DocumentCode :
1851247
Title :
Let the Ants Deploy Your Software - An ACO Based Deployment Optimisation Strategy
Author :
Aleti, Aldeida ; Grunske, Lars ; Meedeniya, Indika ; Moser, Irene
Author_Institution :
Fac. of ICT, Swinburne Univ. of Technol., Hawthorn, VIC, Australia
fYear :
2009
fDate :
16-20 Nov. 2009
Firstpage :
505
Lastpage :
509
Abstract :
Decisions regarding the mapping of software components to hardware nodes affect the quality of the resulting system. Making these decisions is hard when considering the ever-growing complexity of the search space, as well as conflicting objectives and constraints. An automation of the solution space exploration would help not only to make better decisions but also to reduce the time of this process. In this paper, we propose to employ Ant Colony Optmisation (ACO) as a multi-objective optimisation strategy. The constructive approach is compared to an iterative optimisation procedure - a Genetic Algorithm (GA) adaptation - and was observed to perform suprisingly similar, although not quite on a par with the GA, when validated based on a series of experiments.
Keywords :
genetic algorithms; ant colony optmisation; deployment optimisation strategy; genetic algorithm adaptation; multi-objective optimisation; solution space exploration; Ant colony optimization; Design engineering; Genetic algorithms; Hardware; Iterative methods; Reliability engineering; Safety; Software algorithms; Software quality; Space exploration; Ant Colony Optimisation; Component Deployment;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Automated Software Engineering, 2009. ASE '09. 24th IEEE/ACM International Conference on
Conference_Location :
Auckland
ISSN :
1938-4300
Print_ISBN :
978-1-4244-5259-0
Electronic_ISBN :
1938-4300
Type :
conf
DOI :
10.1109/ASE.2009.59
Filename :
5431744
Link To Document :
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