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

TY - CHAP

T1 - Surrogate-Assisted Optimisation of Composite Applications in Mobile Ad-hoc Networks

AU - Efstathiou, Dionysios

AU - McBurney, Peter

AU - Zschaler, Steffen

AU - Bourcier, Johann

PY - 2014

Y1 - 2014

N2 - Infrastructure-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 defined by this flexible composition model. For the studied problem, the evaluation of the fitness 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 efficient 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 effort.

AB - Infrastructure-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 defined by this flexible composition model. For the studied problem, the evaluation of the fitness 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 efficient 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 effort.

U2 - 10.1145/2576768.2598307

DO - 10.1145/2576768.2598307

M3 - Conference paper

SN - 978-1-4503-2662-9

SP - 1239

EP - 1246

BT - Proc. 2014 Conference on Genetic and Evolutionary Computation (GECCO '14)

PB - ACM Digital Library

CY - New York

ER -