Fuzzy logic based qos optimization mechanism for service composition

Increase emphasis on Quality of Service and highly changing environments make management of composite services a time consuming and complicated task. Adaptation approaches aim to mitigate the management problem by adjusting composite services to the environment conditions, maintaining functional and quality levels, and reducing human intervention. This paper presents an adaptation approach that implements self-optimization based on fuzzy logic. The proposed optimization model performs service selection based on the analysis of historical and real QoS data, gathered at different stages during the execution of composite services. The use of fuzzy inference systems enables the evaluation of the measured QoS values, helps deciding whether adaptation is needed or not, and how to perform service selection. Experimental results show significant improvements in the global QoS of the use case scenario, providing reductions up to 20.5% in response time, 33.4% in cost and 31.2% in energy consumption

ASG - Techniques of Adaptivity

The introduction of service-orientation leads to significant improvements regarding flexibility in the choice of business partners and IT-systems. This requires an increased adaptability of enterprise software landscapes as the environment is more dynamic than the ones in traditional approaches. In this paper we present different types of adaptation scenarios for service compositions and their implementation in a service provision platform. Based on experiences from the Adaptive Services Grid (ASG) project, we show how dynamic adaptation strategies are able to support an automated selection, composition and binding of services during run-time

Non-functional Data Collection for Adaptive Business Processes and Decision Making

International audienceMonitoring application services becomes more and more a transverse key activity in SOA. Beyond traditional human system administration and load control, new activities such as autonomic management as well as SLA enforcement raise the stakes over monitoring requirements. In this paper, we address a new monitoring-based activity which is selecting among competitive service offers based on their currently measured QoS. Starting from this use case, the late binding of service calls in SOA given the current QoS of a set of candidate services, we first elicit the requirements and then describe M4ABP (Monitoring for Adaptive Business Process), a middleware component for monitoring services and delivering monitoring data to business processes wishing to call them. M4ABP provides solutions for general requirements: flexibility as well as performance in data access for clients, coherency of data sets and network usage optimization. Lessons learned from this first use case can be applied to similar monitoring scenario, as well as to the larger field of context-aware computing

Web Services Support for Dynamic Business Process Outsourcing

Outsourcing of business processes is crucial for organizations to be effective, efficient and flexible. To meet fast-changing market conditions, dynamic outsourcing is required, in which business relationships are established and enacted on-the-fly in an adaptive, fine-grained way unrestricted by geographic distance. This requires automated means for both the establishment of outsourcing relationships and for the enactment of services performed in these relationships over electronic channels. Due to wide industry support and the underlying model of loose coupling of services, Web services increasingly become the mechanism of choice to connect organizations across organizational boundaries. This paper analyzes to which extent Web services support the dynamic process outsourcing paradigm. We discuss contract -based dynamic business process outsourcing to define requirements and then introduce the Web services framework. Based on this, we investigate the match between the two. We observe that the Web services framework requires further support for cross - organizational business processes and mechanisms for contracting, QoS management and process-based transaction support and suggest ways to fill those gaps

An adaptive approach for QoS-aware Web service composition

Web service composition is the process of integrating existing web services. It is a prospective method to build an application system. Current approaches, however, only take service function aspect into consideration. With the rapid growth of web service applications and the abundance of service providers, the consumer is facing the inevitability of selecting the maximum satisfied service providers due to the dynamic nature of web services. This requirement brings us some research challenges including a web service quality model, to design a web service framework able to monitor the service\u27s real time quality. A further challenge is to find an algorithm that can handle extensible service quality parameters and has good performance to solve NP-hard web services global selection problem. In this thesis, we propose a web service framework, using an extensible service quality model. A Cultural Algorithm is adopted to accelerate service global selection. We also provide experimental results comparing between Cultural Algorithm with Genetic Algorithm and Random service selection

Self-supervising BPEL Processes

Service compositions suffer changes in their partner services. Even if the composition does not change, its behavior may evolve over time and become incorrect. Such changes cannot be fully foreseen through prerelease validation, but impose a shift in the quality assessment activities. Provided functionality and quality of service must be continuously probed while the application executes, and the application itself must be able to take corrective actions to preserve its dependability and robustness. We propose the idea of self-supervising BPEL processes, that is, special-purpose compositions that assess their behavior and react through user-defined rules. Supervision consists of monitoring and recovery. The former checks the system's execution to see whether everything is proceeding as planned, while the latter attempts to fix any anomalies. The paper introduces two languages for defining monitoring and recovery and explains how to use them to enrich BPEL processes with self-supervision capabilities. Supervision is treated as a cross-cutting concern that is only blended at runtime, allowing different stakeholders to adopt different strategies with no impact on the actual business logic. The paper also presents a supervision-aware runtime framework for executing the enriched processes, and briefly discusses the results of in-lab experiments and of a first evaluation with industrial partners

QoS awareness and adaptation in service composition

The dynamic nature of a Web service execution environment generates frequent variations in the Quality of Service offered to the consumers, therefore, obtaining the expected results while running a composite service is not guaranteed. When combining this highly changing environment with the increasing emphasis on Quality of Service, management of composite services turns into a time consuming and complicated task. Different approaches and tools have been proposed to mitigate the impacts of unexpected events during the execution of composite services. Among them, self-adaptive proposals have stood out, since they aim to maintain functional and quality levels, by dynamically adapting composite services to the environment conditions, reducing human intervention. The research presented in this Thesis is centred on self-adaptive properties in service composition, mainly focused on self-optimization. Three models have been proposed to target self-optimization, considering various QoS parameters, the benefit of performing adaptation, and looking at adaptation from two perspectives: reactive and proactive. They target situations where the QoS of the composition is decreasing. Also, they consider situations where a number of the accumulated QoS values, in certain point of the process, are better than expected, providing the possibility of improving other QoS parameters. These approaches have been implemented in service composition frameworks and evaluated through the execution of test cases. Evaluation was performed by comparing the QoS values gathered from multiple executions of composite services, using the proposed optimization models and a non-adaptive approach. The benefit of adaptation was found a useful value during the decision making process, in order to determine if adaptation was needed or not. Results show that using optimization mechanisms when executing composite services provide significant improvements in the global QoS values of the compositions. Nevertheless, in some cases there is a trade-off, where one of the measured parameters shows an increment, in order to improve the others

Flexible provisioning of Web service workflows

Web services promise to revolutionise the way computational resources and business processes are offered and invoked in open, distributed systems, such as the Internet. These services are described using machine-readable meta-data, which enables consumer applications to automatically discover and provision suitable services for their workflows at run-time. However, current approaches have typically assumed service descriptions are accurate and deterministic, and so have neglected to account for the fact that services in these open systems are inherently unreliable and uncertain. Specifically, network failures, software bugs and competition for services may regularly lead to execution delays or even service failures. To address this problem, the process of provisioning services needs to be performed in a more flexible manner than has so far been considered, in order to proactively deal with failures and to recover workflows that have partially failed. To this end, we devise and present a heuristic strategy that varies the provisioning of services according to their predicted performance. Using simulation, we then benchmark our algorithm and show that it leads to a 700% improvement in average utility, while successfully completing up to eight times as many workflows as approaches that do not consider service failures