A Genetic Algorithms-Based Approach for Optimized Self-protection in a Pervasive Service Middleware
ICSOC-ServiceWave '09 Proceedings of the 7th International Joint Conference on Service-Oriented Computing
QoS-Aware Self-adaptation of Communication Protocols in a Pervasive Service Middleware
GREENCOM-CPSCOM '10 Proceedings of the 2010 IEEE/ACM Int'l Conference on Green Computing and Communications & Int'l Conference on Cyber, Physical and Social Computing
Service oriented middleware for the internet of things: a perspective
ServiceWave'11 Proceedings of the 4th European conference on Towards a service-based internet
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Planning (for example choosing most suitable servicesfor self-configuration) is one important task in selfmanagement for pervasive service computing, and can bereduced to the problem of multi-objective services selectionwith constraints. Genetic algorithms (GAs) are effectivein solving such multi-objective optimization problems, andare one of the most successful computational intelligenceapproaches currently available. GAs are beginning to beused in planning for self-management, but there is a lack ofcomprehensive work that evaluates GAs performance andsolution quality, and guides the setting of GAs’ parameters.This situation makes the application of GAs difficultin the pervasive service computing domain in which performance may be critical and the settings of parameters may have big consequences for performance. In this paper, wewill present our evaluations of two GAs, namely NSGA-IIand MOCell, in the GA framework JMetal2.1, for achievingmulti-objective selection of available services. From theseevaluations, suggestions on how and when to use NSGA-IIand MOCell are given in the planning for self-management.Our experiences show that to get a true Pareto front for aproblem, combining solutions set from different GAs is abetter way than using a single GA.