Efficient load balancing techniques for multi-datacenter cloud milieu

Sharma, Sumanta Chandra Mishra; Rath, Amiya Kumar; Parida, Bivasa Ranjan

doi:10.1007/s41870-020-00529-2

Cited by 13 publications

(1 citation statement)

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“…The first group discusses the work that is being carried out using the metaheuristic approach. Sharma et al (Sharma, Rath, & Parida, 2020) have proposed a heuristic max select load balancing (MSLB) algorithm for both intra-datacenter and inter-datacenter systems without considering communication delay, as well as max select load balance with communication delay (MSLBCD) for different datacenters. Although the proposed techniques are providing better makespan and efficient resource utilization in comparison with some contemporary load balancing techniques, they are not considering multiple task allocation as well as the dynamic nature of the cloud system.…”

Section: Related Workmentioning

confidence: 99%

Binary Self-Adaptive Salp Swarm Optimization-Based Dynamic Load Balancing in Cloud Computing

Parida

Rath

Mohapatra

2022

International Journal of Information Technology and Web Engineering

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In the recent era of cloud computing, the huge demand for virtual resource provisioning requires mitigating the challenges of uniform load distribution as well as efficient resource utilization among the virtual machines in cloud datacenters. Salp swarm optimization is one of the simplest, yet efficient metaheuristic techniques to balance the load among the VMs. The proposed methodology has incorporated self-adaptive procedures to deal with the unpredictable population of the tasks being executed in cloud datacenters. Moreover, a sigmoid transfer function has been integrated to solve the discrete problem of tasks assigned to the appropriate VMs. Thus, the proposed algorithm binary self-adaptive salp swarm optimization has been simulated and compared with some of the recent metaheuristic approaches, like BSO, MPSO, and SSO for conflicting scheduling quality of service parameters. It has been observed from the result analysis that the proposed algorithm outperforms in terms of makespan, response time, and degree of load imbalance while maximizing the resource utilization.

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Section: Related Workmentioning

confidence: 99%