A comparative review of meta-heuristic approaches to optimize the SLA violation costs for dynamic execution of cloud services

Abstract

This paper presents comparative analysis results of research work done using the five most popular meta-heuristic techniques to optimize the service-level agreement (SLA) violation cost in cloud computing. The meta-heuristic algorithms have the ability to handle multifarious types of constraints and offer better results. The Quality of Service criteria, SLA penalty cost and the cloud-domain-specific constraints have been mathematically formulated in this paper. The sole motivation of this paper is that the constraints of feasible domain must be satisfied and the profit of cloud service provider should be maximized. An effort has been made to experimentally demonstrate the comparative performance of five meta-heuristic algorithms, namely Ant Colony Optimization, Particle Swarm Optimization, Genetic Algorithm, Gray Wolf Optimizer and Harmony Search. Eleven test benchmark functions have been applied to demonstrate the efficiency and performance. The best solutions of each meta-heuristic technique have been reported in four performance metric cases: worst, best, average and standard deviation.

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Correspondence to Ajay Kumar.

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Kumar, A., Bawa, S. A comparative review of meta-heuristic approaches to optimize the SLA violation costs for dynamic execution of cloud services. Soft Comput 24, 3909–3922 (2020). https://doi.org/10.1007/s00500-019-04155-4

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Keywords

  • Cloud computing
  • Service-level agreement
  • Meta-heuristic algorithms
  • Resource optimization