartmentMore information
Round Robin with Server Affinity: A VM Load Balancing Algorithm for Cloud Based Infrastructure
J Inf Process Syst, Vol.9, No.3, September 2013 pissn 1976-913X eissn 2092-805X http://dx.doi.org/10.3745/jips.2013.9.3.379 Round Robin with Server Affinity: A VM Load Balancing Algorithm for Cloud BasedMore information
The Client Side of Cloud Computing
Cloud Clients Service Look-Up Resumé Literature SE aus Informatik, SS 2009 26. Mai 2009 Cloud Clients Service Look-Up Resumé Literature 1 Cloud Clients Definition Hardware Clients Software Clients SoftwareMore information
Cloud Computing for SCADA
Cloud Computing for SCADA Moving all or part of SCADA applications to the cloud can cut costs significantly while dramatically increasing reliability and scalability. A White Paper from InduSoft LarryMore information
Cloud Computing-based IT Solutions For Organizations with Multiregional Branch Offices
Cloud Computing-based IT Solutions For Organizations with Multiregional Branch Offices Harris Wang School of Computing and Information Systems Athabasca University, Athabasca, Canada firstname.lastname@example.com information
A Comparative Analysis: Grid, Cluster and Cloud Computing
A Comparative Analysis: Grid, Cluster and Cloud Computing Kiranjot Kaur 1, Anjandeep Kaur Rai 2 M.Tech Scholar, Department of Computer Science and Engineering,Lovely Professional University, Phagwara,More information
Quattra s Cloud Vision & Framework Value
Quattra s Cloud Vision & Framework Value Data centers provide the foundation for the applications and services that organizations deliver, and companies need their IT facilities to be reliable, compliantMore information
Video Collaboration & Application Sharing Product Overview
. Video Collaboration & Application Sharing Product Overview Overview NPL s Collaborative Real-Time Information Sharing Platform (CRISP ) combines high quality video collaboration, remote application sharingMore information
A Study on Service Oriented Network Virtualization convergence of Cloud Computing
A Study on Service Oriented Network Virtualization convergence of Cloud Computing 1 Kajjam Vinay Kumar, 2 SANTHOSH BODDUPALLI 1 Scholar(M.Tech),Department of Computer Science Engineering, Brilliant InstituteMore information
THE QUEST FOR A CLOUD INTEGRATION STRATEGY ENTERPRISE INTEGRATION Historically, enterprise-wide integration and its countless business benefits have only been available to large companies due to the highMore information
Tamanna Roy Rayat & Bahra Institute of Engineering & Technology, Punjab, India email@example.com
IJCSIT, Volume 1, Issue 5 (October, 2014) e-issn: 1694-2329 p-issn: 1694-2345 A STUDY OF CLOUD COMPUTING MODELS AND ITS FUTURE Tamanna Roy Rayat & Bahra Institute of Engineering & Technology, Punjab, IndiaMore information
Requirement Analysis Document v.2 14.12.2009 CENG-401 SOFTWARE ENGINEER PROJECT MANAGEMENT SYSTEM (Project Manager) Ahmet Edip SEÇKİN 07010555 (Developer) Erhan ŞEN 07010507 (Developer) Semih Serdar CENGİZOĞLUMore information
Towards a New Model for the Infrastructure Grid
INTERNATIONAL ADVANCED RESEARCH WORKSHOP ON HIGH PERFORMANCE COMPUTING AND GRIDS Cetraro (Italy), June 30 - July 4, 2008 Panel: From Grids to Cloud Services Towards a New Model for the Infrastructure GridMore information
An Introduction to Cloud Computing Concepts
Software Engineering Competence Center TUTORIAL An Introduction to Cloud Computing Concepts Practical Steps for Using Amazon EC2 IaaS Technology Ahmed Mohamed Gamaleldin Senior R&D Engineer-SECC firstname.lastname@example.com information
SCADA Cloud Computing
SCADA Cloud Computing Information on Cloud Computing with SCADA systems Version: 1.0 Erik Daalder, Business Development Manager Yokogawa Electric Corporation Global SCADA Center T: +31 88 4641 360 E: firstname.lastname@example.com information
WhitePaper. Private Cloud Computing Essentials
Private Cloud Computing Essentials The 2X Private Cloud Computing Essentials This white paper contains a brief guide to Private Cloud Computing. Contents Introduction.... 3 About Private Cloud Computing....More information
Security Considerations for Public Mobile Cloud Computing
Security Considerations for Public Mobile Cloud Computing Ronnie D. Caytiles 1 and Sunguk Lee 2* 1 Society of Science and Engineering Research Support, Korea email@example.com 2 Research Institute ofMore information
email@example.com email@example.com
1 The following is merely a collection of notes taken during works, study and just-for-fun activities No copyright infringements intended: all sources are duly listed at the end of the document This workMore information
Networking for Caribbean Development
Networking for Caribbean Development BELIZE NOV 2 NOV 6, 2015 w w w. c a r i b n o g. o r g Virtualization: Architectural Considerations and Implementation Options Virtualization Virtualization is theMore information
Computing Service Provision in P2P Clouds
Computing Service Provision in P2P Clouds Ghislain FOUODJI TASSE Supervisor: DR. Karen BRADSHAW Department of Computer Science Rhodes University Research Statement Leverage advantages of cloud computingMore information
Lecture 02a Cloud Computing I
Mobile Cloud Computing Lecture 02a Cloud Computing I 吳 秀 陽 Shiow-yang Wu What is Cloud Computing? Computing with cloud? Mobile Cloud Computing Cloud Computing I 2 Note 1 What is Cloud Computing? WalkingMore information
Permanent Link: http://espace.library.curtin.edu.au/r?func=dbin-jump-full&local_base=gen01-era02&object_id=154091
Citation: Alhamad, Mohammed and Dillon, Tharam S. and Wu, Chen and Chang, Elizabeth. 2010. Response time for cloud computing providers, in Kotsis, G. and Taniar, D. and Pardede, E. and Saleh, I. and Khalil,More information
SOA and Cloud in practice - An Example Case Study
SOA and Cloud in practice - An Example Case Study 2 nd RECOCAPE Event "Emerging Software Technologies: Trends & Challenges Nov. 14 th 2012 ITIDA, Smart Village, Giza, Egypt Agenda What is SOA? What isMore information
http://www.cisjournal.org A Conceptual Architectural Framework of Cloud Computing for Higher Educational Institutions in the Sultanate of Oman 1
A Conceptual Architectural Framework of Cloud Computing for Higher Educational Institutions in the Sultanate of Oman 1 Zeyana Saif Alkindi, 2 John Haynes, 3 S. Arockiasamy 1 Demonstrator, Information Systems,More information
Managing the Cloud as an Incremental Step Forward
WP Managing the Cloud as an Incremental Step Forward How brings cloud services into your IT infrastructure in a natural, manageable way white paper email@example.com Table of Contents Accepting theMore information
International Journal of Computer Engineering and Applications, Volume VIII, Issue II, November 14 FEDERATED CLOUD: A DEVELOPMENT IN CLOUD COMPUTING AND A SOLUTION TO EDUCATIONAL NEEDS Saju Mathew 1, Dr.More information
Cloud Computing. Adam Barker
Cloud Computing Adam Barker 1 Overview Introduction to Cloud computing Enabling technologies Different types of cloud: IaaS, PaaS and SaaS Cloud terminology Interacting with a cloud: management consolesMore information
The Construction of Seismic and Geological Studies' Cloud Platform Using Desktop Cloud Visualization Technology
Send Orders for Reprints to firstname.lastname@example.com 1582 The Open Cybernetics & Systemics Journal, 2015, 9, 1582-1586 Open Access The Construction of Seismic and Geological Studies' Cloud Platform UsingMore information
Cloud Infrastructure Pattern
1 st LACCEI International Symposium on Software Architecture and Patterns (LACCEI-ISAP-MiniPLoP 2012), July 23-27, 2012, Panama City, Panama. Cloud Infrastructure Pattern Keiko Hashizume Florida AtlanticMore information
Course Descriptions. preparation.
Course Descriptions CS 101 Intro to Computer Science An introduction to computer science concepts and the role of computers in society. Topics include the history of computing, computer hardware, operatingMore information
Mike Boyarski Jaspersoft Product Marketing firstname.lastname@example.com. Business Intelligence in the Cloud
Mike Boyarski Jaspersoft Product Marketing email@example.com Business Intelligence in the Cloud Agenda Introductions Cloud BI Jaspersoft Open Source Powers the Cloud Jaspersoft Cloud BI FuturesMore information
2) Xen Hypervisor 3) UEC
5. Implementation Implementation of the trust model requires first preparing a test bed. It is a cloud computing environment that is required as the first step towards the implementation. Various toolsMore information
Mobile Cloud Computing Security Considerations
보안공학연구논문지 (Journal of Security Engineering), 제 9권 제 2호 2012년 4월 Mobile Cloud Computing Security Considerations Soeung-Kon(Victor) Ko 1), Jung-Hoon Lee 2), Sung Woo Kim 3) Abstract Building applicationsMore information
Overview of Cloud Computing (ENCS 691K Chapter 1)
Overview of Cloud Computing (ENCS 691K Chapter 1) Roch Glitho, PhD Associate Professor and Canada Research Chair My URL - http://users.encs.concordia.ca/~glitho/ Overview of Cloud Computing Towards a definitionMore information
HIGH-SPEED BRIDGE TO CLOUD STORAGE Addressing throughput bottlenecks with Signiant s SkyDrop 2 The heart of the Internet is a pulsing movement of data circulating among billions of devices worldwide betweenMore information
APPLICATION OF CLOUD COMPUTING IN ACADEMIC INSTITUTION 1 PRIYANKA DUKLE, 2 TRISHALA PAWAR, 3 SNEH BHAT 1,2,3 Computer, Amrutvahini College of Engineering, Sangamner Email: email@example.com 1, email@example.com information
Optimizing Environments for Desktop Virtualization
Optimizing Environments for Desktop Virtualization This paper is designed for enterprise organizations evaluating or deploying application and desktop virtualization technologies Contents page ExecutiveMore information
Experiments in Collaborative Cloud-based Distance Learning
Experiments in Collaborative Cloud-based Distance Learning Aleksandar Karadimce, Danco Davcev Faculty of Computer Science and Engineering, University Ss Cyril and Methodius Skopje, R. Macedonia firstname.lastname@example.com,More information
Security issues for Cloud Computing
274 Security issues for Cloud Computing Vikas Goyal [1], Dr. Chander Kant [2] [1] Research Scholar, [2] Assistant Professor Deptt. of Comp. Sc. & Appl., Kurukshetra University, Kurukshetra, India. firstname.lastname@example.com,More information
KeyTerm- Cloud Computing, Information Technology, Organize and Optimize, Applications, Virtualization, Infrastructure
Volume 3, Issue 6, June 2013 ISSN: 2277 128X International Journal of Advanced Research in Computer Science and Software Engineering Research Paper Available online at: www.ijarcsse.com A Study on OrganizeMore information
Dell Active System, Enabling service-centric IT, the path to the Cloud. Pavlos Kitsanelis Enterprise Solutions Lead Greece, Cyprus, Malta
Dell Active System, Enabling service-centric IT, the path to the Cloud Pavlos Kitsanelis Enterprise Solutions Lead Greece, Cyprus, Malta Delivery Deployment Cloud models & approaches Private Operated andMore information
Virtualization with VMware ESX and VirtualCenter SMB to Enterprise
Virtualization with VMware ESX and VirtualCenter SMB to Enterprise Course VM-03 5 Days Instructor-led, Hands-on Course Description This is a 5-day intense introduction to virtualization using VMware sMore information
Computing in a virtual world Cloud Computing
Computing in a virtual world Cloud Computing Just what is cloud computing anyway? Skeptics might say it is nothing but industry hyperbole, visionaries might say it is the future of IT. In realty, bothMore information
Service Component Architecture for Building Cloud Services
Service Component Architecture for Building Cloud Services by Dr. Muthu Ramachandran, Principal Lecturer in the Computing and Creative Technologies School Abstract: The emergence of cloud computing hasMore information
STRATEGIES FOR SERVICE DELIVERY IN THE CLOUD. Andy Brauer Chief Technology Officer Business Connexion
STRATEGIES FOR SERVICE DELIVERY IN THE CLOUD Andy Brauer Chief Technology Officer Business Connexion Agenda Markets Models Trends Money Tools Mindsets Hyper Connectivity creating a situation of Hyper-CompetitionMore information
Cloud Computing Essentials Course Outline
Cloud Computing Essentials Course Outline Taught by real professionals/ teachers who guide you through every topic step by step. You can now avoid all the frustration and disappointment that comes whenMore information
Cloud Computing. Chapter 1 Introducing Cloud Computing
Cloud Computing Chapter 1 Introducing Cloud Computing Learning Objectives Understand the abstract nature of cloud computing. Describe evolutionary factors of computing that led to the cloud. Describe virtualizationMore information
Keywords Cloud computing, Cloud platforms, Eucalyptus, Amazon, OpenStack.
Volume 3, Issue 11, November 2013 ISSN: 2277 128X International Journal of Advanced Research in Computer Science and Software Engineering Research Paper Available online at: www.ijarcsse.com Cloud PlatformsMore information
Cloud Computing using
Cloud Computing using Summary of Content Introduction of Cloud Computing Cloud Computing vs. Server Virtualization Cloud Computing Components Stack Public vs. Private Clouds Open Source Software for PrivateMore information
A PLM Certificate Program Update: Teaching PLM Online Using VMs in the Cloud
A PLM Certificate Program Update: Teaching PLM Online Using VMs in the Cloud A. B. Mueller Department of Computer Graphics Technology Purdue University Abstract Purdue University Department of ComputerMore information
OpenShift. OpenShift platform features. Benefits Document. openshift. Feature Benefit OpenShift. Enterprise
openshift Benefits Document platform features Feature Benefit FOR APPLICATIO DEVELOPMET Self-Service and On-Demand Application Stacks By enabling Developers with the ability to quickly and easily deployMore information
Implementing SAAS: Cloud Computing and Android Based Application Framework for.net Programming
Available Online at www.ijcsmc.com International Journal of Computer Science and Mobile Computing A Monthly Journal of Computer Science and Information Technology IJCSMC, Vol. 4, Issue. 4, April 2015,More information
In a dynamic economic environment, your company s survival
Chapter 1 Cloud Computing Defined In This Chapter Examining the reasons for cloud Understanding cloud types Defining the elements of cloud computing Comparing private and public clouds In a dynamic economicMore information


text:
|Publication number
|Publication type
|Application number
|Publication date
|May 27, 1997
|Filing date
|Sep 2, 1993
|Priority date
|Sep 2, 1993
|Fee status
|Publication number
|Shih-Gong Li
|Original Assignee
|International Business Machines Corporation
|Export Citation
|BiBTeX, EndNote, RefMan
|Patent Citations (14), Non-Patent Citations (8), Referenced by (52), Classifications (7), Legal Events (6)
|External Links: USPTO, USPTO Assignment, Espacenet
This invention relates generally to data messaging retrieval and storage in a data processing system. More particularly, it relates to determining a set of data structures from which data may be distributed between nodes in a parallel database system.
Databases have become the subject of significant recent interest, not only because of the increasing volume of data being stored and retrieved by computerized databases but also by virtue of the data relationships which can be established during the storage and retrieval processes.
In the last decade, database system developers have turned their attention toward parallel processing platforms, because a parallel processing system's cost/performance ratio is often superior to that of conventional mainframes. Set-oriented database systems, like relational database systems, are particularly well-suited to parallel processing since the database can be spread across the multiple computers or "nodes" in the system, and requests against the database can thus be executed in parallel. A generic parallel database system is characterized by a cluster of powerful, inexpensive microprocessor-based computers, each of which includes one of more disk storage devices with high performance and capacity. The nodes are interconnected using a shared communication medium. The cluster uses standard "off the shelf" microprocessor and workstation hardware products to take advantage of the high performance, lower cost, and higher reliability found in commodity components. When the database size or workload grows near the capacity of the system, more nodes can be added to extend that capacity.
In such a system, the database is distributed across the nodes; each node stores a fraction of the database. Likewise, the workload is distributed across the nodes: requests are sent to the nodes that contain the desired data and are executed there. Consequently, data placement determines how well the workload is balanced across the nodes, and how well the system performs as a whole. In many cases, the best performance can be obtained by spreading the workload as evenly as possible across all of the nodes. However, in an initially balanced system, the type and frequency of requests will change over time, data will be added to and deleted from the database over time, causing the workload to shift over time. Eventually, the system will become imbalanced across the nodes. Thus, the data will occasionally have to be redistributed to rebalance the load. Also, as nodes are added or deleted from the system, the data will have to be redistributed across the new number of nodes.
In a Parallel Database (PDB) System, data records are partitioned into data structures hereinafter referred to as "buckets". All the data records belonging to a bucket should always be placed into a single node. When adding new nodes into the PDB system, "buckets" of data must be moved from the existing nodes to the new nodes. A logical link is established with a predefined number of communication buffers for sending data records from the old residing node to the new node. As most relational database systems do not support a physical bucket in their storage organization, a table scan is required to select the to-be-moved records into communication buffers for redistribution. Because the table scan operation requires a table lock, it is logical to lock the same table on every PDB node to obtain the exclusive right on this particular table for data integrity and data placement consistency. Thus, every node will execute based on the same table sequence for data redistribution. However, the table locking makes performance one of the primary concerns for the operation of adding a new node. The faster the locks can be released, the less impact to the other ongoing transactions in PDB system.
The redistribution of data within a parallel database is traditionally done in a quiescent mode or a dynamic mode. In a quiescent mode, all functions other than data redistribution are halted until the data redistribution is complete. In an on-line or dynamic mode, data redistribution takes place concurrently with other PDB tasks.
There are two modes which have been proposed for data redistribution in a parallel database system. In a quiescent mode, the PDB system halts all other operations until the entire data redistribution takes place. In an on-line or dynamic mode, data redistribution takes place concurrently with other PDB tasks. In a commonly assigned, copending application, Ser. No. 08/116,089 entitled "Selecting Buckets for Redistributing Data Between Nodes in a Parallel Database in the Incremental Mode" S. G. Li, pending, which is hereby incorporated by reference, a new incremental mode is introduced. In the incremental mode, a set of quiescent data redistribution slices of the time alternate within time dedicated to other PDB tasks.
A quiescent mode PDB operation blocks any other operations using PDB during its operation for adding or removing nodes in PDB system. When adding new nodes, it is necessary to redistribute data for both load balancing and data availability. Instead of reloading the entire database, another approach to redistributing data is moving a portion of data from the existing nodes to the new nodes. In PDB, the unit of data movement is the bucket. As load balancing is the primary goal of data redistribution, it should be considered when choosing the appropriate buckets for moving. Because there are so many buckets in a node, using the traditional mathematical programming method for choosing the buckets cannot guarantee a feasible solution efficiently.
This invention describes a technique that can efficiently select the buckets to move to the new nodes with load balancing considered.
These and other objects are accomplished by a method, system and program for determining which data stored in the nodes in a parallel database to redistribute to a receiving node. First, an average workload for the nodes within the parallel database is calculated based on the total workload divided by the number of existing and new nodes. Next, a set of transmitting nodes is established each of which has a workload which exceeds the average workload. A set of data structures which when redistributed from the transmitting nodes to the receiving node will lower the transmitting nodes workload to equal slightly greater than the average workload is selected. Finally, the set of data structures is redistributed from the transmitting nodes to the receiving node.
The process of selecting the data structures to be redistributed includes ordering a set of candidate data structures according to a redistribution value of the respective data structure. The workload associated with a first candidate data structured having a highest redistribution value is subtract from the transmitting node workload. The first candidate data structure is selected for the set of data structures to be redistributed, if a result of the substracting step is greater than or equal to the average workload. The receiving node is either a new node to the parallel database system or an underutilized node in the parallel database system.
These features, advantages, objects and others will be more readily understood with reference to the attached drawings and following description.
FIG. 1 depicts a single computer system including system unit, display, keyboard and mouse.
FIG. 2 is a block diagram of the components of the computer depicted in FIG. 1.
FIG. 3 depicts a parallel database system.
FIG. 4 depicts a parallel database system in which a new node is added.
FIG. 5 depicts a table within a parallel database system.
FIG. 6 depicts a flow diagram for redistributing data to a new node in a parallel database system.
FIG. 6A is a flow diagram for determining from which table data may be redistributed.
FIG. 7 depicts the process to calculate the minimum sufficient buffer space for data read distribution.
FIG. 8 is a flow diagram for a table scan operation.
FIG. 9 is a flow diagram for determining an imbalanced condition in a parallel database system.
The invention may be run on a collection of computers including variety of computers made by different vendors under a number of different operating systems. Computers in the system could be, for example, a personal computer, a mini computer or mainframe computer. The computer network could be Local Area Network or a Wide Area Network or larger teleprocessing system. Although the specific choice of computer is limited only by disk and disk storage requirements, computers in the IBM PS/2 (TM) series of computers could be used in the present invention. For additional information on IBM's PS/2 series of computers, the reader is referred to Technical Reference Manual Personal System/2Model 50, 60 Systems IBM Corporation, Part No. 68X2224 Order Number S68X-2224 and Technical Reference Manual Personal Systems/2 (model 80) IBM Corporation Part No. 68X2256 Order Number S68X-2254. One operating system which an IBM PS/2 personal computer may run is IBM's OS/2 2.0 (TM) for more information on the IBM OS/2 2.0 operating system, the reader is referred to OS/2 2.0 Technical Library, Programming Guide Vol. 1, 2, 3 Version 2.00 Order Nos. 10G6261, 10G6495, 10G6494.
In the alternative, computer systems in the IBM RISC System/6000 (TM) line of computers which run on the AIX (TM) (TM) operating system could comprise the network. The various models of the RISC System/6000 are described in many publications of the IBM Corporation, for example, RISC System/6000, 7073 and 7016 POWERstation and POWERserver Hardware Technical Reference, Order No. SA23-2644-00. The AIX operating system is described in General Concepts and Procedure--AIX Version 3 for RISC System/6000 Order No. SC23-2202-00 as well as other publications of the IBM Corporation.
In FIG. 1, a computer 10, comprising a system unit 11, a keyboard 12, a mouse 13 and a display 14 are depicted. The screen 16 of display device 14 is used to present visual feedback to the user on parallel database operations. A graphical user interface supported by the operating system allows the user to use a point and shoot method of input by moving a mouse pointer 15 to an icon representing a selection at a particular location on the screen 16 and press one of the mouse buttons to perform a user command or selection.
FIG. 2 shows a block diagram of the components of the personal computer shown in FIG. 1. The system unit 11 includes a system bus or plurality of system buses 21 to which various components are coupled and by which communication between the various components is accomplished. The microprocessor 22 is connected to the system bus 21 and is supported by read only memory (ROM) 23 and random access memory (RAM) 24 also connected to system bus 21. A microprocessor in the IBM multimedia Ps/2 series of computers is one of the Intel family of microprocessors including the 386 or 486 microprocessors. However, other microprocessors including, but not limited to, Motorola's family of microprocessors such as the 68000, 68020 or the 68030 microprocessors and various Reduced Instruction Set Computer (RISC) microprocessors manufactured by IBM, Hewlett Packard, Sun, Intel, Motorola and others may be used in the specific computer.
The ROM 23 contains among other code the Basic Input-Output system (BIOS) which controls basic hardware operations such as the interaction and the disk drives and the keyboard. The RAM 24 is the main memory into which the operating system and application programs are loaded. The memory management chip 25 is connected to the system bus 21 and controls direct memory access operations including, passing data between the RAM 24 and hard disk drive 26 and floppy disk drive 27. A CD ROM drive 32 also coupled to the system bus 21 is used to store a large amount of data.
Also connected to this system bus 21 are various I/O controllers: The keyboard controller 28, the mouse controller 29, the video controller 30, and the audio controller 31. As might be expected, the keyboard controller 28 provides the hardware interface for the keyboard 12, the mouse controller 29 provides the hardware interface for mouse 13, the video controller 30 is the hardware interface for the display 14, and the audio controller 31 is the hardware interface for the speakers 15a and 15b. Also coupled to the system bus 21 is digital signal processor 33 which corrects the sound produced by the speaker system and is preferably incorporated into the audio controller 31. The speakers 15a and 15b may be used to present audio objects to the user. An I/O controller 40 such as a Token Ring Adapter enables communication over a network 46 to other similarly configured data processing systems.
One of the preferred implementations of the present invention is as a set of instructions in a code module resident in the random access memory of the least one of the computers in the network. Until required by the computer system, the set of instructions may be stored in a another computer memory, for example, the hard disk in hard drive 26, or a removable computer memory such as an optical disk in the CD ROM 32 or a floppy disk in the floppy disk drive 27. As shown in the figure, the operating system 50 and presentation manager 52 are resident in RAM 24. In this example, the invention is embodied in a database manager 54 which cooperates with the operating system. The database manager 54 manages a database 56 which forms a portion of the parallel database system.
A parallel database system comprised of a plurality of computer systems coupled to a network is depicted in FIG. 3. Four computer systems or nodes 61, 63, 65, 67 are coupled together by means of a network 69. As discussed previously, the computer systems are typically personal computers or workstations. The network can be a Local Area Network, such as a Token Ring or Ethernet Network which conform to ISO and IEEE standards or a Wide Area Network including telecommunication links. Both the Token Ring or Ethernet Network conform IEEE and International Standard Organization standards for local area networks. The ISO family of standards are described in standard publications ISO 8802-2 (Logical Link Control Protocol), ISO 8802-3 (CSMA/CD Bus), ISO 8802-4 (Token passing bus), ISO 8802-5 (Token Passing Ring), and ISO 8802-7 (Slotted Ring).
The parallel database system stores a plurality of tables 62, 64, 66 and 68 at each of the nodes. Notice that a parallel database normally has redundant storage of the tables for fault tolerance purposes in case one node is unavailable. For example, if node 61 which holds tables 62 and 64 goes down, table 62 is available at node 65 and table 64 is available at node 67. As mentioned previously, a relational database is particularly useful within a PDB system. One of the better known languages for relational databases is the standard Structure Query Language (SQL). A search is defined in a query which defines the tables in which the data will be found. The table columns of interest, the conditions rows must satisfy, the order of columns, distinctiveness constraints, connections of data within tables and other relationships one also specified in the query. Within a PDB system, if the search query can not be satisfied using locally stored data, a remote call is made for other portions of the database stored at other nodes. For more information on relational databases, and in particular SQL and its uses, the reader is referred to IBM Operating Systems/2 Extended Edition Database Managers Structured Query Language (SQL) Concepts Booklet published by the IBM Corporation in 1991.
The situation when a new node is added to the parallel database system is depicted in FIG. 4. New node 71, which will include tables 72 and 74 will be added to the network depicted in FIG. 3. Logical links 73 will be connected to each of the existing nodes 61, 63, 65, 67 to facilitate transfer of portions of the existing tables 62, 64, 66, 68 to the new node 71. A logical link is established by the transport layers which are located at each of the nodes and include communication buffers which store the portions of the tables existing nodes prior to transmission over the logical links to new node 71. The network connectivity to provide the logical link for communication is provided by network communications software 75. A variety of protocols can be supported for example, NetBIOS, SNA and TCP/IP. Information on the NetBIOS protocol can be found in IBM Operating Systems/2 Local Area Network Server Version 2.0 Information and Planning Guide. (G236-0162); IBM Local Area Network Server Programs (Specification sheet) (G360-2753); and IBM Local Area Network Technical Reference. (SC30-3383). The network communications software is in charge of setting up a session, confirming that messages are sent and received over the LAN, bundling data from the database to conform with network protocols, etc.
The communications software also places data which cannot be immediately transmitted over the LAN into a communications buffer. A communication buffer can store header information which stores communication control information, the actual data containing the data records to be transmitted and a end section indicating the end of the data records.
A sample relational database table 100 is shown in FIG. 5. A table generally includes a plurality of records in rows with a corresponding set of fields in a set of columns. For example, record 1 in row 101 and record 2 in row 103 include data in a plurality of fields each of which is written to a particular column, for example, account number information is written to column 102 account name information is written to column 104, last transaction date information is written to column 106, comment information is written in column 108 and balance information is written in column 110. While only two records with only five fields are depicted in the figure, in practice, the relational database table can become exceedingly large comprising several pages of storage. As the relational databases grow too large to accommodate on existing nodes, a new node can be added to the parallel database system and portions of the tables on existing nodes can be redistributed.
The data redistribution process for adding a new node is depicted in FIG. 6. The process starts in step 150 and proceeds to step 152 where the new node is physically attached and registered to the parallel database network. In the registration process the node ID, network address, etc. are sent to a registration server node in the PDB system. Next, in step 154, the buckets of data to be moved to the new node are determined for each existing node. This step is depicted in greater detail in FIG. 6A.
Next, in step 156, the necessary buffer size for the logical link is calculated for each existing node. Calculating the buffer size is depicted in greater detail in FIG. 7.
Steps 157, 159, 162, 169 and 170 are performed in each of the existing nodes; Steps 161, 163, 165 and 167 are performed in the new node being added to the parallel database system. In step 157, the table scan process is initiated. The table scan process is described in greater detail with reference to FIG. 8 below. Next, in step 159 the communication buffer which has been filled by the table scan process is sent to the new node. The new node receives and stores the redistributed data in step 161. In step 162, the last communication buffer is sent from the existing node to the new node Each communication buffer can contain a flag indicating the buffer is the last one or not. In step 163, the end of data reception from a given existing node is marked. A table in the receiving node stores resides in the receiving node. In step 165, a test is performed to determine whether all existing nodes are marked for end of data redistribution. If true, each existing node is notified that the reception of information is complete, step 167. In step 169, the completion message is received from the new node and in step 170 the marked data records which have been transferred to the new node are deleted from the tables in the existing nodes. The process ends, step 172.
In the quiescent-mode operation, load balancing is the primary goal. When adding new nodes to the PDB system, the ideal arrangement is shifting work load from the existing nodes to the new nodes to distribute the work load evenly. This is achieved by moving buckets from the existing nodes to the new nodes according to their work loads. As the PDB applications usually maintain a similar working pattern from time to time, it is reasonable to derive the ideal potential work load distribution based on the current work load information. Therefore, instead of simply selecting the hottest bucket from an existing node for data redistribution, it is more appropriate to select the buckets that can best reduce the node's load toward the ideal load.
When reaching load balancing in the PDB system, each node should have the same amount of work load. Thus, the ideal work load is the new average work load determined by: Wideal=(W/(N+K)), where W is the total work load of the entire PDB before adding the new nodes, N is the number of existing nodes, and K is the number of new nodes to be added. Using this ideal work load as a target, each node can locate the to-be-moved buckets by a process described below. The status monitor can provide the work load and size of each bucket.
The Average-Target workload process is a method that works on a node at a time. As shown in FIG. 6A, the total PDB system workload is determined in step 201. Next, in step 203 the ideal workload is calculated for the PDB system where the total workload is averaged over the existing node and any new nodes which are to be added to the PDB. If the test in step 205 is satisfied, the remainder of the steps in the flow diagram builds an ordered list for each node that describes the selected buckets for moving to the new nodes, List-Move. For each existing node, a list of candidates, Candidate-- List, initially contains the numbers of all the buckets residing in this node, step 207. Then, from the Candidate-- List, the process finds the bucket that has the highest value of workload. In one preferred embodiment, workload value is calculated according to the equation W=W1, *(Work-load)-W2 *(Table-- size)-W3 *(Bucket size)-W4 *(#records) where Work-- load is the potential work load of this bucket and #-- records is the number of data records in the bucket, step 209. W1, W2, W3, and W4 are appropriate weighing factors, determined by the usage and nature of the PDB system. Assuming that the usage of PDB is following a certain pattern, the history data of workload for each table on each node can be used as an important reference to determine the potential workload for each table on each node in the future usage. The W1 weighting factor is used to reflect the possible mapping from history data to possible future workload. W1 can be determined based statistical analysis on historical workload information. If a PDB system is used for running the same type of applications most of the time and the data are distributed into buckets randomly, it is very possible that the W1 factor's value would fall in the small range close to value 1.
The cost of moving a bucket in a specific table is usually determined by three factors: (1) The table size: Since the memory allocated for processing table scan is fixed, there will be more swappings between disk and memory for larger table. (2) The bucket size: Larger bucket means more data to be moved and higher communication cost. (3) The number of records: Table scan process needs to fetch and examine every record in the table. The larger number of records, the longer the process.
The values of the weighing factors, W2, W3, and W4, is dependent on and can be determined by the configuration and performance of the PDB system and the communication software. The relative values among W2, W3, and W4 can be determined with information comparing the speed of swapping, communication, and scanning processes. After W2, W3, and W4 are determined relatively, W1 value can be adjusted again to allow appropriate wrights between the workload and the cost of sending a bucket.
Next, in step 211, the highest value bucket is removed from the candidate list of buckets. In step 213, a test is performed to determine if the remaining work load is still above the ideal work load after moving this bucket according to the equation: Wrest Work-- load≧Wideal Wrest -Work-- load≧Wideal.
Wrest is the work load on this node before moving out this bucket, Work-- load is the workload of the highest value bucket.
If the comparison result is true, selecting this bucket is allowed. Then, in step 215, an entry (Table-- Name, Bucket-- Number) is placed into the List-- Move for this node.
The workload for this node is updated by subtracting the workload from the highest value bucket from the remaining work load in step 217. A test is performed in step 219 to determine if the Candidate-- List is empty. If not, the process returns to step 209, to determine the next highest value bucket. The process repeats until the test in step 219 determines that the candidate list is empty. The process then determines whether there are any additional nodes possible redistribution in step 221. If so, the next node is chosen in step 223. The process repeats until there are no more nodes to be tested and ends in step 225.
As mentioned above, the invention preferably uses queuing theory to calculate the amount of buffer space which should be set aside for the table scan operation. This is discussed in far greater detail in copending, commonly assigned, patent application, Ser. No. 08/116,087 to W. T. Chen et al. entitled "Minimal Sufficient Buffer Space for Data Redistribution in a Parallel Database System", pending.
The process begins in step 251, the number of pages to fill up the communication buffer is calculated for each node. Next, in step 253, the average time to fill a communication buffer in each node is determined for node. Next, in step 255, the arrival rate of data redistribution messages from node i to the new node, is calculated for each node.
Next, the aggregate arrival rate of data read distributions and that of the regular transaction messages are calculated in step 257.
Next, the aggregate message arrival rate at the new node is calculated in step 259. In step 261 the mean service time at the new node is determined. Next, in step 263, the mean queuing time of data redistribution messages is calculated.
Finally, in step 265 the minimal sufficient number of buffers is calculated for each node by the equation by dividing the mean queuing time by the average time to fill a buffer in the node.
The table scan process is depicted in FIG. 8. The process begins in step 300 when the table and bucket numbers within the table are given to the table scan process. With this information, the process opens and locks the table corresponding to the table number in step 301. In step 303, the process is set to check the first record in the table. In step 305, record i is checked and the bucket number k to which record i belongs is retrieved. In step 307, a test is performed to determine whether bucket number k is in the set of buckets to be redistributed. If it is, record i is copied into the communication buffer and record i is marked to be deleted once the redistribution process is complete, step 309. In step 311, the record number is incremented by one. In step 313, a test is performed to determine whether the table can process is reached the end of the table. If not, the process resumes checking records to determine whether they belong to the set of buckets to be redistributed. If the end of the table is reached, the process ends in step 315.
While the description above has concentrated on redistributing data when a new node is added into the parallel database system, the invention may also be used when the PDB system becomes imbalanced. The process for determining whether the PDB is unbalanced is depicted in FIG. 9. The process begins in step 350 and continues immediately to step 351, where the data load information is collected from each node in the PDB system. Next, in step 353, the nodes which have a data load over a predetermined threshold level are determined and classified as "overloaded". Next, in step 355, the nodes which have a load lower than a predetermined minimal standard are determined and classified as "underloaded". In step 357, an ordered list of bucket to be moved in the overloaded nodes is established. Preferably, the list is arranged in the order of bucket size and includes information such as bucket number, bucket size and node id.
The node in the underloaded category which has the least planned load is determined, and assigned the name Node-min. Planned load is determined according to the equation, planned load equals the current load plus the loads from buckets planned to be moved into the node, step 359. Next, in step 361, the top bucket in the ordered list of buckets to be moved and, X, the tentative planned load of Node-min are retrieved. L(B(top)) is determined, which is a potential load caused by the data in bucket B(Top). Next, a test is made in step 363, to determine whether the sum of X plus L(B(Top)) is greater than the predetermined threshold level. If not, in step 365, the top bucket is allocated to the Node-min and B (top) is removed from the ordered list of buckets. Additionally, X, the tentative planned load of Node-min is updated. The process continues in this loop until the tentative planned load exceeds the threshold level. Once this is true, the top bucket is allocated to Node-min and removed from ordered list and Node-min's planned load is updated in step 367. Next, in test 369, the process determines whether the list of buckets to be moved is empty. If not, i.e., there are more buckets to be moved, the process returns to find the new Node-min which has the least planned load and repeats the process as described above. If there are no more buckets to be moved, the process ends, step 371.
While the invention has been described with respect to particular embodiments above, it will be understood by those skilled in the art that modifications may be made without departing from the spirit and scope of the present invention. These embodiments are for purposes of example and illustration only and are not to be taken to limit the scope of the invention narrower than the scope of the appended claims.
|Cited Patent
|Filing date
|Publication date
|Feb 11, 1982
|Aug 14, 1984
|At&T Bell Telephone Laboratories, Incorporated
|Message routing in a computer network
|Jan 11, 1983
|Feb 17, 1987
|Iowa State University Research Foundation, Inc.
|Apparatus, methods, and systems for computer information transfer
|Nov 14, 1988
|Aug 21, 1990
|International Business Machines Corp.
|Updating pattern-matching networks
|Nov 7, 1989
|Nov 27, 1990
|Harris Corporation
|Distributed split flow routing mechanism for multi-node packet switching communication network
|Jun 30, 1989
|Nov 27, 1990
|At&T Bell Laboratories
|Load balancing and overload control in a distributed processing telecommunications system
|Dec 20, 1988
|Mar 12, 1991
|Itt Corporation
|Network connectivity control by artificial intelligence
|Dec 19, 1988
|Sep 10, 1991
|Tektronix, Inc.
|File management system
|May 11, 1990
|Oct 1, 1991
|Nippon Telegraph And Telephone Corporation
|Multiprocessor system and a method of load balancing thereof
|Nov 21, 1990
|Jul 14, 1992
|Storage Technology Corporation
|Computer system having apparatus for automatically redistributing data records stored therein
|Jun 14, 1990
|Mar 2, 1993
|Bell Communications Research, Inc.
|Packet parallel interconnection network
|Apr 30, 1990
|Feb 1, 1994
|International Business Machines Corporation
|Semi-dynamic load balancer for periodically reassigning new transactions of a transaction type from an overload processor to an under-utilized processor based on the predicted load thereof
|Apr 11, 1991
|May 31, 1994
|Galileo International Partnership
|Method and apparatus for managing and facilitating communications in a distributed hetergeneous network
|Sep 4, 1991
|Jun 28, 1994
|Storage Technology Corporation
|Intelligent storage manager for data storage apparatus having simulation capability
|Nov 8, 1994
|Jun 6, 1995
|Teleserve Transaction Technology As
|Continuously available database server having multiple groups of nodes, each group maintaining a database copy with fragments stored on multiple nodes
|Barlos, F. et al., "On the Development of a Site selection optimizer for distributed and parallel database systems." Proc. Of Conf. On Information and Knowledge Management (CIKM-93), 1-5 Nov. 1993, pp. 684-693.
|Barlos, F. et al., On the Development of a Site selection optimizer for distributed and parallel database systems. Proc. Of Conf. On Information and Knowledge Management (CIKM 93), 1 5 Nov. 1993, pp. 684 693.
|Ehrenreich, D., "Rightsizing: redistributing the electronic workload". Telecommunications, vol. 26, No. 6 (Jun./1992), pp. 59-62.
|Ehrenreich, D., Rightsizing: redistributing the electronic workload . Telecommunications, vol. 26, No. 6 (Jun./1992), pp. 59 62.
|IBM Technical Disclosure Bulletin vol. 32, No. 4A, Sep. 1989. Generalization of Min Cut Partitioning To Three Structures, pp. 315 322.
|IBM Technical Disclosure Bulletin vol. 32, No. 4A, Sep. 1989. Generalization of Min-Cut Partitioning To Three Structures, pp. 315-322.
|Rahm, E. et al., "Analysis of Dynamic load balancing strategies for parallel shared nothing database systems." Proc. Of 19th Internat'l Conf. On Very Large Data Bases, 24-27 Aug. 1993, pp. 182-193.
|Rahm, E. et al., Analysis of Dynamic load balancing strategies for parallel shared nothing database systems. Proc. Of 19th Internat l Conf. On Very Large Data Bases, 24 27 Aug. 1993, pp. 182 193.
|Citing Patent
|Filing date
|Publication date
|Jun 10, 1996
|Mar 17, 1998
|Hitachi, Ltd.
|Access analysis method and system in parallel data base system
|Jun 30, 1997
|Jun 27, 2000
|International Business Machines Corporation
|Shared nothing parallel execution of procedural constructs in SQL
|Oct 13, 1998
|Sep 12, 2000
|Hewlett-Packard Company
|Methods and apparatus for implementing quality-of-service guarantees in data storage systems
|Dec 19, 1997
|Jul 24, 2001
|Cyberiq Systems
|Cross-platform server clustering using a network flow switch
|Oct 31, 2000
|Jun 10, 2003
|Hitachi, Ltd.
|Database management methods and equipment, and database management program storage media
|Sep 15, 2000
|Jun 24, 2003
|Hitachi, Ltd.
|Parallel data base record distribution method and parallel data base management system
|Dec 19, 1997
|Jul 29, 2003
|Avaya Technology Corp.
|Dynamic load balancer for multiple network servers
|Apr 29, 2003
|Jun 1, 2004
|Hitachi, Ltd.
|Parallel database record distribution method and parallel database management system
|Aug 15, 2000
|Aug 3, 2004
|Avaya Technology Corp.
|VPN device clustering using a network flow switch and a different mac address for each VPN device in the cluster
|Oct 12, 2001
|Aug 17, 2004
|International Business Machines Corporation
|Method for distributing a set of objects in computer application
|Mar 31, 2000
|Aug 17, 2004
|Avaya Technology Corp.
|System and method for routing message traffic using a cluster of routers sharing a single logical IP address distinct from unique IP addresses of the routers
|Jul 25, 2001
|Sep 7, 2004
|Atitania Ltd.
|Method and apparatus for load balancing a distributed processing system
|Mar 31, 2000
|Apr 12, 2005
|Avaya Technology Corp.
|Firewall clustering for multiple network servers
|Apr 24, 2001
|Sep 27, 2005
|Atitania Ltd.
|System and method for transmission of information between locations on a computer network with the use of unique packets
|Oct 5, 2001
|Dec 13, 2005
|Atttania Ltd.
|Method and apparatus for monitoring and logging the operation of a distributed processing system
|Jun 12, 2001
|Apr 25, 2006
|Atitania Ltd.
|Method and apparatus for generating unique ID packets in a distributed processing system
|Apr 1, 2000
|May 30, 2006
|Avaya Technology Corp.
|Firewall pooling in a network flowswitch
|Jun 30, 2000
|Jun 27, 2006
|Cisco Technology, Inc.
|Methods and apparatus slow-starting a web cache system
|Jun 22, 2001
|Aug 29, 2006
|Atitania, Ltd.
|Method and apparatus for converting data between two dissimilar systems
|Apr 3, 2003
|Oct 16, 2007
|Hitachi, Ltd.
|Database management methods and equipment and database management program storage media
|Aug 3, 2000
|Nov 18, 2008
|Microsoft Corporation
|Scalable virtual partitioning of resources
|Dec 3, 2004
|Jan 27, 2009
|Microsoft Corporation
|Scalable virtual partitioning of resources
|Apr 25, 2006
|Jan 12, 2010
|Atitania Ltd.
|Method and apparatus for generating unique ID packets in a distributed processing system
|Aug 2, 2007
|Sep 21, 2010
|International Business Machines Corporation
|Redistributing a distributed database
|Oct 9, 2008
|Nov 2, 2010
|Microsoft Corporation
|Scalable virtual partitioning of resources
|Jul 23, 2005
|Mar 8, 2011
|Ac Technologies S.A.
|Data access management system as well as a method for data access and data management for a computer system
|Jul 27, 2007
|Dec 20, 2011
|International Business Machines Corporation
|Redistributing messages in a clustered messaging environment
|Jul 15, 2005
|Dec 4, 2012
|Symantec Operating Corporation
|Automated optimal workload balancing during failover in share-nothing database systems
|Feb 17, 2011
|Feb 18, 2014
|Ac Technologies, S.A.
|Data access and management system as well as a method for data access and data management for a computer system
|Jan 18, 2012
|Sep 9, 2014
|Symantec Operating Corporation
|Automated optimal workload balancing during failover in share-nothing database systems
|Dec 28, 2010
|Jan 13, 2015
|Microsoft Corporation
|Fast and low-RAM-footprint indexing for data deduplication
|Dec 28, 2010
|Jun 9, 2015
|Microsoft Technology Licensing, Llc
|Fast and low-RAM-footprint indexing for data deduplication
|Dec 11, 2010
|Dec 8, 2015
|Microsoft Technology Licensing, Llc
|Addition of plan-generation models and expertise by crowd contributors
|Jun 17, 2013
|Mar 29, 2016
|Microsoft Technology Licensing, Llc
|Flash memory cache including for use with persistent key-value store
|Jun 17, 2013
|Sep 6, 2016
|Microsoft Technology Licensing, Llc
|Flash memory cache including for use with persistent key-value store
|Apr 24, 2001
|Jan 23, 2003
|Atitania Ltd.
|System and method for transmission of information between locations on a computer network with the use of unique packets
|Oct 5, 2001
|Apr 17, 2003
|Peterson Diane L.
|Method and apparatus for monitoring and logging the operation of a distributed processing system
|Oct 12, 2001
|May 15, 2003
|International Business Corporation
|Method for distributing a set of objects in computer application
|Jun 22, 2001
|Jan 6, 2005
|Atitania Ltd.
|Method and apparatus for converting data between two dissimilar systems
|Apr 3, 2003
|Mar 3, 2005
|Nobuo Kawamura
|Database management methods and equipment and database management program storage media
|Dec 3, 2004
|May 19, 2005
|Microsoft Corporation
|Scaleable virtual partitioning of resources
|Jul 23, 2005
|Dec 8, 2005
|Peter Gailer
|Data access and management system as well as a method for data access and data management for a computer system
|Dec 13, 2005
|Jan 25, 2007
|Peterson Diane L
|Method and apparatus for monitoring and logging the operation of a distributed processing system
|Apr 25, 2006
|Feb 15, 2007
|Atitana. Ltd.
|Method and apparatus for generating unique id packets in a distributed processing system
|Jan 12, 2006
|Jul 12, 2007
|International Business Machines Corporation
|Method and system for performing a redistribute transparently in a multi-node system
|Jul 23, 2005
|Oct 18, 2007
|Peter Gailer
|Data access and management system as well as a method for data access and data management for a computer system
|Jul 27, 2007
|Feb 7, 2008
|Graham Derek Wallis
|Redistributing Messages in a Clustered Messaging Environment
|May 14, 2008
|Nov 20, 2008
|Hinshaw Foster D
|Allocation and redistribution of data among storage devices
|Aug 19, 2008
|Dec 11, 2008
|International Business Machines Corporation
|System for performing a redistribute transparently in a multi-node system
|Aug 2, 2007
|Feb 5, 2009
|Kristin Marie Hazlewood
|Redistributing a distributed database
|Oct 9, 2008
|Feb 19, 2009
|Microsoft Corporation
|Scalable virtual partitioning of resources
|Dec 4, 1998
|Mar 23, 2000
|Holontech Corp
|Dynamic load balancer for multiple network servers
|U.S. Classification
|International Classification
|Cooperative Classification
|European Classification
|Sep 2, 1993
Owner name: INTERNATIONAL BUSINESS MACHINES CORPORATION, NEW Y
Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNOR:LI, SHIH-GONG;REEL/FRAME:006701/0270
Effective date: 19930902
|Sep 18, 2000
|Fee payment
Year of fee payment: 4
|Sep 22, 2004
|Fee payment
Year of fee payment: 8
|Dec 1, 2008
|Maintenance fee reminder mailed
|May 27, 2009
|Lapse for failure to pay maintenance fees
|Jul 14, 2009
|Expired due to failure to pay maintenance fee
Effective date: 20090527

subset: commoncrawl


text:
Recent Members
Military Alumni
Honoring Our Heroes
This area is dedicated to our alumni that have served or are serving in our armed forces!
Lost Class Rings
Have you lost your Northwood High School class ring? Have you found someone's class ring? Visit our Falcons lost class ring page to search for your class ring or post information about a found ring.
Honored Military Alumni
Alan Murray Class of 2003
Army, 7 Years Special Forces
Benjamin Deaton Class of 2008
National Guard, 2 Years Currently on med hold from our deployment. I'm with the 256 Infantry Brigade Combat Team.
CHARLES W HORTON JR Class of 1978
Navy, 9 Years I served in the US Marine Corps 1977. I served in the US Navy from 1979-1988. My Home of Record is in Nashville Tennessee. I was at the duty stations-NRTCC San Diego Ca/NAS Millington Tennessee/NAS Whidbey Island Wa/NRTCC Servscolcom RM A SCHOOL/USS Mount Vernon LSD-39/NCS SUBBASE Bangor Washington State/USS MISSOURI BB-63
Douglas Loyd Class of 1983
Navy, 12 Years I served in the USMC from 1985 - 1987, transferred into the Navy in 1987 and was medically discharged in 1997. I am a disabled Vet. I was an 0341 81mm mortarman in the USMC Reserves in S'port, La. I was a Damage Controlman (firefighter) in the Navy. I was on the USS Yellowstone (AD-41), I was a Navy Recruiter in S'port, La. from '91 - '94. I went to CBR-D specialist school in '94, then reported aboard the aircraft carrier USS John F. Kennedy (CV-67). I was medically discharged from the Kennedy in Mayport, Florida. I was a DC1 (E-6), and up for DCC (E-7) when I was discharged. I was a shipboard fire fighter, fire marshal, CBR specialist, and a Navy Gas Free Engineer among many other jobs on a fire fighting team on a Navy ship. I also served for 7 1/2 months in Desert Shield/Desert Storm. Thanks to everyone of you for your service!! God Bless You for your sacrifice!!
gabriel lewis poland Class of 2011
Army, 1 Years Gabriel is currently serving active duty and deployed in the middle east.
Julio E. Perez III Class of 2005
Air Force, 3 Years Air Battle Manager Aboard The E-8C JSTARS Jet. Stationed At Robins, AFB Georgia.
Michael Roberts Class of 2007
Army, 3 Years 12 mo. deployment to Iraq Job description Radar Technician
Richard Ballard Class of 1991
Army, 17 Years Enlisted in 1992 as an Infantryman, Stationed in Germany, Washington, Hawai'i, Tenesse, George. Kansas, Nebraska. Deployed to Afganhistan, Iraq twice.
Robert D. Rigg Class of 1969
Marine Corps, 20+ Years 1974-1996 HELOCOPTER /AIRCRAFT MECHANIC AND CREW CHIEF ON UH-1Ns. Jet engine test operator.
Trent Daugherty Class of 2006
Navy, 4 Years Navy Master At Arms (Police)
Zachary Keaton Class of 2004
Air Force, 5 Years Serving at Offutt AFB, NE since 2007.
Classmates Spotlight
Douglas Peeples
Alumni Stories
Read and submit stories about our classmates from Northwood High School, post achievements and news about our alumni, and post photos of our fellow Falcons.
School News
Do you have any news to share? Keep our classmates informed on news and current events! Please post all reunion information in the reunion area.


text:
Don't polish the fish, change the water: The Search for Leadership
Triarchy Theory proposes a model of devolved leadership which returns the focus to teams and work groups, to the flow of the work and to employees as a whole. The emphasis is more on the 'water' and less on the individual 'fish' that swim in it. This, of course, is systems thinking in action.
This approach has been refined and developed and taken in different directions by several of our authors - with a number of important implications:
If individual leaders matter less than the larger system, then leader development programmes start to make less sense. Leadership - as a body of qualities and competencies embedded in the organisation - start to matter more.
This approach is most clearly set out by Bill Tate in his Search for Leadership and the accompanying Toolkit.
The assumed qualities of a leader ('macho' would be the shorthand) are slowly being replaced by more collaborative, consultative skills and qualities. This can take several foms:
Vince Barabba looks at the decision-making skills and processes required in a 21st-century organisation in The Decision Loom.
Graham Davies and Geoff Garrett drew on their own experience and interviewed dozens of other senior executives in writing Herding Cats and the new Herding Professional Cats - guidance on how to lead and manage 'unmanageable' people in academia, research and the professions.
Jennifer Sertl and Koby Huberman look at the variety of ways that leaders can embrace ambiguity, uncertainty and change in Strategy, Leadership and the Soul.
For Dancing at the Edge, Graham Leicester and Maureen O'Hara shadowed emerging leaders in a variety of sectors before updating Carl Rogers's view of what is required of a 'Person of Tomorrow' or a 'Leader of Tomorrow'.
For free reading - Thoughts, Articles and Ideas from the Idioticon - see below.
Featured titles:
A special issue of the AMED Journal looks at paradoxes relating to:
The issue costs £10 but you can get it - and other recent editions of the Journal - for £5 as a Triarchy reader by clicking here.


text:
Don't Miss
Latest News
Must Read
Rush Limbaugh has been hospitalised in Hawaii after reportedly experiencing chest pains.
According to ET Online, local media outlets have claimed that the radio host was taken from his hotel on the island to hospital by paramedics.
A statement on his official website said: "Rush was admitted to a Honolulu hospital today and is resting comfortably after suffering chest pains.
"Rush appreciates your prayers and well wishes."
Advertisement - Continue Reading Below
The 58-year-old's radio show is broadcast on over 600 stations across the US.


text:
Reason for swollen hand? I'm three weeks post-op
Doctor Answers 4
Recovery from breast surgery
I advise you see your surgeon for review. Possible reasons are: residual swelling from problems with a fluid line (intravenous cannula) placed at the time of surgery, positional causes at the time of surgery on the operating table (pressure on hand), impairment of the lymphatic drainage of that arm secondary to the breast surgery itself.
All the very best,
Jonathan Adamthwaite
FRCS (Plast) Board Certified Plastic Surgeon
Swollen hand after surgery
could arise from a difficult IV or extravasation into the hand... but most are back to normal after 3 weeks. Elevate as much as you can and discuss with your surgeon if it isn't getting better as he/she may have better ideas as they know just what was done at your procedure.
Reason for swollen hand? I'm three weeks post-op
Hello dear, thanks for your question and provided information as well.. I don't have an exact answer from this, because your breast surgery doesn't have to be with your arms, it is very weird. Besides that you can get swollen, and maybe your body reacts that way to it, go to your surgeon and check what's happening, I don't think it can be something to worry about.
You might also like...
Swollen hand?
I'm not sure that we are going to figure out why your hand is swollen 3 weeks post-op on an online forum. However, it is normal for your breasts to be swollen if you're only 3 weeks out from a breast augmentation. Some possible causes are a predisposition to have water retention, too much salt in your diet, or you've performed some extra activity that's leading to swelling in the hand. I suggest you call your plastic surgeon and discuss your concerns about the hand swelling for an appropriate diagnosis. Best, Dr. ALDO
These answers are for educational purposes and should not be relied upon as a substitute for medical advice you may receive from your physician. If you have a medical emergency, please call 911. These answers do not constitute or initiate a patient/doctor relationship.


text:
Celebrate Halloween with Ozio
Join Ozio and over 20 other bars as we bite into the 13th Annual Nightmare on M Street in Washington DC. This will be the largest roaming costume party in the city! Mark your calendars for Saturday, October 27 from 5pm-1am.
It is sure going to be a crazy event and it's just keeps on getting bigger every year! Get your tickets now here.
Click here for pics from last year.
Reader Comments (0)
Post Comment
You must login to post comments.
Customer Login


text: Joseph Paneno portrayed Max in the season 5 episode "A Witch In Time". External LinksEdit Joseph Paneno at the Internet Movie Database Retrieved from "http://charmed.wikia.com/wiki/Joseph_Paneno?oldid=385678" Ad blocker interference detected! Wikia is a free-to-use site that makes money from advertising. We have a modified experience for viewers using ad blockers Wikia is not accessible if you've made further modifications. Remove the custom ad blocker rule(s) and the page will load as expected. Categories: Performers Out of Universe Stubs Add category Cancel Save


text:
Housekeeper in Ajax
Hi, I am a very honest, dependable, trust worthy, hard working individual. I am self employed. Great customer satisfaction is very important to me. I take pride in my services. I have been cleaning for many years. I can provide references. It doesn't matter what I list here about myself. I believe it's important to contact each other, have a conversation and get a good feel about one another. Many times I work alone, so I understand how important it is for a homeowner to trust and allow a stranger into their home. It is important to me to have a good communication with my clients to meet their cleaning requirements.
Housekeeper Details
|Email Address
|Phone Number
Bathroom Cleaning
Kitchen Cleaning
General Room Cleaning
Change Bed Linens
Oven Cleaning
Cabinet Cleaning
Surface Polishing
Wall Washing
Basement Clean-Up
Take Out Trash/Recycling
Notes made here will only be seen by you. If you want to message the owner of this listing
More in Ajax, Ontario
Looking for Housekeeper jobs in Ajax, ON?
Find available jobs in your city.


text:
About the Book
Features & Details
Small Square, 7×7 in, 18×18 cm
62 Pages
- Publish Date Jun 22, 2008
See More
About the Creator
John V Palozzi
Lake Worth FL USA
John Palozzi lives and works in Lake Worth Florida. He was the President of Poets of the Palm Beaches for 17 years, and is still on their Board of Directors. He has been teaching Creative Poetry Writing classes for over 10 years and continues to do so. John has always loved taking pictures and doing collage. He is also a member of Artists of Palm Beach County, and sits on their Board of Directors. Contact him at: email@example.com
Log in or create an account to post a comment.
0 Comments
Delete this comment?
See More


text:
Dark-haired stud Tom Byron and his blond slender pal Sven Laarson are World Cup supporters and getting into the spirit of things at Sweet and Raw in their favourite team's colors. They're having a kick about in the park wearing jockstraps and socks.
They make out and strip down even further. These maneuvers are rarely seen on the football field, but they're really impressive. Tattooed Tom gives his twink buddy a good servicing and gets his ass fucked first, before switching over and getting his big manly dick inside that cute tight hole bareback.