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AI-Driven Resource and Communication-Aware Virtual Machine Placement Using Multi-Objective Swarm Optimization for Enhanced Efficiency in Cloud-Based Smart Manufacturing

by Praveena Nuthakki1, Pavan Kumar T.1, Musaed Alhussein2, Muhammad Shahid Anwar3,*, Khursheed Aurangzeb2, Leenendra Chowdary Gunnam4

1 Department of Computer Science & Engineering, Koneru Lakshmaiah Education Foundation, Guntur, 522302, India
2 Department of Computer Engineering, College of Computer and Information Sciences, King Saud University, Riyadh, 11543, Saudi Arabia
3 Department of AI and Software, Gachon University, Seongnam-Si, 13120, Republic of Korea
4 Department of Electronics and Communication Engineering, SRM University, Amaravati, 522502, India

* Corresponding Author: Muhammad Shahid Anwar. Email: email

(This article belongs to the Special Issue: Applications of Artificial Intelligence in Smart Manufacturing)

Computers, Materials & Continua 2024, 81(3), 4743-4756. https://doi.org/10.32604/cmc.2024.058266

Abstract

Cloud computing has emerged as a vital platform for processing resource-intensive workloads in smart manufacturing environments, enabling scalable and flexible access to remote data centers over the internet. In these environments, Virtual Machines (VMs) are employed to manage workloads, with their optimal placement on Physical Machines (PMs) being crucial for maximizing resource utilization. However, achieving high resource utilization in cloud data centers remains a challenge due to multiple conflicting objectives, particularly in scenarios involving inter-VM communication dependencies, which are common in smart manufacturing applications. This manuscript presents an AI-driven approach utilizing a modified Multi-Objective Particle Swarm Optimization (MOPSO) algorithm, enhanced with improved mutation and crossover operators, to efficiently place VMs. This approach aims to minimize the impact on networking devices during inter-VM communication while enhancing resource utilization. The proposed algorithm is benchmarked against other multi-objective algorithms, such as Multi-Objective Evolutionary Algorithm with Decomposition (MOEA/D), demonstrating its superiority in optimizing resource allocation in cloud-based environments for smart manufacturing.

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APA Style
Nuthakki, P., T., P.K., Alhussein, M., Anwar, M.S., Aurangzeb, K. et al. (2024). Ai-driven resource and communication-aware virtual machine placement using multi-objective swarm optimization for enhanced efficiency in cloud-based smart manufacturing. Computers, Materials & Continua, 81(3), 4743–4756. https://doi.org/10.32604/cmc.2024.058266
Vancouver Style
Nuthakki P, T. PK, Alhussein M, Anwar MS, Aurangzeb K, Gunnam LC. Ai-driven resource and communication-aware virtual machine placement using multi-objective swarm optimization for enhanced efficiency in cloud-based smart manufacturing. Comput Mater Contin. 2024;81(3):4743–4756. https://doi.org/10.32604/cmc.2024.058266
IEEE Style
P. Nuthakki, P. K. T., M. Alhussein, M. S. Anwar, K. Aurangzeb, and L. C. Gunnam, “AI-Driven Resource and Communication-Aware Virtual Machine Placement Using Multi-Objective Swarm Optimization for Enhanced Efficiency in Cloud-Based Smart Manufacturing,” Comput. Mater. Contin., vol. 81, no. 3, pp. 4743–4756, 2024. https://doi.org/10.32604/cmc.2024.058266



cc Copyright © 2024 The Author(s). Published by Tech Science Press.
This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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