Efficient Multi-Objective Optimisation of Service Compositions in Mobile Ad hoc Networks Using Lightweight Surrogate Models

International audienceInfrastructure-less Mobile Ad hoc NETworks (MANETs) and Service-Oriented Architecture (SOA) enable the development of pervasive applications. Based on SOA, we can abstract devices' resources as software services which can be combined into value-added composite services providing complex functionalities while exhibiting specified QoS properties. Configuring compositions with optimal QoS is challenging due to dynamic network topologies and availability of resources. Existing approaches seek to optimise the selection of which services to participate in a centralised orchestration without considering the overhead for estimating their combined QoS. QoS metrics can be used as fitness functions to guide the search for optimal compositions. When com-posing services offered by diverse devices, there is no trivial relationship between the composition's QoS and its component services. Measuring the fitness values of a candi-date composition could be done either by monitoring its actual invocation or simulating it. However, both approaches are too expensive to be used within an optimisation pro-cess. In this paper, we propose a surrogate-based multi-objective optimisation approach for exploring trade-off compositions. The evaluation results show that by replacing the expensive fitness functions with lightweight surrogate models, we can vastly accelerate the optimisation algorithm while producing trade-off solutions of high quality

Efficient Multi-Objective Optimisation of Service Compositions in Mobile Ad hoc Networks Using Lightweight Surrogate Models

International audienceInfrastructure-less Mobile Ad hoc NETworks (MANETs) and Service-Oriented Architecture (SOA) enable the development of pervasive applications. Based on SOA, we can abstract devices' resources as software services which can be combined into value-added composite services providing complex functionalities while exhibiting specified QoS properties. Configuring compositions with optimal QoS is challenging due to dynamic network topologies and availability of resources. Existing approaches seek to optimise the selection of which services to participate in a centralised orchestration without considering the overhead for estimating their combined QoS. QoS metrics can be used as fitness functions to guide the search for optimal compositions. When com-posing services offered by diverse devices, there is no trivial relationship between the composition's QoS and its component services. Measuring the fitness values of a candi-date composition could be done either by monitoring its actual invocation or simulating it. However, both approaches are too expensive to be used within an optimisation pro-cess. In this paper, we propose a surrogate-based multi-objective optimisation approach for exploring trade-off compositions. The evaluation results show that by replacing the expensive fitness functions with lightweight surrogate models, we can vastly accelerate the optimisation algorithm while producing trade-off solutions of high quality