Mohammad Zubair Khan

Work place: Department of Computer Science and Information, Taibah University Madinah, 42353, Saudi Arabia

E-mail: mkhanb@taibahu.edu.sa

Website:

Research Interests: Big Data, Data Mining, Parallel Computing, Distributed Computing, Computer Networks

Biography

Mohammad Zubair Khan received the M. Tech. degree in computer science and engineering from Uttar Pradesh Technical University, Lucknow, India, in 2006, and the Ph. D. degree in computer science and information technology from the Faculty of Engineering, Mahatma Jyotiba Phule Rohilkhand University, Bareilly, India. He is currently a Professor at the Department of Computer Science, College of Computer Science and Engineering, Taibah University. His current research interests include data mining, big data, parallel and distributed computing, the theory of computations, and computer networks.

Author Articles
Hybrid Spider Monkey Optimization Mechanism with Simulated Annealing for Resource Provisioning in Cloud Environment

By A. Archana N. Kumar Mohammad Zubair Khan

DOI: https://doi.org/10.5815/ijcnis.2024.01.03, Pub. Date: 8 Feb. 2024

Cloud computing is an emerging concept that makes better use of a large number of distributed resources. The most significant issue that affects the cloud computing environment is resource provisioning. Better performance in the shortest amount of time is an important goal in resource provisioning. Create the best solution for dynamically provisioning resources in the shortest time possible. This paper aims to perform resource provisioning with an optimal performance solution in the shortest time. Hybridization of two Meta-heuristics techniques, such as HSMOSA (Hybrid Spider Monkey Optimization with Simulated Annealing), is proposed in resource provisioning for cloud environment. Finds the global and local value using Spider Monkey Optimization's (SMO) social behavior and then utilizes Simulated Annealing (SA) to search around the global value in each iteration. As a result, the proposed approach aids in enhancing their chances of improving their position. The CloudSimPlus Simulator is used to test the proposed approach. The fitness value, execution time, throughput, mean, and standard deviation of the proposed method were calculated over various tasks and execution iterations. These performance metrics are compared with the PSO-SA algorithm. Simulation results validate the better working of the proposed HSMOSA algorithm with minimum time compared to the PSO-SA algorithm.

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