| Autor: |
Nuthakki, Praveena, T., Pavan Kumar, Alhussein, Musaed, Anwar, Muhammad Shahid, Aurangzeb, Khursheed, Gunnam, Leenendra Chowdary |
| Předmět: |
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| Zdroj: |
Computers, Materials & Continua; 2024, Vol. 81 Issue 3, p4743-4756, 14p |
| Abstrakt: |
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. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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