Renumber coevolutionary multiswarm particle swarm optimization for multi-objective workflow scheduling on cloud computing environment
- Authors
- Li, Hai-Hao; Chen, Zong-Gan; Zhan, Zhi-Hui; Du, Ke-Jing; Zhang, Jun
- Issue Date
- Jul-2015
- Publisher
- Association for Computing Machinery, Inc
- Keywords
- Cloud computing; Guiding point; Particle swarm optimization; Renumber; Scheduling
- Citation
- GECCO Companion '15: Proceedings of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation, pp 1419 - 1420
- Pages
- 2
- Indexed
- SCOPUS
- Journal Title
- GECCO Companion '15: Proceedings of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation
- Start Page
- 1419
- End Page
- 1420
- URI
- https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/115704
- DOI
- 10.1145/2739482.2764632
- Abstract
- Resources scheduling is a significant research topic in cloud computing, which is often modeled as a cost-minimization and deadline-constrained workflow scheduling model. This is a constrained single objective problem that to minimize the overall workflow execution cost while meeting deadline constraints. In this paper, we offer a new horizon to convert this single-objective problem to a multi-objective problem and present coevolutionary multiswarm particle swarm optimization (CMPSO) to find the non-dominated solutions with different execute cost and time. Meanwhile, the renumber strategy is adopted in CMPSO to make the learning efficient. CMPSO is compared with a renumber PSO (RNPSO) by setting the execute time in the CMPSO's nondominated solutions as the deadline constraint of RNPSO. Results show that CMPSO not only offers many non-dominated solutions with different prices and execute time, but also obtains better solution than RNPSO under a same deadline. Copyright is held by the owner/author(s).
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