Investigation of service selection algorithms for grid services

Grid computing has emerged as a global platform to support organizations for coordinated sharing of distributed data, applications, and processes. Additionally, Grid computing has also leveraged web services to define standard interfaces for Grid services adopting the service-oriented view. Consequently, there have been significant efforts to enable applications capable of tackling computationally intensive problems as services on the Grid. In order to ensure that the available services are assigned to the high volume of incoming requests efficiently, it is important to have a robust service selection algorithm. The selection algorithm should not only increase access to the distributed services, promoting operational flexibility and collaboration, but should also allow service providers to scale efficiently to meet a variety of demands while adhering to certain current Quality of Service (QoS) standards. In this research, two service selection algorithms, namely the Particle Swarm Intelligence based Service Selection Algorithm (PSI Selection Algorithm) based on the Multiple Objective Particle Swarm Optimization algorithm using Crowding Distance technique, and the Constraint Satisfaction based Selection (CSS) algorithm, are proposed. The proposed selection algorithms are designed to achieve the following goals: handling large number of incoming requests simultaneously; achieving high match scores in the case of competitive matching of similar types of incoming requests; assigning each services efficiently to all the incoming requests; providing the service requesters the flexibility to provide multiple service selection criteria based on a QoS metric; selecting the appropriate services for the incoming requests within a reasonable time. Next, the two algorithms are verified by a standard assignment problem algorithm called the Munkres algorithm. The feasibility and the accuracy of the proposed algorithms are then tested using various evaluation methods. These evaluations are based on various real world scenarios to check the accuracy of the algorithm, which is primarily based on how closely the requests are being matched to the available services based on the QoS parameters provided by the requesters

Investigation of service selection algorithms for grid services

Grid computing has emerged as a global platform to support organizations for coordinated sharing of distributed data, applications, and processes. Additionally, Grid computing has also leveraged web services to define standard interfaces for Grid services adopting the service-oriented view. Consequently, there have been significant efforts to enable applications capable of tackling computationally intensive problems as services on the Grid. In order to ensure that the available services are assigned to the high volume of incoming requests efficiently, it is important to have a robust service selection algorithm. The selection algorithm should not only increase access to the distributed services, promoting operational flexibility and collaboration, but should also allow service providers to scale efficiently to meet a variety of demands while adhering to certain current Quality of Service (QoS) standards. In this research, two service selection algorithms, namely the Particle Swarm Intelligence based Service Selection Algorithm (PSI Selection Algorithm) based on the Multiple Objective Particle Swarm Optimization algorithm using Crowding Distance technique, and the Constraint Satisfaction based Selection (CSS) algorithm, are proposed. The proposed selection algorithms are designed to achieve the following goals: handling large number of incoming requests simultaneously; achieving high match scores in the case of competitive matching of similar types of incoming requests; assigning each services efficiently to all the incoming requests; providing the service requesters the flexibility to provide multiple service selection criteria based on a QoS metric; selecting the appropriate services for the incoming requests within a reasonable time. Next, the two algorithms are verified by a standard assignment problem algorithm called the Munkres algorithm. The feasibility and the accuracy of the proposed algorithms are then tested using various evaluation methods. These evaluations are based on various real world scenarios to check the accuracy of the algorithm, which is primarily based on how closely the requests are being matched to the available services based on the QoS parameters provided by the requesters

Investigation of service selection algorithms for grid services

Grid computing has emerged as a global platform to support organizations for coordinated sharing of distributed data, applications, and processes. Additionally, Grid computing has also leveraged web services to define standard interfaces for Grid services adopting the service-oriented view. Consequently, there have been significant efforts to enable applications capable of tackling computationally intensive problems as services on the Grid. In order to ensure that the available services are assigned to the high volume of incoming requests efficiently, it is important to have a robust service selection algorithm. The selection algorithm should not only increase access to the distributed services, promoting operational flexibility and collaboration, but should also allow service providers to scale efficiently to meet a variety of demands while adhering to certain current Quality of Service (QoS) standards. In this research, two service selection algorithms, namely the Particle Swarm Intelligence based Service Selection Algorithm (PSI Selection Algorithm) based on the Multiple Objective Particle Swarm Optimization algorithm using Crowding Distance technique, and the Constraint Satisfaction based Selection (CSS) algorithm, are proposed. The proposed selection algorithms are designed to achieve the following goals: handling large number of incoming requests simultaneously; achieving high match scores in the case of competitive matching of similar types of incoming requests; assigning each services efficiently to all the incoming requests; providing the service requesters the flexibility to provide multiple service selection criteria based on a QoS metric; selecting the appropriate services for the incoming requests within a reasonable time. Next, the two algorithms are verified by a standard assignment problem algorithm called the Munkres algorithm. The feasibility and the accuracy of the proposed algorithms are then tested using various evaluation methods. These evaluations are based on various real world scenarios to check the accuracy of the algorithm, which is primarily based on how closely the requests are being matched to the available services based on the QoS parameters provided by the requesters

Quality assessment of service providers in a conformance-centric Service Oriented Architecture

In a Service Oriented Architecture (SOA), the goal of consumers is to discover and use services which lead to them experiencing the highest quality, such that their expectations and needs are satisfied. In supporting this discovery, quality assessment tools are required to establish the degree to which these expectations will be met by specific services. Traditional approaches to quality assessment in SOA assume that providers and consumers of services will adopt a performance-centric view of quality, which assumes that consumers will be most satisfied when they receive the highest absolute performance. However, adopting this approach does not consider the subjective nature of quality and will not necessarily lead to consumers receiving services that meet their individual needs. By using existing approaches to quality assessment that assume a consumer's primary goal as being optimisation of performance, consumers in SOA are currently unable to effectively identify and engage with providers who deliver services that will best meet their needs. Developing approaches to assessment that adopt a more conformance-centric view of quality (where it is assumed that consumers are most satisfied when a service meets, but not necessarily exceeds, their individual expectations) is a challenge that must be addressed if consumers are to effectively adopt SOA as a means of accessing services. In addressing the above challenge, this thesis develops a conformance-centric model of an SOA in which conformance is taken to be the primary goal of consumers. This model is holistic, in that it considers consumers, providers and assessment services and their relationship; and novel in that it proposes a set of rational provider behaviours that would be adopted in using a conformance-centric view of quality. Adopting such conformance-centric behaviour leads to observable and predictable patterns in the performance of the services offered by providers, due to the relationship that exists between the level of service delivered by the service and the expectation of the consumer. In order to support consumers in the discovery of high quality services, quality assessment tools must be able to effectively assess past performance information about services, and use this as a prediction of future performance. In supporting consumers within a conformance-centric SOA, this thesis proposes and evaluates a new set of approaches to quality assessment which make use of the patterns in provider behaviour described above. The approaches developed are non-trivial – using a selection of adapted pattern classification and other statistical techniques to infer the behaviour of individual services at run-time and calculating a numerical measure of confidence for each result that can be used by consumers to combine assessment information with other evidence. The quality assessment approaches are evaluated within a software implementation of a conformance-centric SOA, whereby they are shown to lead to consumers experiencing higher quality than with existing performance-centric approaches. By introducing conformance-centric principles into existing real-world SOA, consumers will be able to evaluate and engage with providers that offer services that have been differentiated based on consumer expectation. The benefits of such capability over the current state-of-the-art in SOA are twofold. Firstly, individual consumers will receive higher quality services, and therefore will increase the likelihood of their needs being effectively satisfied. Secondly, the availability of assessment tools which acknowledge the conformance-centric nature of consumers will encourage providers to offer a range of services for consumers with varying expectation, rather than simply offering a single service that aims to delivery maximum performance. This recognition will allow providers to use their resources more efficiently, leading to reduced costs and increased profitability. Such benefits can only be realised by adopting a conformance-centric view of quality across the SOA and by providing assessment services that operate effectively in such environments. This thesis proposes, develops and evaluates models and approaches that enable the achievement of this goal