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
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