Enhanced Neural Network Metaheuristic Approach for Optimizing Virtual Machine Deployment in Cloud Data Centers

Abstract

In recent years, the optimization of virtual machine placement (VMP) in cloud data centers has emerged as a crucial and complex challenge, garnering considerable attention from researchers. Due to its NP-Hard nature, metaheuristic algorithms have become a popular approach in addressing this problem. These algorithms typically initiate with the random generation of initial solutions, which are then iteratively refined using specific operators to find the optimal solution. Among these algorithms, the Neural Network Algorithm (NNA) stands out as a promising swarm optimization technique known for its strong global search capability, making it well-suited for tackling various intricate optimization tasks. However, NNA often suffers from slow convergence due to its limited exploitation capability, thereby hindering its practical utility in optimization problem-solving. To address this limitation, this study proposes an approach to enhance the convergence speed of NNA while achieving a better balance between exploitation and exploration. Specifically, we adopt a heuristic algorithm to generate initial solutions in a more suitable and less random manner for the VMP problem. Additionally, the search operators of the algorithm are optimized using Cauchy and Lévy distributions. We evaluate the performance of the Improved Biogeography-based Neural Network Algorithm (IBNNA) on synthetic datasets of varying dimensions and compare the results with existing similar algorithms in the literature. The experimental findings demonstrate that the proposed algorithm outperforms other methods in terms of physical machine utilization, convergence rate, and power consumption across most experimental scenarios.