Journal
IET COMMUNICATIONS
Volume 11, Issue 2, Pages 161-167Publisher
INST ENGINEERING TECHNOLOGY-IET
DOI: 10.1049/iet-com.2016.0417
Keywords
cloud computing; software reliability; scheduling; task analysis; resource allocation; computational complexity; genetic algorithms; Markov processes; optimisation; cloud service reliability modelling; optimal task scheduling; cloud computing; service sharing; network access; configurable computing resources; heterogeneous environment; resource allocation; NP-hard problem; reliability analysis; Markov-based method; cloud scheduling problem; multiobjective optimisation problem; genetic algorithm-based chaotic ant swarm algorithm; GA-CAS algorithm
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Funding
- National Natural Science Foundation of China [61201153]
- National 973 Program of China [2012CB315805]
- National Key Science and Technology Projects [2010ZX03004-002-02]
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Cloud computing enables service sharing in a massive scale via network access to a pool of configurable computing resources. It has to allocate resources adaptively for tasks and applications to be executed effectively and reliably in a large scale, highly heterogeneous environment. Resource allocation in cloud computing is an NP-hard problem. In this study, the authors conduct a reliability analysis of cloud services by applying a Markov-based method. They formulate the cloud scheduling problem as a multi-objective optimisation problem with constraints in terms of reliability, makespan, and flowtime. Furthermore, they propose a genetic algorithm-based chaotic ant swarm (GA-CAS) algorithm, in which four operators and natural selection are applied, to solve this constrained multi-objective optimisation problem. Simulation results have demonstrated that GA-CAS generally speeds up convergence and outperforms other meta-heuristic approaches.
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