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Virtual Machine Allocation in Heterogeneous Cloud for Load Balancing Based on Virtual Machine Classification

  • Badshaha MullaEmail author
  • C. Rama Krishna
  • Raj Kumar Tickoo
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 98)

Abstract

Many organizations are turning to cloud users because of the potential benefits of the cloud. The increasing popularity of cloud services has brought several difficulties as well. Balancing the workload among the available resources at cloud datacenter is one of them and becomes a crucial task. The cloud service provider needs an effective mechanism for achieving workload balance to meet the demands of large numbers of users. To overcome this, many different approaches are suggested in the literature. But still, there is scope to improve the performance of the heterogeneous cloud. The method of distribution of workload among resources needs to consider the processing capability of each resource. Here, in this work, we propose a method “VAHC (VM Allocation in Heterogeneous Cloud for Load Balancing Based on VM Classification)” for allocation of VM based on its classification. The median is used for effective classification of VMs into two groups based on their capacities. This work focuses on minimizing the response time and time required for processing the request in the heterogeneous cloud. The performance of this work is analyzed and compared with “Equally Spread Current Execution (ESCE)”, “Throttled”, and “Round Robin (RR)” Algorithms. The results of the proposed method showed a considerable reduction of 16% in response time whereas 29% in time required processing the request at the datacenter.

Keywords

VM Allocation VM Classification Load balancing Heterogeneous cloud CloudAnalyst 

References

  1. 1.
    Louis Columbus, LogicMonitor’s Cloud Vision 2020: The Future of the Cloud Study. https://www.forbes.com/sites/louiscolumbus/2018/01/07/83-of-enterprise-workloads-will-be-in-the-cloud-by-2020/#4ad96e796261
  2. 2.
    Furht, B., Escalante, A.: Handbook of Cloud Computing, vol. 63, no. 3, pp. 67–76. Springer, New York (2006)Google Scholar
  3. 3.
    Garg, A.: A literature review of various load balancing techniques in cloud computing environment. Big Data Anal. 654, 667–675 (2018)Google Scholar
  4. 4.
    Sridhar, S., Smys, S.: A hybrid multilevel authentication scheme for private cloud environment. In: 10th IEEE International Conference on Intelligent Systems and Control (ISCO), pp. 1–5 (2016)Google Scholar
  5. 5.
    Karthiban, K., Smys, S.: Privacy preserving approaches in cloud computing, In: 2nd IEEE International Conference on Inventive Systems and Control (ICISC), pp. 462–467 (2018)Google Scholar
  6. 6.
    Roy, S., Md Alam, H., Sen, S.K., Nazmul, H., Md Rashid, A.A.: Measuring the performance on load balancing algorithms. Glob. J. Comput. Sci. Technol. 19(1), 41–49 (2019)Google Scholar
  7. 7.
    Siddiqui, S., Darbari, M., Diwakar, Y.: A comprehensive study of challenges and issues in cloud computing. Soft Comput. Signal Process. 900, 325–344 (2019)Google Scholar
  8. 8.
    Mesbahi, M., Rahmani, A.M.: Load balancing in cloud computing: a state of the art survey. Int. J. Mod. Educ. Comput. Sci. 8(3), 64–78 (2016)Google Scholar
  9. 9.
    Kumar, M., Sharma, S.C., Goel, A., Singh, S.P.: A comprehensive survey for scheduling techniques in cloud computing. J. Netw. Comput. Appl. 143(1), 1–33 (2019)Google Scholar
  10. 10.
    Bhathiya, W., Buyya, R.: Cloudanalyst: a cloudsim-based tool for modelling and analysis of large scale cloud computing environments. MEDC Proj. Rep. 22(6), 433–659 (2009)Google Scholar
  11. 11.
    Bhathiya, W., Rodrigo, N.C., Buyya, R.: CloudAnalyst: a CloudSim-based visual modeller for analysing cloud computing environments and applications. In: IEEE International Conference on Advanced Information Networking and Applications, pp. 446–452 (2010)Google Scholar
  12. 12.
    Sui, X., Dan, L., Li, L., Huan, W., Hongwei, Y.: Virtual machine scheduling strategy based on machine learning algorithms for load balancing. EURASIP J. Wirel. Commun. Netw. 2019(1), 1–16 (2019)Google Scholar
  13. 13.
    Xu, M., Tian, W., Buyya, R.: A survey on load balancing algorithms for virtual machines placement in cloud computing. Concurr. Comput.: Pract. Exp. 29(12), 1–16 (2017)Google Scholar
  14. 14.
    Kumar, P., Kumar, K.: Issues and challenges of load balancing techniques in cloud computing: a survey. ACM Comput. Surv. 51(6), 1–35 (2019)Google Scholar
  15. 15.
    Mishra, N.K.: Load balancing techniques: need, objectives and major challenges in cloud computing-a systematic review. Int. J. Comput. Appl. 131(18), 975–8887 (2015)Google Scholar
  16. 16.
    Ghomi, E.J., Rahmani, A.M., Qader, N.N.: Load-balancing algorithms in cloud computing: a survey. J. Netw. Comput. Appl. 88, 50–71 (2017)Google Scholar
  17. 17.
    Shoja, H., Nahid, H., Azizi, R.: A comparative survey on load balancing algorithms in cloud computing. In: IEEE 5th International Conference on Computing, Communication and Networking Technologies (ICCCNT), pp. 1–5 (2014)Google Scholar
  18. 18.
    Shah, M.D.: allocation of virtual machines in cloud computing using load balancing algorithm. Int. J. Comput. Sci. Inf. Technol. Secur. (IJCSITS) 3(1), 93–95 (2013)Google Scholar
  19. 19.
    Yeboah, T., Odabi, I., Hiran, K.K.: An integration of round robin with shortest job first algorithm for cloud computing environment. Int. Conf. Manag. Commun. Technol. 3, 1–5 (2015)Google Scholar
  20. 20.
    Elmougy, S., Sarhan, S., Joundy, M.: A novel hybrid of shortest job first and round robin with dynamic variable quantum time task scheduling technique. J. Cloud Comput. 6(1), 1–12 (2017)Google Scholar
  21. 21.
    Singh, K., Mahaan, R.: Equally spread current execution load algorithm - a novel approach for improving data centre’s performance in cloud computing. Int. J. Future Revolut. Comput. Sci. Commun. Eng. 4(8), 8–10 (2018)Google Scholar
  22. 22.
    Lamba, S., Kumar, D.: A comparative study on load balancing algorithms with different service broker policies in cloud computing. Int. J. Comput. Sci. Inf. Technol. 5(4), 5671–5677 (2014)Google Scholar
  23. 23.
    Tyagi, V., Kumar, T.: ORT broker policy: reduce cost and response time using throttled load balancing algorithm. Procedia Comput. Sci. 48, 217–221 (2015)Google Scholar
  24. 24.
    Mesbahi, M.R., Hashemi, M., Rahmani, A.M.: Performance evaluation and analysis of load balancing algorithms in cloud computing environments. In: IEEE 2nd International Conference on Web Research (ICWR), pp. 145–151 (2016)Google Scholar
  25. 25.
    Rani, S., Kalan, K., Rana, S.: A hybrid approach of round Robin, Throttle & equally spaced technique for load balancing in cloud environment. Int. J. Innov. Adv. Comput. Sci. 6(8), 116–121 (2017)Google Scholar
  26. 26.
    Elrotub, M., Gherbi, A.: Virtual machine classification-based approach to enhanced workload balancing for cloud computing applications. Procedia Comput. Sci. 130, 683–688 (2018)Google Scholar
  27. 27.
    Phi, N.X., Tin, C.T., Nguyen, L., Thu, K., Hung, T.C.: Proposed load balancing algorithm to reduce response time and processing time on cloud computing. Int. J. Comput. Netw. Commun. 10(3), 87–98 (2018)Google Scholar
  28. 28.
    Internet World Facebook Stats. http://www.internetworldstats.com

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Badshaha Mulla
    • 1
    Email author
  • C. Rama Krishna
    • 1
  • Raj Kumar Tickoo
    • 2
  1. 1.Computer Science and Engineering DepartmentNational Institute of Technical Teachers Training and Research (NITTTR)ChandigarhIndia
  2. 2.National Informatics Centre, Punjab State UnitChandigarhIndia

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