Energy Efficient and Multi-resource Optimization for Virtual Machine Placement by Improving MOEA/D

Abstract

The explosive growth of cloud services has led to the widespread construction of large-scale data centers to meet diverse and multifaceted cloud computing demands. However, this expansion has resulted in substantial energy consumption. Virtual machine placement (VMP) has been extensively studied as a means to provide flexible and scalable cloud services while optimizing energy efficiency. Yet, the increasing complexity and diversity of applications have posted VMP suffering from waste of resources and bottlenecks due to unbalanced utilization of multi-dimensional resources. To address these issues, this article proposes a bi-objective optimization model for VMP that jointly optimizes power consumption and multi-dimensional resource utilization. Solving this large-scale bi-objective model presents a significant challenge in balancing performance and computational complexity. To tackle this, an enhanced decomposition-based multi-objective evolutionary algorithm (MOEA/D) based on$\varepsilon$-domination, termed$\varepsilon$-IMOEA/D-M2M is designed to provide solutions for the proposed optimization. Compared with both heuristics and evolutionary algorithms, performance evaluations demonstrate that our proposed VMP algorithm effectively reduces power consumption and balances multidimensional resource utilization while significantly decreasing running time compared to both heuristic and traditional evolutionary algorithms.