Improving the Quality of Distributed Composite Service Applications

Dynamic service composition promotes the on-the-fly creation of value-added applications by combining services. Large scale, dynamic distributed applications, like those in the pervasive computing domain, pose many obstacles to service composition such as mobility, and resource availability. In such environments, a huge number of possible composition configurations may provide the same functionality, but only some of those may exhibit the desirable non-functional qualities (e.g. low battery consumption and response time) or satisfy users\u27 preferences and constraints. The goal of a service composition optimiser is to scan the possible composition plans to detect these that are optimal in some sense (e.g. maximise availability or minimise data latency) with acceptable performance (e.g. relatively fast for the application domain). However, the majority of the proposed optimisation approaches for finding optimal composition plans, examine only the Quality of Service of each participated service in isolation without studying how the services are composed together within the composition. We argue that the consideration of multiple factors when searching for the optimal composition plans, such as which services are selected to participate in the composition, how these services are coordinated, communicate and interact within a composition, may improve the end-to-end quality of composite applications

An intelligent, time-optimized monitoring scheme for edge nodes

Monitoring activities over edge resources and services are essential in today's applications. Edge nodes can monitor their status and end users/applications requirements to identify their ‘matching’ and deliver alerts when violations are present. Violations are related to any disturbance of the desired Quality of Service (QoS). QoS depends on a number of performance metrics and can differ among applications. In this paper, we propose the use of an intelligent mechanism to be incorporated in monitoring tools adopted by edge nodes. The proposed mechanism observes the realizations of performance parameters that result in specific QoS values and decides when it is the right time to ‘fire’ mitigation actions. Hence, edge nodes are capable of changing their configuration to secure the desired QoS levels as dictated by end users/applications requirements. In our work, a mitigation action could involve either upgrades in the current services/resources or offloading tasks by transferring computational load and data to peer nodes or the Cloud. We present our model and provide formulations for the solution of the problem. A high number of simulations reveal the performance of the proposed mechanism. Our experiments show that our scheme outperforms any deterministic model defined for the discussed setting as well as other efforts found in the relevant literature

Automatic Dynamic Web Service Composition: A Survey and Problem Formalization

The aim of Web service composition is to arrange multiple services into workflows supplying complex user needs. Due to the huge amount of Web services and the need to supply dynamically varying user goals, it is necessary to perform the composition automatically. The objective of this article is to overview the issues of automatic dynamic Web service composition. We discuss the issues related to the semantics of services, which is important for automatic Web service composition. We propose a problem formalization contributing to the formal definition of the pre-/post-conditions, with possible value restrictions, and their relation to the semantics of services. We also provide an overview of several existing approaches dealing with the problem of Web service composition and discuss the current achievements in the field and depict some open research areas

Metaheuristic Optimization of Large-Scale QoS-Aware Service compositions

We present an optimization approach for service compositions in large-scale service-oriented systems that are subject to Quality of Service (QoS) constraints. In particular, we leverage a composition model that allows a flexible specification of QoS constraints by using constraint hierarchies. We propose an extensible metaheuristic framework for optimizing such compositions. It provides coherent implementation of common metaheuristic functionalities, such as the objective function, improved mutation or neighbor generation. We implement three metaheuristic algorithms that leverage these improved operations. The experiments show the efficiency of these implementations and the improved convergence behavior compared to purely randomized metaheuristic operators

Metaheuristic optimization of large-scale QoS-aware service compositions

We present an optimization approach for service compositions in large-scale service-oriented systems that are subject to Quality of Service (QoS) constraints. In particular, we leverage a composition model that allows a flexible specification of QoS constraints by using constraint hierarchies. We propose an extensible metaheuristic framework for optimizing such compositions. It provides coherent implementation of common metaheuristic functionalities, such as the objective function, improved mutation or neighbor generation. We implement three metaheuristic algorithms that leverage these improved operations. The experiments show the efficiency of these implementations and the improved convergence behavior compared to purely randomized metaheuristic operators