CROSS-REFERENCE TO RELATED APPLICATION 
   The present application is related to the following co-pending application: 
   (1) U.S. patent application Ser. No. 10/756,138. 
   BACKGROUND OF THE INVENTION 
   1. Technical Field 
   The present invention relates in general to improved performance in distributed systems and in particular to a method for minimizing complex decision making when allocating additional resources to a job submitted to a first selection of resources in a grid environment. Still more particularly, the present invention relates to storing previous decisions to allocate additional resources in a grid environment according to characteristics of the jobs for which decisions were made, such that the stored decisions can be reused for subsequent jobs with similar characteristics to minimize complex decisions when allocating additional resources in a grid environment. 
   2. Description of the Related Art 
   Ever since the first connection was made between two computer systems, new ways of transferring data, resources, and other information between two computer systems via a connection continue to develop. In a typical network architecture, when two computer systems are exchanging data via a connection, one of the computer systems is considered a client sending requests and the other is considered a server processing the requests and returning results. In an effort to increase the speed at which requests are handled, server systems continue to expand in size and speed. Further, in an effort to handle peak periods when multiple requests are arriving every second, server systems are often joined together as a group and requests are distributed among the grouped servers. Multiple methods of grouping servers have developed such as clustering, multi-system shared data (sysplex) environments, and enterprise systems. With a cluster of servers, one server is typically designated to manage distribution of incoming requests and outgoing responses. The other servers typically operate in parallel to handle the distributed requests from clients. Thus, one of multiple servers in a cluster may service a client request without the client detecting that a cluster of servers is processing the request. 
   Typically, servers or groups of servers operate on a particular network platform, such as Unix or some variation of Unix, and provide a hosting environment for running applications. Each network platform may provide functions ranging from database integration, clustering services, and security to workload management and problem determination. Each network platform typically offers different implementations, semantic behaviors, and application programming interfaces (APIs). 
   Merely grouping servers together to expand processing power, however, is a limited method of improving efficiency of response times in a network. Thus, increasingly, within a company network, rather than just grouping servers, servers and groups of server systems are organized as distributed resources. There is an increased effort to collaborate, share data, share cycles, and improve other modes of interaction among servers within a company network and outside the company network. Further, there is an increased effort to outsource nonessential elements from one company network to that of a service provider network. Moreover, there is a movement to coordinate resource sharing between resources that are not subject to the same management system, but still address issues of security, policy, payment, and membership. For example, resources on an individual&#39;s desktop are not typically subject to the same management system as resources of a company server cluster. Even different administrative groups within a company network may implement distinct management systems. 
   The problems with decentralizing the resources available from servers and other computing systems operating on different network platforms, located in different regions, with different security protocols and each controlled by a different management system, has led to the development of Grid technologies using open standards for operating a grid environment. Grid environments support the sharing and coordinated use of diverse resources in dynamic, distributed, virtual organizations. A virtual organization is created within a grid environment when a selection of resources from geographically distributed systems operated by different organizations with differing policies and management systems is organized to handle a job request. 
   In addition to decentralizing resources available in a grid environment to improve efficiency of network transactions, capacity on demand resources are gaining more presence. An on demand resource is one that is accessible to a system, but is operational only when a fee is paid or an electronic key to open the resource is provided. 
   An important attribute of a grid environment that distinguishes a grid environment from merely that of another management system is quality of service maintained across multiple diverse sets of resources. A grid environment does more than just provide resources; a grid environment provides resources with a particular level of service including response time, throughput, availability, security, and the co-allocation of multiple resource types to meet complex user demands. A limitation of current grid technology, however, is that maintenance of agreed to quality of service from grid resources requires human intervention. For example, human intervention is relied on in a grid environment to decide when to allocate and deallocate resources to reach specified performance levels. Further, manual intervention is relied on in a grid environment to suspend low priority jobs or move jobs to other selections of resources within the grid environment. Manual intervention is limiting on the efficiency and expansion of grid environments because it is by nature inefficient and prone to error. 
   As a result of developing grid environments and on demand resources, a single system may have access to multiple discrete sets of resources. For example, first, a system typically accesses those components within the system that provide a primary set of local resources. Next, a system may access resources from other systems within a local or enterprise network. Further, a system may access and activate capacity on demand resources either from within the system or from a system accessible via a network. Finally, a system may access grid resources accessible through participation in a grid environment. 
   With the availability of multiple sets of discrete resources, an additional limitation of current grid technology is that human intervention is required to manage the flow between each of these discrete sets of resources to determine whether use of grid resources is required. Further, human intervention is required to determine whether to activate capacity on demand resources first or go directly to grid resources. Thus, a disadvantage of current grid technology is that when a job request is executing within a set of resources that become unable to handle performance requirements for the job, human intervention is required to decide whether to schedule the job into a grid environment or other set of resources. Given the quality of service requirements within a grid environment, it would first be advantageous to make decisions about the flow of a job through discrete sets of resources without requiring human intervention. 
   A common feature in network computing is that the same type of job may be requested from the same client system or multiple client systems within a short period of time. If the first time the job is received a complex decision has to be made to manage the flow of the job between discrete sets of resources, it would be advantageous to reuse that complex decision for other similar jobs. Therefore, in view of the foregoing, it would be advantageous to provide a method, system, and program for improving the efficiency of the use of a hierarchy of resources in a grid environment by storing complex decisions about the flow of a job such that the complex decisions may be reused for future jobs of the same type. 
   SUMMARY OF THE INVENTION 
   In view of the foregoing, the present invention in general provides for improved performance in distributed systems and in particular provides a method for minimizing complex decision making when allocating additional resources to a job submitted to a first selection of resources in a grid environment. Still more particularly, the present invention relates to storing previous decisions to allocate additional resources in a grid environment according to characteristics of the jobs for which decisions were made, such that the stored decisions can be reused for subsequent jobs with similar characteristics to minimize complex decisions when allocating additional resources in a grid environment. 
   According to one aspect of the present invention, in a grid environment, each set of discrete resources may be further controlled by a grid manager or router. The grid manager or router includes a resource monitor that monitors the performance of a first selection of resources to which a first job is submitted. 
   A decision controller within the grid manager compares the performance of the first selection of resources in handling the job with performance specifications for the first job. If the first selection of resources is adequately handling the job, then no additional resources are needed and a record of the decision not to add additional resources is stored with characteristics of the first job. If the selection of resources is not adequately handling the job, then additional resources are needed. However, the job must qualify for additional resources. If the job qualifies for additional resources, then decision controller determines what additional resources should be allocated and queries the hierarchy of resources for availability. 
   According to another aspect of the present invention, the performance specification may include quality of service specifications that are compared against the performance to determine whether quality of service guarantees are being met. Quality of service specifications may be designated by service level objectives and agreements for a particular client, group of clients, or type of service plan. 
   According to yet another aspect of the present invention, the decision to allocate additional resources and the specific resources from the hierarchy of resources selected for allocation is stored in a decision cache or other memory medium locally accessible to the decision controller. Each decision is stored according to at least one characteristic of the first job. 
   The decision controller may also compare characteristics of a next job submitted to the grid environment with the characteristics of jobs stored in the decision cache. If there is a match, then the decision associated with the matching characteristics is retrieved from the decision cache and applied when allocating additional resources for the next job. 
   All objects, features, and advantages of the present invention will become apparent in the following detailed written description. 


   
     BRIEF DESCRIPTION OF THE DRAWINGS 
     The novel features believed characteristic of the invention are set forth in the appended claims. The invention itself however, as well as a preferred mode of use, further objects and advantages thereof, will best be understood by reference to the following detailed description of an illustrative embodiment when read in conjunction with the accompanying drawings, wherein: 
       FIG. 1  depicts one embodiment of a computer system which may be implemented in a grid environment and in which the present invention may be implemented; 
       FIG. 2  is block diagram illustrating one embodiment of the general types of components within a grid environment; 
       FIG. 3  is a block diagram depicting one example of an architecture that may be implemented in a grid environment; 
       FIG. 4  is a block diagram depicting a system operating within the grid environment from the perspective of the grid management system is depicted in accordance with the method, system, and program of the present invention; 
       FIG. 5  is an illustrative representation depicting one embodiment of the logical infrastructure of a grid environment in which the present invention may be implemented; 
       FIG. 6  is a block diagram depicting a grid manager or router (GM) including a decision cache that may implement the present invention; 
       FIGS. 7A-7B  depict a high level logic flowchart of a process and program for enhancing grid job submission performance via hierarchical decision caching; 
       FIG. 8  is a block diagram depicting a threshold specification process in accordance with the method, system, and program of the present invention; 
       FIG. 9  depicts an example of a table used to decide use of resources based on job priority; and 
       FIG. 10  depicts an example of a decision logic cache table in accordance with the method, system, and program of the present invention. 
   


   DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT 
   Referring now to the drawings and in particular to  FIG. 1 , there is depicted one embodiment of a computer system which may be implemented in a grid environment and in which the present invention may be implemented. As will be further described, the grid environment includes multiple computer systems managed to provide resources. Additionally, as will be further described, the present invention may be executed in a variety of computer systems, including a variety of computing systems, mobile systems, and electronic devices operating under a number of different operating systems managed within a grid environment. 
   In one embodiment, computer system  100  includes a bus  122  or other device for communicating information within computer system  100 , and at least one processing device such as processor  112 , coupled to bus  122  for processing information. Bus  122  preferably includes low-latency and higher latency paths that are connected by bridges and adapters and controlled within computer system  100  by multiple bus controllers. When implemented as a server system, computer system  100  typically includes multiple processors designed to improve network servicing power. 
   Processor  112  may be a general-purpose processor such as IBM&#39;s PowerPC™ processor that, during normal operation, processes data under the control of operating system and application software accessible from a dynamic storage device such as random access memory (RAM)  114  and a static storage device such as Read Only Memory (ROM)  116 . The operating system may provide a graphical user interface (GUI) to the user. In a preferred embodiment, application software contains machine executable instructions that when executed on processor  112  carry out the operations depicted in the flowcharts of  FIGS. 7A-7B , and others described herein. Alternatively, the steps of the present invention might be performed by specific hardware components that contain hardwired logic for performing the steps, or by any combination of programmed computer components and custom hardware components. 
   The present invention may be provided as a computer program product, included on a machine-readable medium having stored thereon the machine executable instructions used to program computer system  100  to perform a process according to the present invention. The term “machine-readable medium” as used herein includes any medium that participates in providing instructions to processor  112  or other components of computer system  100  for execution. Such a medium may take many forms including, but not limited to, non-volatile media, volatile media, and transmission media. Common forms of non-volatile media include, for example, a floppy disk, a flexible disk, a hard disk, magnetic tape or any other magnetic medium, a compact disc ROM (CD-ROM) or any other optical medium, punch cards or any other physical medium with patterns of holes, a programmable ROM (PROM), an erasable PROM (EPROM), electrically EPROM (EEPROM), a flash memory, any other memory chip or cartridge, or any other medium from which computer system  100  can read and which is suitable for storing instructions. In the present embodiment, an example of a non-volatile medium is mass storage device  118  which as depicted is an internal component of computer system  100 , but will be understood to also be provided by an external device. Volatile media include dynamic memory such as RAM  114 . Transmission media include coaxial cables, copper wire or fiber optics, including the wires that comprise bus  122 . Transmission media can also take the form of acoustic or light waves, such as those generated during radio frequency or infrared data communications. 
   Moreover, the present invention may be downloaded as a computer program product, wherein the program instructions may be transferred from a remote virtual resource, such as a virtual resource  160 , to requesting computer system  100  by way of data signals embodied in a carrier wave or other propagation medium via a network link  134  (e.g. a modem or network connection) to a communications interface  132  coupled to bus  122 . Virtual resource  160  may include a virtual representation of the resources accessible from a single system or systems, wherein multiple systems may each be considered discrete sets of resources operating on independent platforms, but coordinated as a virtual resource by a grid manager. Communications interface  132  provides a two-way data communications coupling to network link  134  that may be connected, for example, to a local area network (LAN), wide area network (WAN), or an Internet Service Provider (ISP) that provide access to network  102 . In particular, network link  134  may provide wired and/or wireless network communications to one or more networks, such as network  102 , through which use of virtual resources, such as virtual resource  160 , is accessible as provided by a grid management system  150 . Grid management system  150  may be part of multiple types of networks, including a peer-to-peer network, or may be part of a single computer system, such as computer system  100 . 
   As one example, network  102  may refer to the worldwide collection of networks and gateways that use a particular protocol, such as Transmission Control Protocol (TCP) and Internet Protocol (IP), to communicate with one another. Network  102  uses electrical, electromagnetic, or optical signals that carry digital data streams. The signals through the various networks and the signals on network link  134  and through communication interface  132 , which carry the digital data to and from computer system  100 , are exemplary forms of carrier waves transporting the information. It will be understood that alternate types of networks, combinations of networks, and infrastructures of networks may be implemented. 
   When implemented as a server system, computer system  100  typically includes multiple communication interfaces accessible via multiple peripheral component interconnect (PCI) bus bridges connected to an input/output controller. In this manner, computer system  100  allows connections to multiple network computers. 
   Additionally, although not depicted, multiple peripheral components and internal/external devices may be added to computer system  100 , connected to multiple controllers, adapters, and expansion slots coupled to one of the multiple levels of bus  122 . For example, a display device, audio device, keyboard, or cursor control device may be added as a peripheral component. 
   Those of ordinary skill in the art will appreciate that the hardware depicted in  FIG. 1  may vary. Furthermore, those of ordinary skill in the art will appreciate that the depicted example is not meant to imply architectural limitations with respect to the present invention. 
   With reference now to  FIG. 2 , a block diagram illustrates one embodiment of the general types of components within a grid environment. In the present example, the components of a grid environment  240  include a client system  200  interfacing with a grid management system  150  which interfaces with server clusters  222 , servers  224 , workstations and desktops  226 , data storage systems  228 , and networks  230 . For purposes of illustration, the network locations and types of networks connecting the components within grid environment  240  are not depicted. It will be understood, however, that the components within grid environment  240  may reside atop a network infrastructure architecture that may be implemented with multiple types of networks overlapping one another. Network infrastructure may range from multiple large enterprise systems to a peer-to-peer system to a single computer system. Further, it will be understood that the components within grid environment  240  are merely representations of the types of components within a grid environment. A grid environment may simply be encompassed in a single computer system or may encompass multiple enterprises of systems. 
   The central goal of a grid environment, such as grid environment  240  is organization and delivery of resources from multiple discrete systems viewed as virtual resource  160 . Client system  200 , server clusters  222 , servers  224 , workstations and desktops  226 , data storage systems  228 , networks  230  and the systems creating grid management system  150  may be heterogeneous and regionally distributed with independent management systems, but enabled to exchange information, resources, and services through a grid infrastructure enabled by grid management system  150 . Further, server clusters  222 , servers  224 , workstations and desktops  226 , data storage systems  228 , and networks  230  may be geographically distributed across countries and continents or locally accessible to one another. 
   In the example, client system  200  interfaces with grid management system  150 . Client system  200  may represent any computing system sending requests to grid management system  150 . In particular, client system  200  may send job requests and jobs to grid management system  150 . Further, while in the present embodiment client system  200  is depicted as accessing grid environment  240  with a request, in alternate embodiments client system  200  may also operate within grid environment  240 . 
   While the systems within virtual resource  160  are depicted in parallel, in reality, the systems may be part of a hierarchy of systems where some systems within virtual resource  160  may be local to client system  200 , while other systems require access to external networks. Additionally, it is important to note, that systems depicted within virtual resources  160  may be physically encompassed within client system  200 . 
   One function of grid management system  150  is to manage job requests and jobs from client system  200  and control distribution of each job to a selection of computing systems of virtual resource  160  for use of particular resources at the available computing systems within virtual resource  160 . From the perspective of client system  200 , however, virtual resource  160  handles the request and returns the result without differentiating between which computing system in virtual resource  160  actually performed the request. 
   To implement grid environment  240 , grid management system  150  facilitates grid services. Grid services may be designed according to multiple architectures, including, but not limited to, the Open Grid Services Architecture (OGSA). In particular, grid management system  150  refers to the management environment which creates a grid by linking computing systems into a heterogeneous network environment characterized by sharing of resources through grid services. 
   Grid environment  240 , as managed by grid management system  150 , may provide a single type of service or multiple types of services. For example, computational grids, scavenging grids, and data grids are example categorizations of the types of services provided in a grid environment. Computational grids may manage computing resources of high-performance servers. Scavenging grids may scavenge for CPU resources and data storage resources across desktop computer systems. Data grids may manage data storage resources accessible, for example, to multiple organizations or enterprises. It will be understood that a grid environment is not limited to a single type of grid categorization. 
   Referring now to  FIG. 3 , a block diagram illustrates one example of an architecture that may be implemented in a grid environment. As depicted, an architecture  300  includes multiple layers of functionality. As will be further described, the present invention is a process which may be implemented in one or more layers of an architecture, such as architecture  300 , which is implemented in a grid environment, such as the grid environment described in  FIG. 2 . It is important to note that architecture  300  is just one example of an architecture that may be implemented in a grid environment and in which the present invention may be implemented. Further, it is important to note that multiple architectures may be implemented within a grid environment. 
   Within architecture  300 , first, a physical and logical resources layer  330  organizes the resources of the systems in the grid. Physical resources include, but are not limited to, servers, storage media, and networks. The logical resources virtualize and aggregate the physical layer into usable resources such as operating systems, processing power, memory, I/O processing, file systems, database managers, directories, memory managers, and other resources. 
   Next, a web services layer  320  provides an interface between grid services  310  and physical and logical resources  330 . Web services layer  320  implements service interfaces including, but not limited to, Web Services Description Language (WSDL), Simple Object Access Protocol (SOAP), and eXtensible mark-up language (XML) executing atop an Internet Protocol (IP) or other network transport layer. Further, the Open Grid Services Infrastructure (OSGI) standard  322  builds on top of current web services  320  by extending web services  320  to provide capabilities for dynamic and manageable Web services required to model the resources of the grid. In particular, by implementing OGSI standard  322  with web services  320 , grid services  310  designed using OGSA are interoperable. In alternate embodiments, other infrastructures or additional infrastructures may be implemented a top web services layer  320 . 
   Grid services layer  310  includes multiple services. For example, grid services layer  310  may include grid services designed using OGSA, such that a uniform standard is implemented in creating grid services. Alternatively, grid services may be designed under multiple architectures. Grid services can be grouped into four main functions. It will be understood, however, that other functions may be performed by grid services. 
   First, a resource management service  302  manages the use of the physical and logical resources. Resources may include, but are not limited to, processing resources, memory resources, and storage resources. Management of these resources includes receiving job requests, scheduling job requests, distributing jobs, and managing the retrieval of the results for jobs. Resource management service  302  preferably monitors resource loads and distributes jobs to less busy parts of the grid to balance resource loads and absorb unexpected peaks of activity. In particular, a user may specify preferred performance levels so that resource management service  302  distributes jobs to maintain the preferred performance levels within the grid. 
   Second, information services  304  manages the information transfer and communication between computing systems within the grid. Since multiple communication protocols may be implemented, information services  304  preferably manages communications across multiple networks utilizing multiple types of communication protocols. 
   Third, a data management service  306  manages data transfer and storage within the grid. In particular, data management service  306  may move data to nodes within the grid where a job requiring the data will execute. A particular type of transfer protocol, such as Grid File Transfer Protocol (GridFTP), may be implemented. 
   Finally, a security service  308  applies a security protocol for security at the connection layers of each of the systems operating within the grid. Security service  308  may implement security protocols, such as Open Secure Socket Layers (SSL), to provide secure transmissions. Further, security service  308  may provide a single sign-on mechanism, so that once a user is authenticated, a proxy certificate is created and used when performing actions within the grid for the user. 
   Multiple services may work together to provide several key functions of a grid computing system. In a first example, computational tasks are distributed within a grid. Data management service  306  may divide up a computation task into separate grid services requests of packets of data that are then distributed by and managed by resource management service  302 . The results are collected and consolidated by data management system  306 . In a second example, the storage resources across multiple computing systems in the grid are viewed as a single virtual data storage system managed by data management service  306  and monitored by resource management service  302 . 
   An applications layer  340  includes applications that use one or more of the grid services available in grid services layer  310 . Advantageously, applications interface with the physical and logical resources  330  via grid services layer  310  and web services  320 , such that multiple heterogeneous systems can interact and interoperate. 
   With reference now to  FIG. 4 , a block diagram of a system operating within the grid environment from the perspective of the grid management system is depicted in accordance with the method, system, and program of the present invention. As illustrated in  FIG. 1 , a computer system includes many hardware components. As part of a grid environment, however, these hardware components are viewed as resources. For example, a system  400  includes an application resource  402 , two CPU resources  404  and  406 , a memory resource  408 , and a storage resource  410 . The resources in system  400  are typical of the types of resources when viewed within the grid environment, however, in an alternate embodiment, other types of resources may be provided. Further, the resources in system  400  may be physically located within a single computer system or distributed across multiple computer systems connected by a network, for example 
   As part of the grid management system described in  FIG. 2 , a grid manager and router (GM)  424  provides the interface between the resources of system  400  and client systems sending requests. 
   In particular, a resource monitor  422  within GM  424  monitors the working status of each of the resources available in system  400 . GM  424  preferably sends status reports to other grid managers and routers within the grid environment to indicate the availability of the resources in system  400 . Additionally, status reports may describe the computer hardware, operating system, and resources of system  400 . Status reports may be generated, for example, when system  400  joins or leaves the grid environment, when a threshold is detected, at predetermined time intervals, and on specific predetermined events, including, but not limited to a hardware fault or a portion of a service failing. 
   Referring now to  FIG. 5 , an illustrative representation depicts one embodiment of the logical infrastructure of a grid environment in which the present invention may be implemented. While  FIG. 2  depicts an example of general components of a grid environment, in the present figure, an example of how the general components are viewed logically within a grid environment is illustrated in a grid environment  540 . In particular, the grid management system functions are logically dispersed into multiple GMs, such as GM  504 , GM  510 , and GM  520 . Further, the virtual resource is logically dispersed into multiple resources (RSs), such as RS  506 , RS  508 , RS  512 , RS  514 , RS  522 , and RS  524 . It is important to note that a resource may not be a direct representation of a physical resource, but rather a logical representation of one or more physical resources and or groups of physical resources. 
   In the example, client system  200  sends a job request to GM  504 . GM  504  searches for resources available to handle the job specified in the job request. In particular, GM  504  checks whether RS  506  and RS  508  can handle the job specified in the job request and may send queries to other GMs, such as GM  510  or GM  520 . GMs  510  and  520  return reports on the availability of resources to handle the job request. 
   For purposes of illustrations, RS  506  and RS  508  are considered local resources or resources within the same discrete set of resources to which jobs from client system  200  are submitted. In the examples following, when RS  506  and  508  are not meeting performance requirements for a job from client system  200 , then additional resources may be allocated including other resources within the same discrete set of resources, capacity on demand resources, resources from internal grids and finally resources from external grids. 
   More specifically, in the example, GM  510 , RS  512 , and RS  514  are part of one grid infrastructure “grid A” operated by a first business that provides a first specified number of grid services for a first specified price. Then, GM  520 , RS  522 , and RS  524  are part of another grid infrastructure “grid B” operated by a second business that provides a second specified number of grid services for a second specified price. When GM  504  sends the job request to GM  510  and GM  520 , the each GM preferably reports whether the job request can be handled and a price for handling the request. In relation to client system  200 , grids A and B may be internal grids operating within the same enterprise system as client system  200  or external grids. 
   After receiving reports on the availability of resources, GM  504  collects the options for handling the job and returns the options to client system  200 . Client system  200  may then decide to select a preferred option and send the job to have handled according to the selected option. GM  504  manages the return of the results of the job to client system  200 . 
   The resources utilized in the option selected by client system  200  form a virtual organization for handling the job. For example, if client system  200  selects an option to handle the job based on the option returned from GM  510 , then a virtual organization may be formed to process the job which would include GM  504 , GM  510 , RS  512 , and RS  514 . According to an advantage of the present invention, any of the GMs may also automatically allocate additional resources to a job submitted to a first selection of resources. In particular, if the resources handling a job do not meet performance specifications, then additional resources may be allocated to the virtual organization of resources formed to process the job. These performance specifications may include quality of service specifications compiled from service level objects and agreements. 
   With reference now to  FIG. 6 , there is depicted a block diagram of a grid manager or router (GM) including a decision cache that may implement the present invention. As depicted, a GM  600  includes a client interface  602  for receiving jobs. Client interface  602  assigns a unique identifier to each job and holds jobs for processing by GM  600 . In particular, client interface  602  may require authentication of the client sending the job request before the job is assigned a job identifier and transferred to decision controller  614 . 
   Once a job request is received at GM  600 , GM  600  preferably determines which resources are available for the job, submits the job to a selection of resources, and monitors the resource availability of multiple levels of resources accessible to GM  600  to determine current usage levels. First, a resource monitor  610  preferably monitors the resource availability of multiple levels of resources accessible to GM  610  to determine which are able to handle the job request. Then, a decision controller  614  preferably determines resource options to meet performance specification  612  designated for the job request or for the requesting client. In particular, decision controller  614  may be required to make complex decisions with respect to job resource options for a particular job request to maintain performance service level agreements with the client sending the job request. For example, decision controller  614  may decide when to allocate additional resources to maintain performance service level agreements specified in performance specification  612  for the job request. Advantageously, decision controller  614  stores these decisions in decision cache  608  according to identifiers such as system state and job priority. 
   Once a decision is stored in decision cache  608 , decision controller  614  may reuse the decision. Decision cache  608  may implement a data storage format, such as a table, for storage of decisions with characteristics of the job for which the decision was made. Preferably, data is stored in decision cache  608  so that decision controller  614  can compare characteristics of new job requests with stored characteristics of previous job requests and reuse decisions made for previous job requests with the same or similar characteristics. For example, the system state and job priority are characteristics that may be stored with a decision and subsequently matched with other jobs. The data storage stored in decision cache  608  may advantageously be stored in secondary locations and may be stored in relational or other media based application so that the data is not lost in the event of a power failure. 
   Referring now to  FIGS. 7A-7   b , there is illustrated a high level logic flowchart of a process and program for enhancing grid job submission performance via hierarchical decision caching. The following process may be performed through a grid manager and router or other component of the grid management system. In particular, in the logic flowchart, complex decisions are made multiple times as a job flows through the layers of resources, and ultimately to a selection of resources able to execute the job and meet performance specifications. Advantageously, the decisions made for a job request are stored as a system state and decision code in a decision cache so that even if a second similar job is executing back-to-back with the first, the second similar job is more efficiently processed using the predetermined decisions in the decision cache. 
   As depicted, the process starts at block  700  and thereafter proceeds to block  702 . Block  702  depicts defining a job. Next, block  704  illustrates scheduling the job. In particular, a job may be scheduled according to job requirements and job priority. In the example, the job is scheduled for use of processing resources and thus the steps of the process reflect those taken for deciding whether additional processing resources are required for the job and if so, acquiring the additional processing resources. It will be understood that in alternate embodiments, a job may be scheduled for use of other types of resources including, but not limited to, memory, disk space, I/O subsystems, and networks. Thus, in alternate embodiments, decisions may be made about acquiring other types of resources. Thereafter, block  706  depicts submitting the job to a selection of resources, and the process passes to block  708 . In particular, considerations for job submission may include, but are not limited to, operating system type, kernel width, application threading, CPU requirements, memory requirements, and other factors. In one example of submitting the job into the local client environment, a determination is made whether the job is single threaded or multi-threaded. If the job is single threaded, then the job is queued at one processor. If the job is multi-threaded, then the job is queued at multiple processors. 
   Block  708  depicts executing a system performance check. The resource monitor, for example, may execute the system performance check that surveys the performance of available resources. In particular, many factors or a combination of factors may be used to determine system performance including, but not limited to, the depth of the processor(s) run queue, the amount of memory available, the amount of disk space free, and the network bandwidth available. Multiple types of performance monitoring tools capable of providing performance related information may be implemented by the resource monitor including, but not limited to, International Business Machine Corporation (IBM) Performance Toolbox or invoking an operating system command, such as the “vmstat” command in the UNIX operating system. 
   Next, block  710  illustrates a determination whether additional processing power is required to process the current job. Alternatively, a determination may be made whether other additional resources are required for the job. As will be further described in  FIG. 8 , this step requires a determination whether the current resource saturation of the resources handling the job, as monitored by the system performance check, exceeds predefined thresholds for resource saturation for the job or the client system. In the example, if system performance is acceptable, then no additional processing power is required and the process passes to block  712 . If additional processing power is required, then the process passes to block  716 . 
   First, if no additional processing power is required at block  710 , then block  712  illustrates assigning a system state and decision code to the job indicating that no additional processing power was added. In particular, when a system state and decision code are assigned to a job, then the decision, the system state, the job priority and the assigned decision code are cached in the decision cache. The system state indicates the resource saturation level of the available grid resources when the decision was made. The decision code indicates the decision made for a particular priority of job for a particular system state. Next, block  714  depicts executing the job by sending the job to a selection of available resources, and the process ends. 
   Alternatively, if additional processing power is required at block  710 , then block  716  depicts a determination whether the job is eligible for additional resources based on the priority of the job or other required qualification. In particular, the saturation levels of the resources to which the job is submitted may reach levels that indicate additional processing power is required for processing the job. Not every job, however, will qualify for the addition of processing power. For example, jobs which are non-interactive and thus do not require specific performance may not be eligible for access to additional system resources. Further, if certain jobs begin to affect the performance of higher priority jobs, the job priority of a job may be reduced using tools such as a UNIX “nice” command. In comparison, some jobs may be designated as always valuable and granted broad access to additional resources. If the job is not qualified, then the process passes to block  722 . Block  722  illustrates assigning a system state and decision code indicating that no additional processing power was added. Next, block  724  depicts suspending the job or continuing to execute the job on the original selection of resources, and the process returns to block  708 . Alternatively, if at block  716  the job is qualified, then the process passes to block  718 . 
   Block  718  depicts a determination whether the current system state matches a system state in the decision cache according to the job priority. In particular, this is a determination whether a decision was previously cached for this type of job operating in the same system performance conditions. If the system state matches, then the process passes to block  720 . Block  720  depicts processing the job based on the previously cached decision for the system state and job priority, and the process ends. In processing the job based on the previously cached decision in the state table, decisions to allocate additional resources are made more efficient. Alternatively, if at block  718  the system state does not match, then the process passes to block  730  of  FIG. 7B . 
   Block  730  illustrates a determination whether capacity on demand processing is available. If capacity on demand processing is available, then the process passes to block  746 . Block  746  depicts assigning a system state and decision code indicating that a capacity on demand processor was added. Next, block  748  depicts adding the on demand processor, and the process returns to block  708  of  FIG. 7A . Alternatively, if at block  730  capacity on demand processing is not available, then the process passes to block  732 . Block  732  depicts assigning a system state and decision code indicating that a capacity on demand processor was not added, and the process passes to block  734 . 
   Block  734  depicts a determination whether a local processor is available. If a local processor is available, then the process passes to block  746 , where a system state and decision code are assigned indicating that a local processor was added. If a local processor is not available, then the process passes to block  736 . Block  736  illustrates assigning a system state and decision code indicating that a local processor was not added. Next, block  738  depicts submitting the job to the grid for processing. In particular, the grid may be an internal or external grid of systems that provide a particular resource or service. Then, block  740  illustrates assigning a system state and decision code indicating a grid resource was added, and the process ends. In particular, although not depicted, after the job is submitted to the grid for processing, the steps of the process may be repeated locally within a grid to determine which resources are necessary within a grid unit. Further, a system state and decision code may be assigned and stored locally within a grid unit. 
   With reference now to  FIG. 8 , there is depicted a block diagram of a threshold specification process in accordance with the method, system, and program of the present invention. To implement the process of blocks  708  and  710  described in  FIG. 7 , resource saturation levels are defined in the performance specification such that when performance reaches saturation thresholds, decision making is triggered. 
   In  FIG. 7A , a system performance check is executed at block  708 . The performance check may be performed by the resource monitor surveying the current system load on the available resources, including a grid. Then, at block  710 , a determination is made whether additional processing power is required. The decision process may be performed by the decision controller. To determine whether additional processing power is required, the process jumps to block  804 . While the present example is described with reference to determining whether additional processing power is required, in alternate embodiments, the need for other types of additional resources may be determined. For example, in an alternate embodiment, in addition to determining whether additional processing power is required, the resource scheduler may also determine whether additional memory is required, additional storage space is needed, or a combination of additional resources is required. 
   To determine whether additional processing power is required, block  804  depicts comparing the real-time system loading with preset performance thresholds. The performance thresholds define performance specifications for a particular job, a particular client, or other entity for which performance specifications are designated. The present performance thresholds are performance specifications that may include quality of service specifications based on service level agreements and objections. A decision logic threshold table  806  provides an example of the types of preset thresholds that may be selected for managing the performance of a grid to maintain expected performance specifications. When the real-time system loading is compared with the thresholds in decision logic threshold table  806 , the decision subsystem of the resource schedule determines whether additional processing power is required. 
   Real-time system loading is preferably compared with the percentage thresholds in decision logic threshold table  806 . If, for example the CPU use is 60%, then the system state of “000” is returned and no additional action is available. Thus, at block  710 , a determination would be returned that no additional process power is required. If, however, for example, the CPU use is 95%, then the system state of “100” is returned and the job requires additional processing power. As an additional advantage to the present invention, once the system state is established, it is also cached in the decision cache, such that upon submission of the next job or a repeat request for a resource from the same job, a system performance check need not be executed. Rather, the cached system state can be used for the next request and a system performance check need only be performed upon submission of a job with a set of requirements different from the job previously submitted. 
   It is important to note that while the example of decision logic threshold table  806  includes several categories for comparison to determine system state, the example is merely illustrative of the types of logical elements that may be used to specify the resource saturation levels of the grid and the actions available to be taken upon reaching a resource saturation level. Further, in lieu of a table, alternate types of data management systems may be used to implement decision logic. 
   Referring now to  FIG. 9 , there is illustrated an example of a table used to decide use of resources based on job priority. Referring back to  FIG. 7A , conditional block  716  requires a determination whether a job is eligible for additional processing power. This step requires the decision controller to determine whether the job qualifies for additional resources based on the job priority or other qualifications. The decision controller may make this decision based on specifications, such as resource eligibility table  900 , which specifies eligibility for additional resource based on the job priority. In particular, the decision controller may receive the priority of a job based on input from multiple sources including, but not limited to, a resource scheduler, header data in the job submission stream, or another agent that determines job priority. 
   Resource eligibility table  900  illustrates one example of a method for specifying eligibility for resources based on the job priority. In the example, each job is rated with a priority number from 1 to 4. For each job priority rating, a particular action is allowed. For example, for a job rated with a priority of “1”, the job is eligible for unlimited resources. In  FIG. 7A , at conditional block  716 , responsive to a determination that the job is rated with a priority of “1” the job would be considered an eligible job. However, for a job rated with a priority of “4”, at conditional block  716 , the job is not considered eligible for additional resources. It will be understood that other methods for specifying the eligibility of a job for resources may also be implemented. 
   With reference now to  FIG. 10 , there is depicted a block diagram of a decision logic cache table in accordance with the method, system, and program of the present invention. As illustrated, a decision logic cache table  1000  records the action executed for the decision code assigned to a system state and job priority. Through use of table  1000 , as additional jobs flow through the decision controller, multiple decision steps are reduced as cache hits to system states and job priorities provide a predefined course. In particular, table  1000  is an example construction of a decision logical cache table which may be used to store reusable, previously made decisions. It will be understood that table  1000  is representative of one method in which decision logic may be cached. 
   In the example, where the system state is “000”,  FIG. 8  indicates that no action is to be taken. Thus, in previous decisions where the system state was “000”, no additional action was taken. Referring back to  FIG. 7 , the first four entries in table  1000  represent the decisional process of the path from conditional block  710  to action block  712  where no additional processing power is required beyond the resources already allocated for executing the job. When additional job requests are received with the “000” system state and any job priority, then table  1000  may be quickly accessed such that the decision code and previously decided action can be applied. 
   In addition, in the example, where the system state is “100”,  FIG. 8  indicates that an additional CPU is allowed. The job priority, as illustrated in  FIG. 9 , further qualifies the resource eligibility for the job. Thus, for the next job request received during a system state of “100” where the job has a priority of “2”, a decision controller may automatically decide to allow 10 additional CPU&#39;s based on the previous decision recorded in table  1000  for that system state and job priority. Referring back to  FIG. 7 , the entries for the system state of “100” and job priority of 1, 2, and 3 in table  1000  represent the decisional process of the path from conditional block  710  to conditional block  716  to conditional block  718 . The entry for the system state of “100” and the job priority of  4  in table  1000  represent the decisional process of the path from conditional block  710  to conditional block  716  to action block  722 . 
   While the invention has been particularly shown and described with reference to a preferred embodiment, it will be understood by those skilled in the art that various changes in form and detail may be made therein without departing from the spirit and scope of the invention.
A method, system, and program for minimizing complex decisions to allocate additional resources to a job submitted to a grid environment are provided. First, at least one decision to allocate additional resources to at least one previously submitted job is stored in a decision cache, wherein said the least one decision is stored according to at least one characteristic of the at least one previously submitted job. When another job is submitted to the grid environment needs additional resources, the characteristic of the currently submitted job is compared with the characteristics of previous submitted jobs. If there is a match, then the previously made decision associated with the matching characteristic controls allocation of additional resources for the currently submitted job, such that complex decision making for allocation of additional resources is minimized by reusing previously stored decisions to allocate additional resources.