The cloud platforms are becoming popular day by day with the advent of more customers every second, flooding the cloud environment with millions of requests thereby making the processing of such requests a major challenge to be handled. The major goal of cloud computing is to provide requested resources efficiently and effectively which can be achieved by distributing the load in a balanced manner among various nodes leaving the network in an optimal condition. In this research paper, much optimized load balancing scheme has been proposed to schedule the tasks in the cloud environment. The scheme has been designed to calculate the load on the list of available Virtual Machines (VM), considering the CPU utilization and usage as a metric for calculation of load. The proposed scheme modifies the already existing Active Monitoring Load balancing algorithm and merges the advantages of Active Monitoring Load Balancing and dynamic Round Robin scheduling techniques.

Cloud computing; resource management; load balancing; virtual machines

[1] S. Kapoor, “Cluster Based Load Balancing in Cloud Computing,” Ieee, 2015.
[2] S. Hamid, H. Madni, M. Shafie, A. Latiff, Y. Coulibaly, and M. Abdulhamid, “Recent advancements in resource allocation techniques for cloud computing environment : a systematic review,” Cluster Comput., 2016.
[3] S. Singh and I. Chana, “Cloud resource provisioning: survey, status and future research directions,” Knowl. Inf. Syst., vol. 49, no. 3, pp. 1005–1069, 2016.
[4] S. Singh and I. Chana, “A Survey on Resource Scheduling in Cloud Computing :,” J. Grid Comput., pp. 217–264, 2016.
[5] M. Xu and W. Tian, “A survey on load balancing algorithms for virtual machines placement in cloud computing,” no. February, pp. 1–16, 2017.
[6] Y. W. Qiu and J. I. G. Hwang, “A Two-Level Load Balancing Method with Dynamic Strategy for Cloud Computing,” Proc. - 2016 IEEE 14th Int. Conf. Dependable, Auton. Secur. Comput. DASC 2016, 2016 IEEE 14th Int. Conf. Pervasive Intell. Comput. PICom 2016, 2016 IEEE 2nd Int. Conf. Big Data Intell. Comput. DataCom 2016 2016 IEEE Cyber Sci. Technol. Congr. CyberSciTech 2016, DASC-PICom-DataCom-CyberSciTech 2016, pp. 565–571, 2016.
[7] N. K. Chien, N. H. Son, and H. Dac Loc, “Load balancing algorithm based on estimating finish time of services in cloud computing,” 2016 18th Int. Conf. Adv. Commun. Technol., pp. 1–1, 2016.
[8] U. Thakkar, “A Novel Approach for Dynamic Selection of Load Balancing Algorithms in Cloud Computing,” pp. 1–4, 2016.
[9] Y. Gao, “Energy-aware Load Balancing in Heterogeneous Cloud Data Centers.”
[10] S. G. Damanal and G. R. M. Reddy, “Optimal Load Balancing in Cloud Computing By Efficient Utilization of Virtual Machines,” 2014.
[11] S. G. Domanal and G. R. M. Reddy, “Load Balancing in Cloud Computing Using Modified Throttled Algorithm.”
[12] D. Malhotra, Bhagyalakshmi, “A Review : Different Improvised Throttled Load Balancing Algorithms in Cloud Computing Environment,” Int. J. Eng. Technol. Manag. Appl. Sci., vol. 5, no. 7, pp. 409–416, 2017.
[13] R. Panwar and B. Mallick, “Load Balancing in Cloud Computing Using Dynamic Load Management Algorithm,” Ieee, pp. 773–778, 2015.
[14] S. Elmougy, S. Sarhan, and M. Joundy, “A novel hybrid of Shortest job first and round Robin with dynamic variable quantum time task scheduling technique,” J. Cloud Comput. Springer, 2017.