NDLI: Renumber Coevolutionary Multiswarm Particle Swarm Optimization for Multi-objective Workflow Scheduling on Cloud Computing Environment

Content Provider	ACM Digital Library
Author	Chen, Zong-Gan Zhan, Zhi-Hui Du, Ke-Jing Zhang, Jun Li, Hai-Hao
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 non-dominated 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.
Starting Page	1419
Ending Page	1420
Page Count	2
File Format	PDF
ISBN	9781450334884
DOI	10.1145/2739482.2764632
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2015-07-11
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Cloud computing Renumber Scheduling Guiding point Particle swarm optimization
Content Type	Text
Resource Type	Article

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Renumber Coevolutionary Multiswarm Particle Swarm Optimization for Multi-objective Workflow Scheduling on Cloud Computing Environment