A Reliable Web Services Selection Method for Concurrent Requests

Current methods of service selection based on quality of service (QoS) usually focus on a single service request at a time, or let the users in a waiting queue wait for Web services when the same functional Web service has more than one requests, and then choose the Web service with the best QoS for the current request according to its own needs. However, there are multiple service requests for the same functional web service at a time in practice and we cannot choose the best service for users every time because of the service’s load. This paper aims at solving the Web Services selection for concurrent requests and developing a global optimal selection method for multiple similar service requesters to optimize the system resources. It proposes the improved social cognitive (ISCO) algorithm which uses genetic algorithm for observational learning and uses deviating degree to evaluate the solution. Furthermore, to enhance the efficiency of ISCO, the elite strategy is used in ISCO algorithm. We evaluate performance of the ISCO algorithm and the selection method through simulations. The simulation results demonstrate that the ISCO is valid for optimization problems with discrete data and more effective than ACO and GA

A2THOS: Availability Analysis and Optimisation in SLAs

IT service availability is at the core of customer satisfaction and business success for today’s organisations. Many medium-large size organisations outsource part of their IT services to external providers, with Service Level Agreements describing the agreed availability of outsourced service components. Availability management of partially outsourced IT services is a non trivial task since classic approaches for calculating availability are not applicable, and IT managers can only rely on their expertise to fulfil it. This often leads to the adoption of non optimal solutions. In this paper we present A2THOS, a framework to calculate the availability of partially outsourced IT services in the presence of SLAs and to achieve a cost-optimal choice of availability levels for outsourced IT components while guaranteeing a target availability level for the service

Optimal QoS aware multiple paths web service composition using heuristic algorithms and data mining techniques

The goal of QoS-aware service composition is to generate optimal composite services that satisfy the QoS requirements defined by clients. However, when compositions contain more than one execution path (i.e., multiple path's compositions), it is difficult to generate a composite service that simultaneously optimizes all the execution paths involved in the composite service at the same time while meeting the QoS requirements. This issue brings us to the challenge of solving the QoS-aware service composition problem, so called an optimization problem. A further research challenge is the determination of the QoS characteristics that can be considered as selection criteria. In this thesis, a smart QoS-aware service composition approach is proposed. The aim is to solve the above-mentioned problems via an optimization mechanism based upon the combination between runtime path prediction method and heuristic algorithms. This mechanism is performed in two steps. First, the runtime path prediction method predicts, at runtime, and just before the actual composition, execution, the execution path that will potentially be executed. Second, both the constructive procedure (CP) and the complementary procedure (CCP) heuristic algorithms computed the optimization considering only the execution path that has been predicted by the runtime path prediction method for criteria selection, eight QoS characteristics are suggested after investigating related works on the area of web service and web service composition. Furthermore, prioritizing the selected QoS criteria is suggested in order to assist clients when choosing the right criteria. Experiments via WEKA tool and simulation prototype were conducted to evaluate the methods used. For the runtime path prediction method, the results showed that the path prediction method achieved promising prediction accuracy, and the number of paths involved in the prediction did not affect the accuracy. For the optimization mechanism, the evaluation was conducted by comparing the mechanism with relevant optimization techniques. The simulation results showed that the proposed optimization mechanism outperforms the relevant optimization techniques by (1) generating the highest overall QoS ratio solutions, (2) consuming the smallest computation time, and (3) producing the lowest percentage of constraints violated number

Framework for Optimal Selection Using Meta‐Heuristic Approach and AHP Algorithm

Many real‐life decisions are focused on selecting the most preferable combination of available options, by satisfying different kinds of preferences and internal or external constraints and requirements. Focusing on the well‐known analytical hierarchical process (AHP) method and its extension CS‐AHP for capturing different kinds of preferences over two‐layered structure (including conditionally defined preferences and preferences about dominant importance), we propose a two‐layered framework for identifying stakeholders’ decision criteria requirements and employ meta‐heuristic algorithms (i.e., genetic algorithms) to optimally make a selection over available options. The proposed formal two‐layered framework, called OptSelectionAHP, provides the means for optimal selection based on specified different kinds of preferences. The framework has simultaneously proven optimality applied in software engineering domain, for optimal configuration of business process families where stakeholders’ preferences are defined over quality characteristics of available services (i.e., QoS attributes). Furthermore, this domain of application is characterized with uncertainty and variability in selection space, which is proven and does not significantly violate the optimality of the proposed framework

Load Index Metrics for an Optimized Management of Web Services: A Systematic Evaluation

The lack of precision to predict service performance through load indices may lead to wrong decisions regarding the use of web services, compromising service performance and raising platform cost unnecessarily. This paper presents experimental studies to qualify the behaviour of load indices in the web service context. The experiments consider three services that generate controlled and significant server demands, four levels of workload for each service and six distinct execution scenarios. The evaluation considers three relevant perspectives: the capability for representing recent workloads, the capability for predicting near-future performance and finally stability. Eight different load indices were analysed, including the JMX Average Time index (proposed in this paper) specifically designed to address the limitations of the other indices. A systematic approach is applied to evaluate the different load indices, considering a multiple linear regression model based on the stepwise-AIC method. The results show that the load indices studied represent the workload to some extent; however, in contrast to expectations, most of them do not exhibit a coherent correlation with service performance and this can result in stability problems. The JMX Average Time index is an exception, showing a stable behaviour which is tightly-coupled to the service runtime for all executions. Load indices are used to predict the service runtime and therefore their inappropriate use can lead to decisions that will impact negatively on both service performance and execution cost

Service Re-Selection for Disruptive Events in Mobile Environments: A Heuristic Technique for Decision Support at Runtime

Modern service-based processes in mobile environments are highly complex due to the necessary spatial–temporal coordination between multiple participating users and the consideration of context information. Due to the dynamic nature of mobile environments, disruptive events occur at runtime, which require a re-selection of the planned service compositions respecting multiple users and context-awareness. Thereby, when re-selecting services the features performance, solution quality, solution robustness and alternative solutions are essential and contribute to the efficacy of service systems. This paper presents an optimization-based heuristic technique based on a stateful representation that uses a region-based approach to re-select services considering multiple users, context information and in particular disruptive events at runtime. The evaluation results, which are based on a real-world scenario from the tourism domain, show that the proposed heuristic is superior compared to competing artifacts

A MULTI-FUNCTIONAL PROVENANCE ARCHITECTURE: CHALLENGES AND SOLUTIONS

In service-oriented environments, services are put together in the form of a workflow with the aim of distributed problem solving. Capturing the execution details of the services' transformations is a significant advantage of using workflows. These execution details, referred to as provenance information, are usually traced automatically and stored in provenance stores. Provenance data contains the data recorded by a workflow engine during a workflow execution. It identifies what data is passed between services, which services are involved, and how results are eventually generated for particular sets of input values. Provenance information is of great importance and has found its way through areas in computer science such as: Bioinformatics, database, social, sensor networks, etc. Current exploitation and application of provenance data is very limited as provenance systems started being developed for specific applications. Thus, applying learning and knowledge discovery methods to provenance data can provide rich and useful information on workflows and services. Therefore, in this work, the challenges with workflows and services are studied to discover the possibilities and benefits of providing solutions by using provenance data. A multifunctional architecture is presented which addresses the workflow and service issues by exploiting provenance data. These challenges include workflow composition, abstract workflow selection, refinement, evaluation, and graph model extraction. The specific contribution of the proposed architecture is its novelty in providing a basis for taking advantage of the previous execution details of services and workflows along with artificial intelligence and knowledge management techniques to resolve the major challenges regarding workflows. The presented architecture is application-independent and could be deployed in any area. The requirements for such an architecture along with its building components are discussed. Furthermore, the responsibility of the components, related works and the implementation details of the architecture along with each component are presented

FORMAL ANALYSIS OF WEB SERVICE COMPOSITION

Ph.DDOCTOR OF PHILOSOPH

Verification and Analysis of Web Service Composition

Ph.DDOCTOR OF PHILOSOPH