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QoS Based Cloud Service Selection to Handle Large Volume of Concurrent Requests


  • Department of Computer Science and Engineering, Srinivas School of Engineering, Mukka, Mangaluru - 575025, India
  • Department of Computer Science and Engineering, Canara Engineering College, Mangaluru, India


Background/Objectives: With the exponential growth of cloud providers and users, the possibility of arrival of large number of concurrent requests within a small interval, may reduce the performance of broker. This paper presents a cloud service selection mechanism with service grouping. The main objective of this paper to show the performance difference between the selection mechanism with grouping and without grouping. Methods/Statistical Analysis: For the concurrent services groups and sub groups are created using functional and QoS parameters. Analytical hierarchy based selection algorithm is proposed. Performance of selection algorithm with grouping is evaluated. Performance of selection algorithm with grouping is compared with algorithm without grouping and the sufficient reduction in execution time is noted. Findings: AHP based selection algorithm with grouping improves the performance by reducing the computation time and reducing the repetitive repository access. Applications/Improvements: The limitation of AHP based algorithm is the rank reversal problem. The improved AHP based algorithm can be used to eliminate the rank reversal problem.


AHP Algorithm, Concurrent Request, Cloud Service, Quality of Service, Service Selection.

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  • Amazon EC2 - Virtual server hosting [Internet]. [cited 2015 Mar 12]. Available from:
  • Zheng Z, Wu X, Zhang Y, Lyu MR, Wang J. QoS ranking prediction for cloud services. IEEE Transactions on Parallel and Distributed Systems. 2013; 24(6):1213–222.
  • Yu R, Yang X, Huangy J, Duanz Q, Ma Y, Tanaka Y. QoSaware service selection in virtualization-based cloud computing. 2012 Network Operations and Management Symposium (APNOMS), Seoul; 2012. p. 1–8.
  • Service measurement index framework version 2.1, CSMIC Carnegie Mellon University Silicon Valley: Moffett Field, CA USA; 2014.
  • Mahalingam SK, Sengottaiyan N. A QoS guaranteed selection of efficient cloud services. Indian Journal of Science and Technology. 2015 May; 8(S9):103–10 .
  • D’Mello DA, Ananthanarayana VS. A QoS broker based architecture for dynamic web service selection. Proceedings of the 2nd Asia International Conference on Modelling and Simulation (AMS), Kuala Lumpur; 2008. p. 101–6 .
  • Suchithra M, Ramakrishnan M. A survey on different web service discovery techniques. Indian Journal of Science and Technology. 2015 Jul; 8(15):1–5.
  • Shetty J, D’Mello DA. Repository design strategies and discovery techniques for cloud computing. 2013 Proceedings of IEEE International Conferenceon Green Computing, Communication and Conservation Energy, ICGCE’2013, Chennai; 2013. p. 761–6.
  • Shetty J, D’Mello DA. An XML based data representation model to discover infrastructure services. 2015 Proceedings of IEEE International Conference on Smart Technologies and Management for Computing, Communication, Controls, Energy Materials, (ICSTM), Chennai, India; 2015. p. 119–25.
  • Saaty TL. Decision making with the analytic hierarchy process. International Journal of Services Sciences. 2008; 1(1):83–98.
  • Garg SK, Versteeg S, Buyya R. SMICloud: A framework for comparing and ranking cloud services. Proceedings of 2011 Fourth IEEE International Conference on Utility and Cloud Computing, (UCC), Victoria, NSW; 2011. p.210–18.
  • Chung BD, Kwang-Kyu S. A cloud service selection model based on analytic network process. Indian Journal of Science and Technology. 2015; 8(18):1–5.


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