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

Surrogate-Assisted Optimisation of Composite Applications in Mobile Ad hoc Networks

International audienceInfrastructure-less mobile ad hoc networks enable the development of collaborative pervasive applications. Within such dynamic networks, collaboration between devices can be realised through service-orientation by abstracting device resources as services. Recently, a framework for QoS-aware service composition has been introduced which takes into account a spectrum of orchestration patterns, and enables compositions of a better QoS than traditional centralised orchestration approaches. In this paper, we focus on the automated exploration of trade-off compositions within the search space de fined by this flexible composition model. For the studied problem, the evaluation of the fi tness functions guiding the search process is computationally expensive because it either involves a high- fidelity simulation or actually requires calling the composite service. To overcome this limitation, we have developed e fficient surrogate models for estimating the QoS metrics of a candidate solution during the search. Our experimental results show that the use of surrogates can produce solutions with good convergence and diversity properties at a much lower computational e ffort

MDEoptimiser

Model Driven Engineering (MDE) is a methodology that aims tosimplify the process of designing complex systems, by using modelsas an abstract representation of the underlying system.This methodology allows domain experts to more easily focus onsystem design, where their knowledge is more useful, without having to work with the system implementation complexities. SearchBased Model Engineering applies MDE concepts to optimisationproblems. The goal is to simplify the process of solving optimisation problems for domain experts, by abstracting the complexity ofsolving optimisation problems and allowing them to focus on thedomain level issues.In this tool demostration we present MDEOptimiser (MDEO), atool for specifying and solving optimisation problems using MDE.With MDEO the user can specify optimisation problems using asimple DSL. The tool can run evolutionary optimisation algorithmsthat use models as an encoding for population members and modeltransformations as search operators. We showcase the functionalityof the tool using a number of case studies. We aim to show that withMDEO, specifying optimisation problems becomes a less complextask compared to custom implementations