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Makespan-Driven Workflow Scheduling in Clouds Using Immune-Based PSO Algorithm
- Source :
- IEEE Access, Vol 8, Pp 29281-29290 (2020)
- Publication Year :
- 2020
- Publisher :
- IEEE, 2020.
-
Abstract
- Cloud Computing is becoming more and more popular for solving problems that need high concurrency and a lot of resources. Many traditional areas of research choose to solve their problems through the cloud, and workflow scheduling is one of them. Cloud computing brings many benefits, meanwhile, due to the almost “infinite” amount of resources for users, it also brings new challenges for scheduling and optimization, in which cost and makespan are the most concerned issues for workflow scheduling. Users want to obtain a low cost and fast makespan solution. This paper focuses on how to find an optimized solution to achieve better cost-makespan at the same time under the constraint of deadline. In order to solve this problem, an immune particle swarm optimization algorithm (IMPSO) is proposed, which effectively improves the quality and speed of the optimization. The proposed IMPSO overcomes the problem of slow convergence of PSO, which is easy to fall into local optimization. Experiments show the efficiency and effectiveness of the proposed approach.
- Subjects :
- 020203 distributed computing
Mathematical optimization
particle swarm algorithm
General Computer Science
Job shop scheduling
Computer science
business.industry
Concurrency
General Engineering
Particle swarm optimization
Cloud computing
02 engineering and technology
Scheduling (computing)
immune mechanism
0202 electrical engineering, electronic engineering, information engineering
Workflow scheduling
workflow scheduling
020201 artificial intelligence & image processing
General Materials Science
Local search (optimization)
lcsh:Electrical engineering. Electronics. Nuclear engineering
business
lcsh:TK1-9971
Subjects
Details
- Language :
- English
- ISSN :
- 21693536
- Volume :
- 8
- Database :
- OpenAIRE
- Journal :
- IEEE Access
- Accession number :
- edsair.doi.dedup.....105d6c822058fae3ee2d66927b8a72b1