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Time stamp based Stateful Throttled VM Load Balancing Algorithm for the Cloud

Affiliations

  • School of Engineering and Technology, Ansal University, Gurgaon - 122003, Haryana, India

Abstract


Objectives: One of the essential requirements for improving Quality of Service of Cloud is to design load balancing algorithms that equally spread the load among the VMs such that neither of them is overloaded at any given point of time while ensuring that each of the VM is optimally utilised. The objective of this paper is to propose an efficient VM load balancing policy called Timestamp based Stateful Throttled VM load balancing algorithm. Methods/Statistical Analysis: The proposed algorithm deals with the space performance constraints of the existing Stateful Throttled VM Load balancing algorithm. It is based on the principle of locality of reference and deletes the old state information to reclaim space. The authors have carried out an experimental analysis to compare the proposed algorithm with the existing algorithms. Findings: The comparative analysis shows that the algorithm behaves in the same manner as Stateful Throttled Algorithm while taking into consideration its space performance limitations. The authors have observed better performance in terms of response time and data center processing time for Timestamp based Stateful Throttled VM load balancing algorithm than Throttled VM load balancing algorithm in case of spatially distributed VMs. Application/Improvements: The salient features of the proposed algorithm are better response time and data center processing time in case of spatially distributed data centers in cloud.

Keywords

Cloud Computing, Data Center, Load Balancing, Virtualization, Virtual Machine.

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References


  • Mahajan K, Makroo A, Dahiya D. Round robin with server affinity: A VM load balancing algorithm for cloud based infrastructure. Journal of Information Processing Systems.2013; 9(3):379–94.
  • Foster I, Zhao Y, Raicu I, Lu S. Cloud computing and grid computing 360-degree compared. In Grid Computing Environments Workshop; Austin, TX. 2008. p. 1–10.
  • Armbrust M, Fox A, Griffith R, Joseph AD, Katz R, Konwinski A, Lee G, Patterson D, Rabkin A, Stoica I, Zaharia M. A view of cloud computing. Communications of the ACM. 2010 Apr 1; 53(4):50–8.
  • Makroo A, Dahiya D. A systematic approach to deal with noisy neighbour in cloud infrastructure. Indian Journal of Science and Technology. 2016 May 31; 9(19):1–9.
  • Shen Z, Subbiah S, Gu X, Wilkes J. Cloudscale: Elastic resource scaling for multi-tenant cloud systems. Proceedings of the 2nd ACM Symposium on Cloud Computing ACM; USA. 2011 Oct 26. p. 5.
  • Zhang Q, Cheng L, Boutaba R. Cloud computing: State-oftheart and research challenges. Journal of Internet Services and Applications. 2010 May 1; 1(1):7–18.
  • Mahajan K, Dahiya D. A cloud based deployment framework for load balancing policies. IEEE 7th International Conference on Contemporary Computing (IC3); Noida.2014 Aug 7. p. 565–70.
  • Girbea A, Suciu C, Nechifor S, Sisak F. Design and implementation of a service-oriented architecture for the optimization of industrial applications. IEEE Transactions on Industrial Informatics. 2014 Feb; 10(1):185–96.
  • Buyya R, Yeo CS, Venugopal S. Market-oriented cloud computing: Vision, hype, and reality for delivering it services as computing utilities. 10th IEEE International Conference on High Performance Computing and Communications, HPCC’08; Dalian. 2008 Sep 25. p. 5–13.
  • Armbrust M, Fox A, Griffith R, Joseph AD, Katz RH, Konwinski A, Lee G, Patterson DA, Rabkin A, Stoica I, Zaharia M. Above the clouds: A berkeley view of cloud computing [Technical report]. University of California; 2009. p. 1–12.
  • Nuaimi AK, Mohamed N, Nuaimi AM, Al-Jaroodi J. A survey of load balancing in cloud computing: Challenges and algorithms. IEEE 2nd Symposium on Network Cloud Computing and Applications (NCCA); London. 2012 Dec 3. p. 137–42.
  • Lee R, Jeng B. Load-balancing tactics in cloud. IEEE International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC); Beijing. 2011 Oct 10. p. 447–54.
  • Wang L, Von Laszewski G, Younge A, He X, Kunze M, Tao J, Fu C. Cloud computing: a perspective study. New Generation Computing. 2010 Apr 1, 28(2), pp. 137-46.
  • Wu MY, Shu W, Zhang H. Segmented min-min: A static mapping algorithm for meta-tasks on heterogeneous computing systems. Heterogeneous Computing Workshop;Cancun. 2000 May 1. p. 375–85.
  • Kliazovich D, Bouvry P, Khan SU. Green Cloud: A packetlevel simulator of energy-aware cloud computing data centers. The Journal of Supercomputing. 2012 Dec; 162(3):1263–83.
  • Garg SK, Buyya R. Networkcloudsim: Modelling parallel applications in cloud simulations. IEEE 4th Utility and Cloud Computing (UCC); Victoria, NSW. 2011. p. 105–13.
  • Garg SK. NetworkCloudSim: Modelling parallel applications in cloud simulations International Conference on IEEE; Victoria, NSW. 2011 Dec 5. p. 105–13.

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