text:
- Research
- Open Access
Cache performance models for quality of service compliance in storage clouds
© Sithole et al.; licensee Springer. 2013
- Received: 11 March 2012
- Accepted: 28 November 2012
- Published: 10 January 2013
With the growing popularity of cloud-based data centres as the enterprise IT platform of choice, there is a need for effective management strategies capable of maintaining performance within SLA and QoS parameters when responding to dynamic conditions such as increasing demand. Since current management approaches in the cloud infrastructure, particularly for data-intensive applications, lack the ability to systematically quantify performance trends, static approaches are largely employed in the allocations of resources when dealing with volatile demand in the infrastructure. We present analytical models for characterising cache performance trends at storage cache nodes. Practical validations of cache performance for derived theoretical trends show close approximations between modelled characterisations and measurement results for user request patterns involving private datasets and publicly available datasets. The models are extended to encompass hybrid scenarios based on concurrent requests of both private and public content. Our models have potential for guiding (a) efficient resource allocations during initial deployments of the storage cloud infrastructure and (b) timely interventions during operation in order to achieve scalable and resilient service delivery.
- Storage cloud
- Enterprise applications
- Cache performance
- Optimisation
The cloud computing paradigm is emerging as a mainstream approach in the design and implementation of enterprise computing solutions [1–3]. The principal factors favouring the adoption of cloud-based technologies in business computing environments are: (a) the ease with which IT infrastructure deployments and expansions can be achieved when bringing together multiple and heterogeneous computing resources, typically scattered across wide geophysical locations, and (b) the simplified mechanisms by which users can access and utilise hosted IT services.
Based on the specific needs of target user environments, which cloud based technologies are intended to serve, business IT service solutions can be crafted and made available in a variety of offerings, which can be Software-as-a-Service (SaaS), Platform-as-a-Service (PaaS) or Infrastructure-as-a-Service (IaaS) computing capabilities. The adoptions of the SaaS solutions [1, 2] present hosted applications to user environments customers as usable service entities for business computing needs; the PaaS-based solutions [4–7] present for use by application routines executing at the SaaS level, service capabilities that are derived from the integration of Operating System and virtualisation functionalities; the IaaS solutions [3, 8–10] bring together the operational hardware elements such as data centre equipment, processor and storage servers, and networking devices into functional capabilities that can be plugged into and utilised by user routines executing at both the SaaS and PaaS levels of the cloud stack.
Depending on the nature of the affinity groups that are served by cloud-based IT environments, there are four main categories of clouds - Private, Community, Public or Hybrid, which can serve user environments according to their access entitlements. Private clouds are exclusively for intra-organisational needs; Community clouds result from federations of resources that serve the interests of select user groups with common objectives; Public clouds offer on-demand services to anyone with service-provider-authenticated web access over the standard Internet connection; Hybrid clouds fulfill requirements that can only met through services derived from combinations of in-house and off-premises resources. The delivery of cloud computing service according to above mentioned implementations enables reduced setup times for the deployments of outsourced business computing solutions, with little or no requirement being imposed on the customers to understand and manage the underlying technologies operating in the infrastructure.
Despite the enormous strides that have been achieved in developing functional capabilities for cloud computing systems, challenges still remain that present formidable barriers to the reliable performance and therefore effective use of cloud-based IT infrastructures. Performance related issues in the cloud domain encompass a range of considerations such as how to maintain Key Performance Indicators (made up of throughput and response time metrics), on-demand resource and service availability, continuity and scalability of IT services at competitive SLA and QoS levels that will enable business customers to meet performance goals. In order for the management-driven strategies and interventions to meet required levels of service reliability and availability and, thus maintain infrastructure operations within specified SLA and QoS targets, in-depth knowledge is required for establishing quantitative trends that are associated with principal performance indicators such as throughput rates, response times and load-scalability as the levels of user requests being sent to the cloud infrastructure vary. Thus, in quantitative terms, the ability to provide SLA and QoS-capable resource management in cloud-based IT environments requires accurate characterisations of the load response patterns, based on the interplay of factors such as the resource infrastructure's service capacity, the levels of applied user workloads and their resource consumption needs in the cloud service environment.
Given the lack of intimate knowledge that can lead to mature capabilities for establishing the quantitative relationships between user requests and performance in precise detail, the allocations of resources in cloud-based IT infrastructures are largely conducted in a reactive manner, with the assignment of resource entities being carried out statically in response to any changes in user demand. As a result, the fulfilment of SLAs and QoS contracts for outsourced IT services essentially relies on either excess provisioning that leads to inefficiencies or, limited allocation of infrastructure resources that has carries the risk of SLA violations. Hence, instead of having in place proactive technical interventions that can immediately respond by reassigning resources to keep performance levels within acceptable thresholds, any QoS breaches that occur in the infrastructure are handled largely by follow up admin negotiations with a view to settling any business losses that may result from service interruptions through compensation. It is therefore worth exploring approaches by which performance trends in the cloud infrastructure can be determined with sufficient accuracy to enable proactive resource management for QoS compliant delivery of IT services. In our work, we isolate for study the resource utilisation trends of the storage subsystems on server hardware, an aspect that has not been given sufficient consideration in the context of supporting SLA-capable resource management mechanisms in storage clouds.
Proposed strategy for QoS compliance support in storage clouds
As has been highlighted, the challenge of achieving SLA-awareness in cloud environments is a multifaceted research issue with a number of dimensions stemming from it such as determining the levels of resource availability, service continuity rates and scalability trends of performance that will be able to satisfy QoS constraints. As a starting point towards addressing the vital issues pertinent to QoS maintenance in storage cloud infrastructures, this paper focuses on developing characterisations of cache performance trends, an aspect which we consider to have potential for serving as an important source of guidance for informed decisions on the provisioning of scalable data storage services in enterprise IT environments. Our approach, thus aims to support QoS readiness in resource allocation management strategies for storage clouds through accurate modelling of content availability levels at individual cache entities in the infrastructure, and the modelled scalability trends can serve as a feed into the management strategies for storage space provisioning.
In order to establish the validity of the modelled cache performance, a data centre facility with cloud-based storage elements is used. As a key contribution of this paper, we present and validate scalability trends of cache performance at individual nodes, and the derived theoretical models can be a foundation upon which the considerations for infrastructure sizing can proceed and decisions are made in accord with the applications' resource needs and the service capabilities of the resources in enterprise computing environments on the following: (a) initial sizes of the storage deployments for cloud-based services, (b) re-calibration of the scale of storage resource integrations an ongoing basis in order to preempt SLA violations that could arise from short-term increases in demand and, (c) storage capacity upgrades based on anticipated margins of permanent increases in user demand.
Given that the accuracy of cache performance characterisations is the critical component underpinning the ability of our proposed strategy to quantify the scale of resource allocations required to fulfil performance goals in storage clouds, the next section proceeds with a detailed consideration of cache performance trends, with the focus primarily on the analytical derivations of the scalability response patterns of content retention rates at individual storage server nodes in the cloud domain. In Section "Experimental facility for validations of cache performance Trends" we describe the key components of the practical facility used for conducting experimental validations of the models. Sections "Validations of cache performance trends for user requests of private data" and "Validations of cache performance trends for user requests of public data" respectively feature the sets of experiments conducted to establish the validity of the modelled cache performance for private and public data requests. Section "Performance characterisations for concurrent accesses to private and public content" extends the analytical models to scenarios that are based on concurrent requests going to private and public data. A brief evaluation of our cache performance characterisations is provided in Section "Discussion" through the consideration of the implications of the results on the ability to provide support for service continuity, scalability and SLA compliance in the management of storage resources. Section "Related research" provides a summary review of other research initiatives that are aimed at developing SLA support strategies for clouds by addressing aspects that are adjacent to our area of focus. The ninth section concludes the paper by highlighting further issues to be investigated in future work so that viable techniques are developed for QoS-ready deployments in the storage cloud infrastructure.
Derivations of cache performance models
Input and output parameters for the local cache node
N Uers
Number Active Users
10 - 250
Application Request
exp (2.5)
Inter-repetition Time (sec)
J Limit
Transaction Limit for Received Jobs
J Policy
Job Instance Limit Policy
J Priority
Priority assigned for
Regular 5
Average CPU Service Time (sec)
exp (1.5)
Average I/O File Read Count
Constant (1)
Average Read File Size (MB)
Constant (1)
S CPUMem
Average Size of Memory (MB)
Uniform (0- 10)
Capacity of Local Cache (GB)
40 - 200
Capacity of Remote Storage (GB)
Local Cache Capacity (users)
10 - 50
Remote Storage Capacity (users)
Overall Local Cache Hit Ratio
0 - 1
Cache performance analysis of user requests for private data
Based on the interplay of these factors, the overall cache performance in terms of the average cache hit ratio, P L , at each local storage can thus be summarized by the basic expression, . We make the assumption that the interplay of these input parameters impacts the local cache performance by predominantly generating capacity misses.
Next, we consider cache performance in scenarios where users generate dispersed requests i.e. unrestricted access is allowed to all the publicly available files that are kept on the remote storage device of capacity, C S .
Cache performance analysis of user requests for public data
The following section provides an introduction to the setup for the experiments that were conducted to establish the practical validity of the modelled scalability trends of the cache hit rates derived for the two cases of data access patterns considered above.
Experimental facility for validations of cache performance Trends
The Load Generator program initiates the operations in the Application Server, File Manager and Remote Storage VMs in readiness for the start of measurements and results collection. As a preparatory step, an initial start signal is sent by the generator to both the File Manager and Remote Storage VMs so that the cache optimisation algorithm and storage partitions are provisioned with target files to be requested by users are set up. After a delay of appropriate duration, a second start signal is generated to initiate first user request, Request1, which will be followed by a train of arrivals, Request2 … Request N , at the Application Server according to the predefined arrival process rate, λ App . The File Manager VM responds to the initial start signal by setting aside the required storage space in the local cache and activating the algorithm selected for optimising cached content.
In addition to retaining cached content according to the selected optimisation criterion, the cache algorithm functionality in the File Manager VM responds directly to the data requests by compiling a record of the Request, Hit and Miss events occurring in the local cache. The Remote Storage VM ensures that permanent copies of all the file objects to be requested by the users are kept on its storage partition, ready to be copied across to the local cache partition should any misses occur at the latter. Upon the lapse of the predefined duration of the experimental measurements, a stop signal is emitted by Load Generator to the Application Server, File Manager and Remote Storage VMs.
Definitions of scenarios for the experimental study
It is important to reiterate that the specific definitions for the scenarios considered in the experimental studies take into account the fact that the levels of content availability at storage nodes in the cloud are governed by the interplay of the principal factors considered in the derivations of cache hit ratios, which are the data request patterns in terms of whether they specify private or shared data, the amounts of storage space in both the cache and remote nodes, the applied user load according to volumes of data requests and file sizes associated with each request, and the efficacy of the storage management and cache replacement policies in keeping content that closely matches the needs of anticipated user requests. Hence, we define scenarios for experimental investigations to bring out the impact the respective factors to cache performance by setting the key experimental parameters as follows:
Scenario 1 considers Cache Performance with User Requests accessing Private Data
Scenario 2 considers Cache Performance with User Requests accessing Public Data
Within each of the two primary scenarios, three separate studies of cache performance are conducted based on cache optimisation policies for Random, Popularity and Age-based File Evictions.
The applied user load, based on the number of users and the average file sizes per request, is uniformly increased to levels that are beyond the assigned cache storage, C L , and the resulting scalability patterns for cache misses and hits are recorded for comparisons with the theoretical ones.
Validations of cache performance trends for user requests of private data
The arrival and service processes for the user requests received at the local cache nodes server are assumed to be Poisson, and in order to ensure a stable queue on the storage node, the relationship, , must hold for the respective magnitudes of the mean arrival and service times. Reference can be made to the list of input parameters shown in Table 1 for the actual values of arrival and service intervals. To investigate the sensitivity of the local cache ratios to user load, the local cache and remote storage capacities are assigned fixed values of L=50, and S=400 respectively. The applied user load parameter, N Users , is increased uniformly from 10 to 250 with each active user accessing his own set of data whenever requests are sent to the local cache.
Random eviction criterion
The pseudocode representing key functional features of the cache optimisation program is presented in Algorithm 1. It is important to point out that for the purpose of providing a complete summary of the experimental operations, Algorithm 1 also includes the primary functionalities of the Load Generator and Storage Manager programs. Once all start up misses have been dealt with and the cache is space is filled up, the Random Cache Eviction algorithm responds to any further misses by choosing the victim files in the local cache that are marked for deletion. The victim files are then replaced with the requested content, which is brought from the remote storage in order to satisfy the cache miss event. The durations of inter-arrival and service times of the user requests follow the exponential distribution with mean values of = 2.5 and = 1.5 seconds respectively.
Algorithm 1: Cache Eviction Simulator(N Users , L, S)
An important factor leading to better performance for the measurement results is that the analytical models are based on the worst-case situations, in which the consideration of excess requests within each average cycle of user requests by N Users does not take into account the possibility that some of the requests generated in the practical scenarios would be accessing data already in the cache. Hence because of the inability of the performance analysis to quantify exactly the extent of improvement in cache hits caused by the repeat requests that can occur within the {N Users -L} excess requests inside each average load cycle, there are lower cache hit ratios for the theoretical trends.
The use of the Random Eviction criterion in selection of the files for deletion in the cache is another factor contributing to the better cache performance achieved in the practical measurements. Since the identities of requested files is determined by the Load Generator according to the probability, , and that of the files to be deleted according to the probability, , identical strategies are thus used for the respective actions of file requests and cache optimisation. Having such alignment in the Load Generator and Caching Algorithm functional patterns therefore helps improve the cache hit ratios obtained from practical measurements. Despite the consistently lower performance levels for the modelled cache hit ratios, the comparisons in Figure 4 nonetheless show that the theoretical trends can be a reliable indicator of achievable cache performance in practice.
Least frequently used criterion
The LFU Algorithm is structurally similar to the Random Eviction criterion, the important difference as shown in the program module below, being that update and sort functions on the popularity list are performed according to the frequencies of the requests for the cached files. The lowest ranked file on the list is marked for deletion and its place in the cache is taken by the newly requested content brought into the local cache from the remote storage.
Measurements for scenarios based on the popularity of cached data produce lower cache hits than in the case of optimisation techniques that use the Random Eviction policy. As can be seen in Figure 5, the practical values of cache hit ratios are much nearer to the theoretical ones than those shown in Figure 4. The reduced level of cache performance using the popularity based criteria suggests that the LFU algorithm is less efficient than the random deletions of cached files in response to cache misses. The performance knock resulting from the LFU algorithm is likely accounted for by the fact that mechanisms, which rank cached files by virtue of the frequencies of previous requests are not employing the relevant strategy given that the identities of requested files are in fact specified by the Load Generator at random, based on uniform probability of occurrence of magnitude, . Hence, the LFU approach for ranking cached content only produces nonexistent patterns of file popularity, which in turn, results in reductions of cache hit events.
Least recently used criterion
The LRU algorithm is based on rating cached files according to age so that file objects that have been kept in the local cache the longest are assigned the lowest indices of usefulness with respect to data needs of future requests. The oldest files are thus selected for deletion whenever cache miss events occur and the victim files are replaced by the newly requested content, which is transferred from the master storage.
As in the case of the LRU, the lower cache hit ratio performance is probably an outcome of the mismatch between the cache optimisation strategy and the patterns associated with data requests coming onto the cache. The age-based approach of quantifying the likelihood of experiencing repeat requests in the future for cached files is not a useful optimisation technique given the random manner in which requested files are specified by users. Thus, any apparent difference in the ages of stored files that may be computed by the LRU provides no predictive value on the likely patterns of future file requests, which according to Algorithm 1, are stochastic and follow a discrete uniform distribution with probability, , for each request action.
Summary of validation experiments for private data requests
The practical measurements that were conducted using the three cache optimisation algorithms produced results that were closely matched to the modelled theoretical trends. In terms of the actual cache hit ratios, the use of the Random Cache Eviction criterion resulted in better cache performance over the age and popularity-based LRU and LFU policies respectively. The inefficiencies in the LRU and LFU Cache Eviction criteria are due to the non-existence of age and popularity-based behavioural patterns in the requests for cached content. Since each of the public data objects, like private content, are requested with identical probability, the next set of validations of cache performance models for the requests for public data are therefore carried out on the basis that our initial results show superiority of Random Eviction policy over LRU and LFU by considering the scenarios involving use of the Random Cache Eviction policy only.
Validations of cache performance trends for user requests of public data
In conducting the practical measurements, we simplify the cache performance models for public data requests developed in Subsection "Cache performance analysis of user requests for public data" by expressing the storage capacities of the local cache and external storage in terms of L and S, which are the respective numbers of active user requests that can fill up the storage entities. Content access patterns to the S publicly available files on the remote storage are thus governed by the discrete uniform distribution with the probability, , applying to each file request. Consequently, when the requested data objects can be specified randomly from the list of shared data objects that are kept in the remote storage, the respective sizes of the local cache and remote storage capacities impact on cache performance according to Equation 3.
The results from the three scenarios confirm that in the event of data requests predominantly going to publicly hosted content, which all active users are free to access the overall cache performance is independent of load in accord with the theoretical approximation. Another important observation from the graphs in Figure 8 is that for the modelled cache performances of 25 and 50%, the theoretical and measurement results are very similar, with the practical results marginally better than the theoretical estimates in some places for cache hit ratio of 25%. As the size of the local cache is increased to 75% of the remote storage, the practical performance also goes even though it stays within the 70% range. We attribute the lower values of cache hit ratios in the measurements at high cache capacity to a further need for calibrating the number of user request cycles, M, that governs the duration for the measurements to capture the events so that the impact data of the requests to all S files is accurately reflected by practical observations. Despite the discrepancies in the modelled and practical results shown in the graphs, particularly for higher values of hit ratios, the theoretical approximations of cache performance (in terms the respective sizes of local cache and remote storage capacities) is a reliable guide of cache performance for file requests that are spread across publicly available content.
Section "Performance characterisations for concurrent accesses to private and public content" follows the theoretical derivations of cache performance patterns with scenarios where mixed requests are generated simultaneously by users to access both public and private content.
Performance characterisations for concurrent accesses to private and public content
Parameters for concurrent requests of private and public data
Probability of Requesting Private Data
Probability of Requesting Public Data
Capacity of Cache Partition for Private Data (users)
Capacity of Cache Partition for Public Data (users)
Capacity Remote Storage Partition for Private Data (users)
Capacity Remote Storage Partition for Public Data (users)
P HitPrivate
Cache Ratio from the requests of Private Data
P HitPublic
Cache Ratio from the requests of Private Data
Overall Hit Ratio on the Local Cache
Data access patterns for content on separate caches
As shown by both Figures 10 and 11, the Private Access Ratios i.e. the preference weights associated with requests to private data, P1, are varied in uniform steps of 0.1 from 0 to 1. Conversely, the access weights for public data, P2, vary in reverse order from 1 to 0 for the featured scenarios in the graphs, given that the request events to public and private content are mutually exclusive. Hence, when Private Access Ratio is 0, the cache hit and miss trends solely emanate from requests that are directed at public content. It also follows that when Private Access Ratio is equal to 1, the cache performance complete derives from the requests for private user content. Between the two extremes as P1 is raised gradually, the cache performance patterns associated with private data requests become more dominant.
It can be deduced from the equation of the overall cache hit ratio, P L , that the assignment of separate cache partitions provides the ability to isolate and individually control the respective cache performance trends associated with private and public data requests. Thus, within the boundary fixed by P1, there is the ability in the spilt cache configuration to tune L1 and fix the cut-off point, at which cache performance begins to fall exponentially with increase in N Users for requests of private data. Within the bounds of P2, the cache capacity, L2, can similarly be adjusted with respect to S2 to determine the average cache performance associated with the requests of public data.
The trends for overall cache misses, M L , shown in Figure 11 can have serious QoS implications, should there be considerable delays associated with data retrievals from external source storage whenever requested content is not found in the local cache. If the respective data access times that are experienced in the event of cache hits are also taken in account, the tuning of cache performance can be carried out to deliver output performance that keeps average storage access within SLA thresholds. With the allocation of split caches providing the flexibility of enabling individual adjustments of cache sizes, performance can thus be managed in a way that discriminates between different sets of content according to their desired QoS ratings.
Data access patterns for content on a shared cache partition
Since S2 is greater than L, the cumulative requests for public content will inevitably fill all space (equivalent to L-P1N Users ) that is left by public data requests if the number of active users remains lower than L. Once N Users goes beyond the cache capacity, L, the storage space is shared proportionally according to the ratios P1 and P2.
As in the case of separate cache partitions, the cache hit ratios trends for the scenario, P1 = 0, correspond to data requests that are going to completely public content, while that for P1 = 1, applies solely to accesses to private content. Between these two extremes, the cache hit ratios fall more steeply compared to the corresponding scenarios considered for split caches as shown in Figure 12. The rate of performance drops as P1 is raised from 0.1 to 0.9 is due to limited space on L1, which is divided up between the two sets of cached content.
The equations for cache performance trends of private and public data requests are subject to the size of the available space, L, in the common cache. As such it is not possible to individually change the storage allocations for given sets of content without affecting other cached datasets. Hence, even though the shared cache configuration is simpler to implement and is less computationally expensive because of having all the cached datasets on a single global list for cache optimisation, the design does not permit flexible allocation of cache space that would grade various sets of content according their assigned QoS categories.
We began the discussion by highlighting the need for having capabilities for scalable solutions in storage cloud domains so that infrastructure-based responses can be achieved for maintaining performance within SLA thresholds in the event of such challenges as increases in user demand or, interruptions to the operating states of the service entities making up the cloud resource fabric. We went on to argue that for SLA-compliant services to be provided consistently over a wide range of load levels, an in-depth understanding of the performance trends associated with storage cache resource entities in the cloud infrastructure, is an important foundation on which to base QoS-ready solutions. It was further pointed out that from the performance characterisations of storage cache entities, storage resource management decisions on infrastructure sizing can be made, which are relevant to important stages of resource deployment such as initial roll-outs, short-term expansions to deal with overflow requests, and permanent upgrades.
Theoretical models were proposed for estimating performance trends occurring at individual cache entities as the levels of user demand for content increase. In order to validate the derived theoretical trends, three suites of experiments (based on Random, LFU and LRU eviction policies) were defined for studying the sensitivity of cache performance to applied loads. A noteworthy observation from the results was that whenever the data request patterns are characterised by rigid affinity to content (i.e. each user accessing only its own data) and with the requested data objects being of comparable popularity, the decay trends for the measured cache hits exhibit high fidelity to the theoretical characterisations. Additionally, the Random Cache replacement algorithm provides better results than the LFU and LRU algorithms, which although still conforming to theoretical estimates, have lower levels of cache hit ratio performance. Thus, the LFU algorithm is more effective if there are distinct categories of data popularity from the users generating the requests. Similarly, the LRU algorithm performs better for cases where the usefulness of cached content is indexed by age, and thus the algorithm is not equipped deal with the even scatter of requests over a wide range of file objects. Overall, the results for private data requests demonstrate the potential utility of the models for estimating the cache storage needs associated with computing scenarios involving enterprise application routines such as Sales and Distribution (SD), Assembly-to-Order (ATO) or Employee Self-Service Portal (EP-ESS), whereby user requests work with their own sets of customer account data [11].
Another important observation from the results was the confirmation of the validity of trends for the second set of cache performance models that is associated with data requests for wide selections of files [12], where there is loose coupling between users and public content. The cache performance levels in such cases characterised by dispersed requests are independent of the levels of input user load i.e. the local cache hit ratio can be expressed as a function of the respective ratios of the local cache and remote storage capacities, . The accuracy of performance measurements for scattered file requests is based on setting the durations of the observation time window for the experiments long enough to cover data requests to the all the S files kept in the remote storage. Additionally, the measurement results of the three cases of predefined cache performance (corresponding to P L = 0.25, 0.50 and 0.75) show that the actual values of cache hit ratios obtained from practical investigations are close to the theoretical estimates. However performance from results is slightly lower than the theoretical one when the size of the local cache is increased to 0.75 S. It therefore has to be emphasised that the potential usefulness of second set of characterisations in predicting cache performance trends in scenarios where users interact with public content (to which there is unrestricted access) is subject to durations of the observation time window.
The cache model extensions for the characterisations of concurrent requests to private and public data were developed in Sections "Validations of cache performance trends for user requests of public data" and "Performance characterisations for concurrent accesses to private and public content public content", the modelled trends derived for mixed requests apply to scenarios of shared and separate storage cache partitions holding the cached content.
Related research
A number of initiatives are being pursued towards maturing performance management capabilities in cloud computing infrastructures so that scalable, secure and reliable IT services can be delivered to computing environments, most of which run business-critical applications. Below, we briefly highlight some of the work that is underway to develop SLA-based strategies for supporting firm guarantees of performance delivery.
In [13], estimates of output performance based on the levels of applied user loads and the mean service rates at the resource entities are used and, from applying the Laplace Stieltjes Transform, the overall response times at each service node in the cloud infrastructure are calculated, with SLA mappings being derived from probability distributions of the calculated response service times. The study in [14] also features use of input parameters such as the number of service requests from the consumers and the service capacities offered by deployed resource entities in developing SLA indices and, a trust model is then obtained for performing predictive estimates of the levels of resource and service availability in the cloud infrastructure. The strategy for SLA enforcement that is presented in [15] categorises workload instances that are despatched to server entities into four basic classes of resource consumption of the processor, memory and disk entities, and from taking into account the service constraints in the cloud infrastructure, the optimisation function determines the number of VM instances of each resource consumption class, which can be hosted by the provider. The work that is presented in [16] features fault tolerant and redundancy techniques for identifying and filtering out compromised resource elements in the infrastructure in order to ensure service availability and continuity in the cloud. Apart from applying redundancy strategies on the matrix-mapped resource collections, the SLA enforcement in [16] also employs predefined performance constraints on the constituent resource entities in the cloud together with integer linear programming methods that eliminate faulty and malicious elements with the greatest likelihood of compromising service quality.
By making high-level considerations regarding the overall resource capabilities in developing strategies for SLA guarantees, the approaches described above thus treat the runtime operations of CPU execution, memory and storage data access as a single composite service functionality, which differs from our work, whose focus is exclusively on establishing internal cache performance trends pertaining to storage access.
The studies in [17–19] have a similar focus to our approach of isolating subsystems of server hardware in order to characterise their resource consumption patterns for SLA and QoS support, the difference being that the strategies presented in all the three contributions consider CPU and memory utilisations associated with processor-bound workloads. Specifically, the strategy in [17] aims to guarantee CPU QoS delivery by overcoming the common problem of runtime interference effects that usually arises when running multiple instances of applications that are derived from virtualisation technologies. The interferences between the active VM application instances are minimised through the control of the working set sizes of allocated memory pages, thereby ensuring predictability of memory fetch times, CPU utilisations and ultimately, processor QoS support. In [18], the standardised metric, EC2 Compute Unit (ECU), developed by Amazon is used for rating available computing power on various CPU hardware architectures. Based on the ECU metric, thresholds margins can be defined for identifying the resource utilisation levels, at which SLA violations are approached and the reallocation of the CPU resources can be initiated to protect SLA contracts. The framework presented in [19] features a dynamic SLA template that is designed to deal with changing user requirements by mapping consumer requirements to existing capabilities in the cloud infrastructure, with the focus also being on the allocation of processor cores as the primary resource entities of user interest.
In a related contribution on data caching mechanisms featured in [20], models have been developed for cache hit performance, with emphasis however being on the performance of multilevel cache configurations based on hierarchical and cooperative models for data sharing across distributed environments. Another endeavour on developing caching solutions for improve data availability is in the form of the Tuxedo caching framework presented in [21], which is based on the use of protocols to enhance traditional CDN and local caching strategies and thereby ensure that user requests for both personalised and public content are fulfilled incurring minimum latency. While the objectives behind Tuxedo are very similar to the motivations for our work particularly as considered in Section "Performance characterisations for concurrent accesses to private and public content", the approach taken in the former approach is different from ours in that the emphasis of Tuxedo is on an architecture-based solution as opposed to the quantitative analyses for cache performance that we consider in this paper.
Future directions
It has been highlighted that the relative inadequacies of LRU and LFU cache algorithms in the scenarios that were featured in our studies, stem from the inherent bias of the cache optimisation logic to index the usefulness of cached content according to age and popularity respectively. It is therefore necessary to build within our cache algorithms the ability to capture and respond to the complexity in the behaviours of user requests. Hence, one strand of further work will proceed in the direction of establishing and characterising the relevant dynamics affecting the likelihood of repeat requests of cached content based on both popularity and passage of time. A significant part of proposed investigations on this aspect will consider developing strategies for breaking down the cached content into principal categories of popularity (such as High, Moderate and Low popularity) and building time profiles for the request events so that the decay of content popularity is defined as a function of time. The proposed extension will be a further step in the study of the heterogeneity of data access patterns, which in our current models involves two broad classes of data requests; private and public content requests. From the results of this study, we intend to calibrate the cache replacement algorithms on the basis of hybrid criteria that employ adjustable time windows for rating content value.
The allocation of storage cache space for accessed data in our initial studies was simplified through the choice of uniform file sizes. In the next phase of the study, we will therefore also investigate approaches, by which cached content is classified according file sizes. In working towards the overall estimates of the required cache capacity, it will be important to investigate how to characterise patterns of the variability of the range of all file sizes grouped together within each category.
Another dimension worth exploring in the future work is employing the utilisation of the strategy proposed in [22] to harness the cloud infrastructure as a data gathering and dissemination engine to achieve ready availability of context information in supporting informed caching decisions. The information collection and dissemination technique considered in [22] is predicated on the idea that context data exhibits predominantly temporal trends. Hence, cache optimisation mechanisms (most likely in the form of enhanced versions of the LRU policy) can be developed for characterising the time-related properties of cached items in such a way that their values are indexed and, the eviction and retention of content can then follow formal criteria. An additional aspect of scoring the cached files would determine how to categorise the rates of expiration of cached objects based on the frequency of modifications to original files. Typically, the public content which becomes stale more quickly would be based on volatile updates such as live sports news and business feeds.
As has been highlighted in Section "Experimental facility for validations of cache performance Trends", our experimental scenarios employ the Tier 2 configuration i.e. application executions and data fetch operations are conducted on the same physical server. In the next phase, part of the focus will involve deployments based on the Tier 3 setting, whose configuration is such that application routines and data transfer operations are handled in server entities. Based on the outcome of the experiments conducted so far, we consider network delays that are associated with the transfers of requested data to be the most likely factor that can impact the accuracy of the future experiments. Hence, an important aspect of the work on analysing cache performance in Tier 3 server settings will involve characterisations of the network delays so that the time windows for the measurement epochs are properly calibrated according to prevailing conditions on the data transfer paths such as available bandwidth, propagation and congestion delays. And since our current theoretical models basically apply to standalone cache configurations, the Tier 3 scenarios can also be considered in the context of more complex caching environments based on redundant and hybrid physical deployments. Thus, the follow up work will study of the joint use of network management and replica location services on our infrastructure-monitoring framework in order to characterise service performance profiles associated with wide-area data accesses in cloud environments.
The authors acknowledge support for this work from the Engineering and Physical Sciences Research Council (Grant References EP/G051674/1 and EP/J016748/1). Any views or opinions presented herein are those of the authors and do not necessarily represent those of IU-ATC, their associates or their sponsors.
Authors' Affiliations
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- SAP Inc. On-demand solutions from SAP that fit your needs now http://www.sap.com/solutions/technology/cloud/index.epx
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- Rackspace Hosting, Hosting solutions for business . Accessed 12 December 2012 http://www.rackspace.co.uk/managed-hosting/solutions-for-business/ . Accessed 12 December 2012
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- Eucalyptus Systems, Eucalyptus open source cloud computing infrastructure - an overview, Euclayptus Whitepaper . Accessed 12 December 2012 http://www.eucalyptus.com/ . Accessed 12 December 2012
- Alexa - Site Inforamtion . Accessed 14 December 2012 http://www.alexa.com/siteinfo . Accessed 14 December 2012
- Finkelstein S, Brendle R, Jacobs D, Hirsch M, Marquard U: The SAP transaction model: know your applications. ACM. SIGMOD. Conference 2008.Google Scholar
- Xiong K, Perros H: Service performance and analysis in cloud computing. In SERVICES '09 proceedings of the 2009 congress on Services - I. Los Angeles, CA, USA; 2009. July 6–10, 2009 July 6-10, 2009Google Scholar
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This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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subset: commoncrawl


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Beyond ACP Journal Club: how to harness MEDLINE for diagnostic problems
ACP J Club. 1994 Sept-Oct;121:A10. doi:10.7326/ACPJC-1994-121-2-A10
Related Content in the Archives
• Editorial: Beyond ACP Journal Club: how to harness MEDLINE to solve clinical problems
• Editorial: Beyond ACP Journal Club: how to harness MEDLINE for therapy problems
• Editorial: Beyond ACP Journal Club: how to harness MEDLINE for etiology problems
• Editorial: Beyond ACP Journal Club: how to harness MEDLINE for prognosis problems
• Editorial: Beyond ACP Journal Club: how to harness MEDLINE for review articles
The previous editorial in the MEDLINE series, published in the July/August 1994 issue of ACP Journal Club, was on searching for articles addressing therapy questions. This editorial deals with searching for articles on the diagnosis of clinical problems. As with the other editorials in the series, we start with a clinical situation.
One of your long-standing patients, a 50-year-old woman, has made an appointment to ask for your advice. She has come in often over the years with various complaints, including some vague stomach and bowel upsets and weight swings that seem to "run in the family." She is generally healthy although a bit thin. Her niece has been diagnosed with celiac disease. She asks you if screening certain members of her family for this condition might be warranted. Both you and your patient are not all that happy about the prospect of small-bowel biopsy. She asks you about the availability of other less invasive tests.
You remember reading about the diagnostic value of the gliadin antibody test for celiac disease. You tell her you will check to see if the test could be used as a substitute for the biopsy.
You check your personal reprint file and some general medicine evidence-based texts on diagnosis (1-3) and do not find any references to either the test or the disease, even using alternative names such as sprue and gluten enteropathy. Scientific American Medicine (4) does not mention the gliadin test but suggests using fecal fat analysis with the biopsy. ACP Journal Club has not included a single article on celiac disease. It is now too late to call the local gastroenterologist. MEDLINE is next on the list.
Each of the 7 million citations in MEDLINE includes the title of the article, authors, the abstract if available, and keywords assigned by content experts working for the National Library of Medicine (NLM). When you search, you can use the index terms given to the article (Medical Subject Headings [MeSH]), or words in the titles and abstracts of the original article, or a combination of these 2. In other words, you search by trusting the indexers (MeSH headings), the authors (title and abstract), or combine both. Most often, the indexing is a good first approach for MEDLINE searching. By using the indexing, you avoid some of the difficulties of having synonyms for items (e.g., celiac disease, sprue, steatorrhea, and gluten enteropathy), alternative spellings and word endings (e.g., etiology vs. aetiology; esophagitis vs. oesophagitis; and random vs. randomized), and different meanings for the same word (e.g., shoe polish vs. speaking Polish; and AIDS, the disease vs. hearing or teaching aids).
Using title and abstract searching (called textword searching) can be helpful for new concepts or ideas that the indexers have not yet incorporated. For example, it took some time for MEDLINE to include Legionnaire's disease as a MeSH term and restenosis is still on the waiting list. Furthermore, some concepts are not indexed consistently (e.g., sensitivity, specificity, positive and negative likelihood ratios). 2 sample searches follow that show the different approaches and output for a search to find articles that provide the sensitivity, specificity, and predictive values of gliadin antibody tests for screening for celiac disease.
This first search is a textword search:
sensitivity OR specificity OR likelihood OR predictive
celiac OR sprue OR stretorrhea OR gluten enteropathy
English language
This search retrieves 37 citations from 1991 forward, about half of which are relevant to the topic.
The second search, based on MeSH headings is as follows:
gliadin (mh)
celiac disease (mh)
exp sensitivity (mh)
human (mh)
English language (la)
This search retrieves 22 citations, 19 of which were in the first search plus 3 unique citations. Although there were fewer citations in the second search, a larger proportion of them were "on target" than those citations in the textword search.
Either set of citations can help answer the question about the diagnostic properties of gliadin. One of the first citations in each set is abstracted in this issue of ACP Journal Club (see page 51). Bodé and Budmand-Høyer (5) conclude that for adults the sensitivity of the gliadin antibody test was 77%, specificity was 95%, the positive predictive value was 71% to 90%, and the negative predictive value was 97%. The commentator concluded that the gliadin antibody test is an effective noninvasive screening test for patients with suspected celiac disease. A biopsy, however, is still needed to confirm the diagnosis.
Our previous editorial provided the single terms for effective searching for studies of therapy. Table 1 has the best single terms for effective searching for studies of diagnostic tests. We have included 2 searching terms to give you a choice of high sensitivity or high specificity.
Table 1. Sensitivity and Specificity for the Best Single Searching Terms
|Best single searching term
|Term sensitivity
|Term specificity
|Before 1990
|1990 and on
|Before 1990
|1990 and on
|sensitivity (tw)
|diagnosis& (px)*
*Note that the terms diagnosis& (px) means that all the MeSH subheadings that deal with diagnosis will be included in the search automatically, a feature called "exploding" or, in this case, "pre-exploding." We discussed exploding in our previous editorial when we grouped all types of headaches together for the therapy scenario.
If the search terms in Table 1 do not provide satisfactory results, many options exist that could help, including 1 or more terms in Table 2.
Table 2. Textwords and MeSH for Searching for Diagnostic Problems
|Textwords for searching for diagnostic problems
|MeSH for searching for diagnostic problems
|sensitivity, specificity, predictive and value, false and reaction, likelihood and ratio
|sensitivity and specificity, predictive value of tests, ROC curve, false negative reaction, false positive reaction, diagnosis, differential, mass screening, diagnostic use (sh), diagnosis* (sh)
*This subheading can be used in various ways by different searching systems. Use your manual or check with a medical librarian for exact uses and searching conventions.
NLM and other vendors produce many databases, and some of these may be better tailored to specific clinical questions than the general MEDLINE file. Most searching systems, including GRATEFUL MED, can access these databases. Others are available only through specific vendors who provide various "subsets" of MEDLINE and journals to suit specialized interests. EMBASE (Excerpta Medica online), a European "equivalent" to MEDLINE, is another general alternative to MEDLINE with an overlapping set of journals.
The following specialized literature databases are available through NLM and other vendors. AIDSLINE contains references on AIDS and related topics. References are taken from MEDLINE, HEALTH, and CANCERLIT databases from 1980 forward, with additional unique citations added. AIDSLINE and other AIDS-related databases such as AIDSTRIALS and AIDSDRUGS are available free of charge for both health care professionals and consumers.
AVLINE is an audiovisual catalogue that contains listings for all audiovisual materials catalogued by NLM since 1975. The materials are intended for health professionals rather than patients.
CANCERLIT provides citations to cancer literature. Books, technical reports, meeting abstracts, meeting papers, and theses are also included. Material dates back to 1963.
CATLINE contains citations to printed books and journals in the collection at NLM.
CHEMLINE is a combined dictionary and database containing data that can be helpful in selecting strategies for other NLM databases. A user can identify a substance, determine its Chemical Abstracts Registry number, and find out which databases would carry more information on the substance.
DIRLINE contains information on over 15 000 organizations that act as information resource centers. DIRLINE references include names, addresses, telephone numbers, and descriptions of each organization, including primary interests and services.
HEALTH Planning and Administration database provides references back to 1975 on nonclinical aspects of health care delivery including administration and planning of health facilities, services, health insurance, and health care aspects of financial management, regulation, personnel, quality assurance, licensure, and accreditation.
SDILINE is a database that contains references from the current month of MEDLINE and permits you to "store" searches for automatic updating each month.
TOXLINE and TOXLINE65 contain references to the pharmacologic, physiologic, biochemical, and toxicologic effects of drugs and other chemicals. TOXLINE has the current files.
TOXLIT and TOXLIT65 contain references covering the pharmacologic, physiologic, biochemical, and toxicologic effects of drugs and other chemicals. These files carry royalty charges and are more expensive to search than TOXLINE and TOXLINE65. The toxicology files have some overlap, but enough citations are unique that both may need to be searched for comprehensive retrievals.
K. Ann McKibbon, MLS
Cynthia J. Walker-Dilks, MLS
1. Sox HC Jr, ed. Common Diagnostic Tests: Use and Interpretation. Philadelphia: American College of Physicians; 1987.
2. Eddy DM, ed. Common Screening Tests. Philadelphia: American College of Physicians; 1991.
3. Panzer RJ, Black ER, Griner PF, eds. Diagnostic Strategies for Common Medical Problems. Philadelphia: American College of Physicians; 1991.
4. Rubenstein E, Federman DD, eds. Scientific American Medicine. New York: Scientific American Inc; 1978.
5. Bodé S, Budmand-Høyer E. Evaluation of the gliadin antibody test for diagnosing coeliac disease. Scand J Gastroenterol. 1994;29:148-52.


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Based on the way in which the baby is , furthermore, you'll find coats which could possibly be employed for babies (usually whenever they're a little older ). On the contrary hand, in case it's too loose, your son or daughter will get tough time walking indoors them. Your son or daughter will love them later on Bearing this in mind, you still need to get one which could satisfactorily protect your youngster from the snow and ice hockey.
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Ok, I Think I Understand Leather Jacket For Toddler Boy, Now Tell Me About Leather Jacket For Toddler Boy!
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Finding Leather Jacket For Toddler Boy on the Web
Clothing really could make an impression about you and your family members. There's a massive selection of toddlers' clothing available to find in the coziness of of one's personal house. Another good method to save about having clothing, is by simply shopping in outlet malls. Cowboy style clothing is probably the very elastic types of style it's likely to find.
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text:
As the name suggests, the Sunshine Coast is a beautiful coastal town located on the Pacific Ocean coastline of Australia. Apart from being the home to several beautiful beaches including The Noosa Main Beach, Coolum Beach, Kawana Waters, Maroochydore, Mooloolaba and Kings Beach, the Sunshine Coast offers plenty of options for shopping in Australia.
There are a number of high scale shopping centers in Sunshine coast. Some of the most prominent ones include Noosa Civic, Sunshine Plaza, Stockland Caloundra and Kawana shopping world. These shopping centers are home not only to luxury brands like Coles, Woolworths, Best & Less, JB HiFi, but also have discounted stores like Target, Kmart, Myer, The reject shop and Big W.
Apart from these luxury and discount stores, a number of fashionable boutiques and custom made dress shops.


text:
Background: Between paper notebooks, post-it notes, OneNote, Evernote, and so many more, there is no shortage of ways to write things down. Each has its strengths and weaknesses, but none satisfied my requirements to act as a daily work log to record key events, thoughts, and milestones during my work day.
My paper notebook is excellent for free-form thoughts, sketches, and calculations, but I would want to keep a separate notebook to keep track of these sequential events. We use OneNote at work, but where the infinite blank canvas is a strength in applications such as for research or brainstorming, I found it to be a weakness in record keeping since the document is too easy to edit and "fragile".
One day after learning about Git and SourceTree, I knew wanted to create a similar commit-style application to record activites and "freeze" them in time. Not web-based, internet connected, or cross platform; just a no frills desktop application that does one thing and one thing only.
Objective: Develop a simple application to log daily activities at work.
Framework: C#
Source: Github
Published on October 1, 2016


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text:
Galaxy Hut goes west
We caused a ripple of suburban excitement last summer after reporting that Clarendon's iconic, idiosyncratic indie-rock beer bar was opening a second location in Falls Church, taking over the former Stacy's Coffee Parlor on West Broad Street. Owner Lary Hoffman now thinks his new place, Space Bar, will finally be ready to go in February. Look for two dozen microbrews on draft, a "gourmet grilled cheese menu" with sandwiches prepared on a grill behind the bar, and more space than the original Galaxy Hut, but (hopefully) with the same hip vibe. Once the bar is up and running, Space Bar will begin to expand: A rooftop deck is planned for late 2012 or, more likely, 2013.
3 Stars adds to the local beer haul
Like I said in my look back at the bar trends of 2011, last year was really about the rise of local breweries, with DC Brau, Port City, Chocolate City and Lost Rhino all becoming fixtures on local taps. Next to join them: 3 Stars, which is building out its brewery in upper Northwest D.C. If the name sounds familiar, it should: 3 Stars spent 2011 collaborating with Delaware's Evolution Brewing Company and Baltimore's Oliver Breweries, and those beers found their way into ChurchKey, Smith Commons, Meridian Pint and the Big Hunt. (Big Hunt beertender Dave Coleman is one of 3 Stars' founders.) Look for 3 Stars' ales to start flowing this spring.
A rocking new bar on Columbia Pike
South Arlington may not be a nightlife hub yet, but it's building. William Jeffrey's Tavern opened its doors in mid-December, and coming later this year is TNT Bar, a cocktail bar helmed by famed mixologist Todd Thrasher of PX and Restaurant Eve. In contrast to PX's speakeasy style, expect TNT to have loud rock music, a few dozen seats and a cocktail menu that blends original concoctions with drinks created by bartenders from around the world. Back in November, Thrasher told me he expected TNT to open in March or April. From walking by, it looks like it may be closer to the summer.
Growth in Columbia Heights
The stretch of 11th Street NW from Park Road to Kenyon Street is one of my favorite strips for a bar crawl: Craft beers at Meridian Pint, pizza at RedRocks, a cocktail at Room 11, late-night dancing and shenanigans at Wonderland Ballroom. There are going to be a few new additions this year: Jackie Greenbaum (of Jackie's, Sidebar and Quarry House) is opening a Mexican taqueria north of Lamont Street sometime this spring, with a rooftop deck and $5 margaritas. The Tryst/Diner/Open City team is opening Margot's Chair, a 24-hour coffeehouse and restaurant at 11th and Monroe, in "early 2012." And Room 11, which can be cramped at the best of times, announced this week that it's taking over the neighboring Jeans N Things store to expand the dining room and kitchen. Work should be finished this summer.
Drinking in a historic place
It's been a year since work began on Chez Billy, the Petworth landmark that's the latest project for Eric and Ian Hilton. (See also: Marvin, American Ice Company, Blackbyrd Warehouse.) The French bistro, which will feature a DJ lounge, occupies the former Billy Simpson's House of Seafood and Steaks at 3815 Georgia Ave. NW, a building listed on the National Register of Historic Places. In segregated Washington, Billy Simpson's and its upstairs Ebony Table lounge was a gathering place for African-American politicians, civil rights leaders and entertainers. (The description of Billy Simpson's on the National Park Service Web site (PDF) is a fascinating read, containing details of Ella Fitzgerald sipping Moscow Mules as well as meetings chaired by Walter Washington and Jesse Jackson there.) Petworth could use a destination restaurant and lounge, and with all this history, Chez Billy has a lot to live up to when it opens in the next month or so (fingers crossed).


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Here are the 7 most important Google Webmaster Tool functions that you need to be on top of for 2013. Also, included details on how to use each function and why.
Sitemaps – this is one of the most important features of Google Webmaster tools. If you don't know how to use this feature then you must learn as soon as possible. It is very easy to implement. Depending on the type of website you have, (joomla, wordpress, drupal, or straight html/css/php) there are different options when creating a sitemap. Google accepts sitemaps in .xml format. For many platforms you can easily install a plugin that automatically generates the sitemap for you. For WordPress visit http://wordpress.org/extend/plugins/google-sitemap-generator/. For joomla visit http://extensions.joomla.org/extensions/structure-a-navigation/site-map. For drupal visit http://drupal.org/project/xmlsitemap. If you have a straight html site visit http://sitemapxml.net/.
After you have created a sitemap.xml page on your site, simply put in the URL of your sitemap in webmaster tools. Google will now see every page on your website which is very beneficial to SEO. This will force Google to see all of your content and will have a greater chance to index all of your pages. If you own a website and have not implemented this feature, do so right now!
Crawl Errors – from talking to other webmasters this seems to be a feature overlooked. Google lets you know that they have an issue with crawling your site's content. Majority of errors that are listed here are 404 errors. That means a page that used to be on your site, Google can't find anymore. For example say we have a page located at http://www.purposeadvertising.com/SEO. We happened to have deleted that page because we don't like it anymore and remade it at http://www.purposeadvertising.com/search-engine-optimization. Google will then list that under the crawl errors page. Now here is the secret of the pros… simply create a redirect from the page that doesn't exist anymore to the new page you created. Information of creating redirect can be found here http://support.google.com/webmasters/bin/answer.py?hl=en&answer=93633. Once you setup the redirect, mark it as fixed in webmaster tools.
Search Queries – Google lets you know exactly what people type into Google that your site shows up for. Use this as a good starting point when performing search engine optimization techniques. Use the query list as starter keywords to build up.
Links to Your Site – this is probably my favorite feature of webmaster tools. To other SEO companies, how great is this!? Google shows you every other site that links to you on the web. Use this tool to your advantage by visiting the sites that link to your website. If the website that is linking to you is not relevant to your website, then use Google's disavow tool to tell them you don't approve this site linking to you. Google states that there is no such thing as "negative seo" but there is. If a competitor builds a bunch of bad links to your website, you will be hurting in the rankings. It's up to you to look at the links to your site and determine if they are legit. The non legit ones, tell Google about.
HTML Improvements – the most common HTML improvement you need to make is duplicate title & descriptions on each of your pages. Simple edit your site to have unique meta titles & descriptions. Of course keep it as relevant as possible but unique. I can't stress enough on having uniqueness on your website.
Structured Data – this is a newer function offered by Google. Using http://schema.org/ implement any new type of coding that is appropriate for your website. For us, we use the coding around our business information and all of the services we offer. Google says that this doesn't help with search engine optimization but how can it not. This helps Google see what your webpage is about. Please Google in any way possible for better rankings.
Site Performance (Now in Google Analytics)– saved the best for last. Google's algorithm factors in just how fast your website is when ranking you in the results. Have you ever clicked to visit a site and about 8 seconds later it still doesn't load? So what do you do? You close it and will visit a different site. Having a fast site pleases the users so that's why Google decided that it was really important. One can do many different things to make a website faster. The quickest and easiest way to increase site performance is getting your site on a content delivery network (CDN). An inexpensive CDN can be found here http://www.maxcdn.com. A CDN will put your website files on multiple servers located throughout the United States and the World so whoever connects to your site from their physical location, will get the files from your website from the server located closed to them. This is increase the performance of your site because now it's not on a centralized location. If you have poor website performance, such as on shared hosting from Godaddy, get a CDN host right now!
Song of the day: The Parlor Mob – Can't Keep No Good Boy Down
Spotify it here
Comment question of the day: What's your favorite Google webmaster tools feature?
Please leave your interesting and creative responses below.


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Now Open: Dagwoods St-Martin
3280 boul. St-Martin
(Corner Daniel-Johnson)
Montreal Impact – Saputo Stadium
Come and visit our kiosk in section 124/125 and enjoy a freshly sliced
Dagwoods Sandwich with Dagwoods Special Sauce.
Welcome to Dagwoods
Hungry for the freshest sandwich in town? Have a craving for our famous Dagwoods sauce? Come to one of our many Dagwoods locations where our bread is baked fresh everyday and our meats are sliced as you order your sandwich.
Dagwoods Catering
You've been "volunteered" to organize a little get-together? That's easy! Whether you have an office meeting, team party or family event, our catering service has awesome sandwich platters, salads and desserts. Ordering online is so easy, you'll have plenty of time left for relaxing!
Interested in being part of the Dagwoods team? Visit our Employment Opportunities to learn more and apply online now.


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At the end of this street is a cosy restaurant selling glorious Ginseng Chicken, replete with an Ahjumma's warm welcome with impressive Korean-tinged English. A good place to recuperate after jostling your way through the crowds along this street mall.
The way I see it, Myeong-Dong is primarily an open air warehouse with meandering lanes that offer a mind-boggling range of goods at very competitive prices. The bustling area of commerce for large chains and artisan shops peddling wares from cosmetics, clothes, jewellery to food, pottery, socks, is also a popular hangout for the young and chic.
And the eager bargain hunters. *tee hee*
Weekends seem to be the time to be there if the dread of crowds is overtaken by the love of even MORE stalls and bargains to be had.
The city converges at Seoul's marketplaces on rest days, and trading is at its peak then. Whether it is Myeong-Dong, Namdaemun or Hongdae, it never gets dull – there is always something happening (shop assistants yelling about freebies and deals) somewhere (then the shop opposite does the same).
When weary from all that walking, recharge with caffeine at one of many cute coffee joints and some chewy Deokbokki from a streetstall.
Or, have some good old Ginseng Chicken soup.


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I was shocked to find that most people considered cable tv a necessity. TV, really? Come on. If you can't survive without 50 channels, maybe you should consider getting a hobby. Besides... if you have an internet connection, you can watch most shows online anyway. Why pay for it twice? It is one of the first things I have considered cutting should we lose any more income.
Faster downloading was also considered a necessity. Lite Internet vs unlimited downloading is a difference of about $50 a month for cable Internet. I would consider downloading excess amounts of data a luxury, unless you need it for work. Most libraries offer free Internet services, plus there are numerous Internet cafes if you need your online fix. Inconvenient yes, but not impossible.
Most people consider having a car a luxury. Where I live, I have to have a car. There is no public transportation from one town to another here, but if I lived in a big city I'd be taking the train. Or carpooling. Not only is it healthier for the earth, it's better for your pocketbook.
Luxuries we have that I could live without if I had too...
- Clothes Dryer - I could hang to dry, but it's inconvenient.
- Microwave - I could heat it up in the oven, but again... inconvenient
- Cable tv/internet
- Cell phone (we all know how I feel about THAT)
- Take-out food
What would you give up if you had too?


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Appliance Leak Dryout Services
Appliances are not supposed to leak! If they do, there are a couple of steps that should be taken immediately to avoid further damage to your home or place of business. First, turn off the appliance if it is currently running. If you've still got flooding, turn off the water at its source right away. Call your local plumber to fix the problem that is causing the leaking, then call A1 Steamway to remove the water and dry your property.
Call (720) 538-0488
24/7 Emergency Service
Since 2006, A1 Steamway has responded with swift action to remediate properties that have flooded because of appliance leaks. The usual culprits include:
- Dishwashers
- Washing machines
- Refrigerators
- Ice boxes
- Water heaters
- RO (reverse osmosis) systems
- and more!
Appliance Leak Assessment
Water pooling around a water heater doesn't always mean that it's leaking. It could just be condensation. If you see active dripping, or the water is flooding from the heater, call your HVAC repair service right away. If your property has suffered water damage, call A1 Steamway ASAP. We'll assess the damage that can be seen, as well as test for moisture behind your walls. Sometimes, washing machines or other appliances will leak from the second story of a building, through the flooring and down to the first level. When this occurs, A1 Steamway can help. We can even initiate and follow through with an insurance claim so you won't have to. Call (720) 538-0488 if you are experiencing water damage due to an appliance leak.
Water Loss Experts
Have your plumber stop the leaking, then call A1 Steamway to handle your water loss mess! We specialize in emergency dry out services throughout the area. Our response team will arrive within 45 minutes to assess the situation and prevent further damage. Next, we'll extract standing water and provide high powered drying and dehumidification machines to remove moisture from flooring and behind walls. All materials that are damaged beyond repair will be removed. We'll remove all traces of water and moisture and prepare you for the process of reconstruction.


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Kevin Poeschel
Address: W5627 Babcock Drive; Elkhorn,
Phone: 262-742-3513 Fax:
E-mail: firstname.lastname@example.com
Personal Website:
Return to Turkey
Call List
Poeschel Custom Calls
quick glance at the last name and it becomes quite obvious; Kevin
is the son of Dennis Poeschel who was instrumental with Custom Calls
Online coming to fruition. Born and raised in Milwaukee, Wisconsin
Kevin like his father is active in a number of organizations like
NWTF, DU, Wisconsin Waterfowl Association and also Delta Waterfowl.
A knowledgeable hunter and avid fisherman, Kevin participates in
most of these outdoor activities in the Wisconsin area. He loves
to hunt duck, goose, turkey and even deer but looks forward to widening
his game selection in the future.
call making ability was the direct result from a desire to spend
quality time with his father. He recounts the many hours his dad
spent in the shop building calls and Kevin always enjoyed helping
out any way he could. During those early years, call making was
looked at as just a fun activity with dad. But, over the course
of time he picked up a wealth of information that many of todays
call makers would consider invaluable. Call making in essence came
natural to Kevin, thanks to this great teacher, but what has also
played an important role is his background in art.
quickly picked up on the proper steps and processes involved in
making duck and goose calls, but now he finds himself leaning more
toward turkey calls. When making turkey calls, Kevin feels the sky
is the limit because of the many styles that can be produced. In
his mind, turkey calls are completely open to the imagination as
these allow you to become very creative. This ability to be creative
is also why he doesnt have a favorite type of call to make.
Instead, it is more of a favorite thing to do with a call. His most
enjoyable aspect of call making is the ability to carve. Kevin states:
When you carve on a call it makes that call unique. No other call
will ever be alike, you can make a call to look close but it will
never be identical.
time or hobby call making is a decision many call makers face as
they begin building calls. For Kevin, he prefers to leave it as
a hobby for fear that if it turns into a business, the enjoyment
will fade and the quality wont be the same. He also finds
the process of call making extremely relaxing.
produces a variety of calls out of many woods. His preference is
the different flavors of Walnut especially English. He builds approximately
30 or less calls each year and provides both types of calls. Field
grade calls for hunting and collector grade calls that can cure
the itch of any collector. He regularly enters many of the call
making shows and has won numerous first and second place ribbons.
His most significant accomplishment was winning the Best of Show
at the CCAA in 2002. His greatest challenge today is trying to win
in a show and be standing next to his father who has also won in
a picture of the top winners.
Overall Kevin enjoys nothing better than to
make calls for the serious hunter. The greatest satisfaction is
hearing how his customers enjoy his calls and how they helped make
someones hunt successful.


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Planning Applications
Planning Applications at Appeal
Penvose Student Village
Penwerris Coachworks Development
Swanpool Road Site
Rosslyn Hotel Development
Fish Strand Hill
Ocean Bowl Development
Fourwinds Pub Development
Land South of Boscawen Road
Silverdale Road Site
HMO Applications
Falmouth Beach Hotel Development
Tremough Campus
Premier Inn
3 Tresawna Terrace
Housing Survey - Falmouth
Register on Inspectorate Page
Register to comment online to planning
Comment on a planning application
Reasons to object to a planning application
Defibrillators in Falmouth
Useful Contacts
Template letters of objection
Falmouth Town Council
Strategic Planning Commitee
Falmouth Neighbourhood Plan
Falmouth Hospital
Article 4 Direction for Falmouth
Whats On
About Us
Contact Us
SOF Public Meetings
Comment on a planning application
A comment can be submitted and viewed instantly by using the council website.
There are two things needed to
to a planning application, firstly a Login account with the council website and
secondly the planning application reference number i.e.
If you do not have an account with the council please see our webpage:
Register to comment online to a planning application
Link to our webpage: Register to comment online to a planning application
If you do not know the planning application reference number you can easily search for this by using the search facility on the council planning website.
Link to council website: search for a planning application
Click on:
Search for a planning application
The search options are shown in the image below.
Enter a keyword in the search bar highlighted in yellow i.e.
Rosslyn Hotel
and click on the
Search button.
Any applications, current or historic which include your search words will be listed.
Click on the underlined text to go to the application.
This will show the Application Summary Page
Click on the
Comments Tab
, highlighted below in yellow.
This will show the Application Comments Page
Click on the
Login and make a comment text
, highlighted below in yellow.
This will show the Login page.
Click on the
Login to website account
, highlighted below in yellow.
if you have previously registered enter your email address and password, if not click on the
, shown highlighted in the image in yellow.
See our webpage:
Register to comment online to planning.
Link to our webpage: Register to comment online to a planning application
The window will be predominately auto filled with your details from your registration.
But please see the below image showing further details to be submitted:
Select from the drop down list
Commenter Type, i.e. Neighbour.
Select whether you
Object, Support, Neutral.
Select a
reason for comment
, any or all can be selected.
your comment
and click
Please note that there is a time limit of 30 minutes to enter your comment.
It is useful to type up your text beforehand, in Notepad for example, and then just paste it into the comments box.
(Please do not use any fancy layouts or symbols because this will not be accepted)
If you want to add pictures or something more elaborate you can email your comments to the council, perhaps a pdf file.
[email protected]
You can email your comment: Email:
[email protected]
Please quote the planning application number. N.B. this is not instant and may take a couple of days to appear.