Presenting an optimal method for computational resource management of computational cloud data centers using a load balance approach and optimization of energy consumption

Farzaneh Rahmani; Arman Mostafavi Sabzevari; Seyed Mohsen Saneii Alireza Izadi; Alireza Izadi; Roohollah Barzamini

Author(s)

Farzaneh Rahmani Department of Information Technology Management, Islamic Azad University, South Tehran Branch, Tehran, Iran

Arman Mostafavi Sabzevari University of Genoa, Genoa, Italy

Seyed Mohsen Saneii Alireza Izadi Business School, University of Roehampton, SW15 5PH, London, United Kingdom

Alireza Izadi Department of Electrical Engineering, Islamic Azad University, Tehran Central Branch Tehran, Iran

Roohollah Barzamini Department of Engineering, City University of London, London, United Kingdom

Abstract

Cloud computing technology has attracted the attention of many researchers, organizations, governments, etc. In cloud computing, load balance, resource allocation management, and scheduling execution of tasks are among the most important challenges. In a data center, there are still confrontations for load balancing, scheduling tasks, and resource allocation at the level of hosts and VMs. Considering the challenges in cloud computing, this article uses an advanced Cat Swarm Optimization (CSO) algorithm to allocate resources to virtual machines while maintaining a load balance. The mentioned method has several stages including initialization of the algorithm and cloud computing, determining the number of virtual machines and number of tasks, implementing the advanced cat swarm optimization algorithm, allocating resources, and scheduling tasks while maintaining the load balance of virtual machines. Through simulation of the method presented here, it was observed that the extent of improvement of the advanced cat swarm optimization algorithm is considerable in allocating resources and maintaining load balance between virtual machines compared to other methods based on the criteria of execution time, response time, and the number of migrated tasks. In this paper, we have investigated different criteria for evaluating the extent of efficiency of the advanced cat swarm optimization algorithm. The obtained results indicated that the execution time of the advanced cat swarm algorithm with the number of virtual machines of 5 to 40 and 40 tasks is 0.2084s on average, while for the best-performed algorithm(HBB-LB), we know at the time of writing this paper is 0.4004s. Accordingly, the extent of improvement ratio of the proposed algorithm compared to the former best method is 1.92. Further, the average response time to the requests in the advanced cat swarm algorithm with 5 to 40 virtual machines and 40 tasks is 0.2624s. while for the HBBLB algorithm, the response time is 0.4128s on average. Based on the results obtained from implementing the suggested algorithm, it is observed that the number of tasks when the advanced cat swarm algorithm is implemented with 5 to 40 virtual machines and 40 tasks is 3 tasks on average. On the other hand, using HBB-LB the number of migrated tasks is about 5.6 on average, showing that the extent of improvement by the proposed method is good.

Publication Details

Page(s) 152-170

DOI DOI not available

Published Journal: International Journal of Communication Networks and Information Security, Volume: 16, Issue: 5, Year: 2024

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Keywords

resource allocation, Cloud computing, Cat Swarm Optimization, Load Balance, Task Scheduling

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