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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
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
Furht, B., Escalante, A.: Handbook of Cloud Computing, vol. 63, no. 3, pp. 67–76. Springer, New York (2006)
Garg, A.: A literature review of various load balancing techniques in cloud computing environment. Big Data Anal. 654, 667–675 (2018)
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)
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)
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)
Siddiqui, S., Darbari, M., Diwakar, Y.: A comprehensive study of challenges and issues in cloud computing. Soft Comput. Signal Process. 900, 325–344 (2019)
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)
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)
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)
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)
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)
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)
Kumar, P., Kumar, K.: Issues and challenges of load balancing techniques in cloud computing: a survey. ACM Comput. Surv. 51(6), 1–35 (2019)
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)
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)
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)
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)
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)
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)
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)
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)
Tyagi, V., Kumar, T.: ORT broker policy: reduce cost and response time using throttled load balancing algorithm. Procedia Comput. Sci. 48, 217–221 (2015)
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)
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)
Elrotub, M., Gherbi, A.: Virtual machine classification-based approach to enhanced workload balancing for cloud computing applications. Procedia Comput. Sci. 130, 683–688 (2018)
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)
Internet World Facebook Stats. http://www.internetworldstats.com
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Mulla, B., Rama Krishna, C., Tickoo, R.K. (2020). Virtual Machine Allocation in Heterogeneous Cloud for Load Balancing Based on Virtual Machine Classification. In: Smys, S., Bestak, R., Rocha, Á. (eds) Inventive Computation Technologies. ICICIT 2019. Lecture Notes in Networks and Systems, vol 98. Springer, Cham. https://doi.org/10.1007/978-3-030-33846-6_38
Download citation
DOI: https://doi.org/10.1007/978-3-030-33846-6_38
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-33845-9
Online ISBN: 978-3-030-33846-6
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)