Optimizing cloud provisioning for scientific workflow applications is a challenging problem, since the workflows generally contain dependency between tasks and require specific deadlines. Usually, cloud providers offer many options to the consumers. These options include the number of virtual machines, the type of each virtual machine and the purchasing method for each machine. Currently, cloud provisioning cost optimization is an active research topic. Most of this literature is concerned with task scheduling, cloud option selection, and cloud option selection for scientific workflow applications. However, research that attempts to find solutions which cover both cloud option selection and workflow task scheduling is very limited. In this paper, we focus on optimizing the cost of purchasing infrastructure-as-a-service cloud capabilities to achieve scientific work flow execution within the specific deadlines. The proposed system considers the number of purchased instances, instance types, purchasing options, and task scheduling as constraints in an optimization process. Particle swarm optimization augmented with a variable neighborhood search technique is used to find the optimal solution. Our approach finds the configurations of purchasing options with the optimum budget for a specified workflow application based on the required performance. The solutions from the proposed system show promising performance from the perspectives of the total cost and fitness convergence when compared with other state-of-the-art algorithms.
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This work was supported by the Thailand Research Fund through the Royal Golden Jubilee PhD Program (Grant No. PHD/0031/2553). The authors also acknowledge National e-Science Infrastructure Consortium for providing computing resources that have contributed to the research results reported in this paper (http://www.e-science.in.th).
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Netjinda, N., Sirinaovakul, B. & Achalakul, T. Cost optimal scheduling in IaaS for dependent workload with particle swarm optimization. J Supercomput 68, 1579–1603 (2014). https://doi.org/10.1007/s11227-014-1126-9
- Cloud computing
- Cost optimization
- Particle swarm optimization
- Workflow scheduling
- Deadline constraint
- Variable neighborhood search