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A method for the optimum selection of datacenters in geographically distributed clouds

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Abstract

The optimal selection of a datacenter is one of the most important challenges in the structure of a network for the wide distribution of resources in the environment of a geographically distributed cloud. This is due to the variety of datacenters with different quality-of-service (QoS) attributes. The user’s requests and the conditions of the service-level agreements (SLAs) should be considered in the selection of datacenters. In terms of the frequency of datacenters and the range of QoS attributes, the selection of the optimal datacenter is an NP-hard problem. A method is therefore required that can suggest the best datacenter, based on the user’s request and SLAs. Various attributes are considered in the SLA; in the current research, the focus is on the four important attributes of cost, response time, availability, and reliability. In a geo-distributed cloud environment, the nearest datacenter should be suggested after receiving the user’s request, and according to its conditions, SLA violations can be minimized. In the approach proposed here, datacenters are clustered according to these four important attributes, so that the user can access these quickly based on specific need. In addition, in this method, cost and response time are taken as negative criteria, while accessibility and reliability are taken as positive, and the multi-objective NSGA-II algorithm is used for the selection of the optimal datacenter according to these positive and negative attributes. In this paper, the proposed method, known as NSGAII_Cluster, is implemented with the Random, Greedy and MOPSO algorithms; the extent of SLA violation of each of the above-mentioned attributes are compared using four methods. The simulation results indicate that compared to the Random, Greedy and MOPSO methods, the proposed approach has fewer SLA violations in terms of the cost, response time, availability, and reliability of the selected datacenters.

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Notes

  1. Local optimal pairwise interchange.

  2. Simulated annealing.

  3. Phase-connected component-based recursive split.

  4. Greedy heuristic.

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Acknowledgements

Authors would thank University of Kashan to support of this study by Grant # 577242.

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  1. Department of Computer, University of Kashan, Kashan, Iran

    Hassan Ziafat & Seyed Morteza Babamir

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  1. Hassan Ziafat
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  2. Seyed Morteza Babamir
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Correspondence to Seyed Morteza Babamir.

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Ziafat, H., Babamir, S.M. A method for the optimum selection of datacenters in geographically distributed clouds. J Supercomput 73, 4042–4081 (2017). https://doi.org/10.1007/s11227-017-1999-5

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