Multi-Swarm Co-Evolution Based Hybrid Intelligent Optimization for Bi-Objective Multi-Workflow Scheduling in the Cloud

Many scientific applications can be well modelled as large-scale workflows. Cloud computing has become a suitable platform for hosting and executing them. Workflow scheduling has gained much attention in recent years. However, since cloud service providers must offer services for multiple users with...

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Bibliographic Details
Published in:IEEE transactions on parallel and distributed systems 2022-09, Vol.33 (9), p.2183-2197
Main Authors: Li, Huifang, Wang, Danjing, Zhou, MengChu, Fan, Yushun, Xia, Yuanqing
Format: Article
Language:English
Subjects:
Cloud computing
Costs
Dynamic scheduling
Evolutionary algorithms
Genetic algorithms
Heuristic algorithms
Hybrid systems
intelligent optimization
multi-swarm
multiple workflows
Optimization
Quality of service
Quality of Service (QoS)
Scheduling
Simulated annealing
Task analysis
Workflow
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Summary:Many scientific applications can be well modelled as large-scale workflows. Cloud computing has become a suitable platform for hosting and executing them. Workflow scheduling has gained much attention in recent years. However, since cloud service providers must offer services for multiple users with various QoS demands, scheduling multiple applications with different QoS requirements is highly challenging. This work proposes a Multi-swarm Co-evolution-based Hybrid Intelligent Optimization (MCHO) algorithm for multiple-workflow scheduling to minimize total makespan and cost while meeting the deadline constraint of each workflow. First, we design a multi-swarm co-evolutionary mechanism where three swarms are adopted to sufficiently search for various elite solutions. Second, to improve global search and convergence performance, we embed local and global guiding information into the updating process of a Particle Swarm Optimizer, and develop a swarm cooperation technique. Third, we propose a Genetic Algorithm-based elite enhancement strategy to exploit more non-dominated individuals, and apply the Metropolis Acceptance rule of Simulated Annealing to update the local guiding solution for each swarm so as to prevent it from being stuck into a local optimum at an early stage. Extensive experimental results demonstrate that MCHO outperforms the state-of-art scheduling algorithms with better distributed non-dominated solutions.
ISSN:1045-9219
1558-2183
DOI:10.1109/TPDS.2021.3122428