Energy-aware and Carbon Footprint Optimization Model for Virtual Machine Placement in Data Centres – A Systematic Literature Review

Abstract

Purpose—The rapid adoption of cloud resources and their applications has increased energy consumption and carbon footprint emissions in data centres, raising significant environmental concerns. One key strategy to address this challenge is optimizing the allocation of resources and applications, such as virtual machines (VMs), within data centre architectures. Virtual Machine Placement (VMP), which involves selecting the best physical machine (PM) to host user-requested VMs, is critical in reducing energy consumption and carbon emissions.

Method – This study undertakes a systematic analysis of the requirements for energy-aware and carbon footprint (CFP) optimization in VMP within cloud data centres. A comparative approach is employed to evaluate the salient features of various VMP methods, examining their ability to meet energy efficiency and sustainability goals. VMP is identified as a complex combinatorial optimization problem that is NP-Hard, necessitating innovative strategies to achieve optimal solutions.

Conclusion – Despite advancements in energy-efficient and CFP-optimized VMP methods, unresolved challenges remain, such as balancing energy savings with service quality and scalability. These challenges highlight the need for continued exploration of innovative techniques to address the complex nature of VMP problems.

Recommendations – Future research should focus on hybrid optimization techniques, leveraging metaheuristic approaches to improve energy efficiency and sustainability. Moreover, developing real-world prototypes and frameworks to validate theoretical models can bridge the gap between research and practical implementation, paving the way for sustainable cloud computing solutions.

Practical Implications – This review highlights effective energy-aware and CFP strategies for VMP in CDC. Categorizing and analyzing recent developments provides practical insights for cloud service providers and data centre operators to implement efficient VMP methods. These findings enable better resource allocation, reduced operational costs, and minimized environmental impact, aligning with sustainability goals and improving the overall performance of cloud infrastructure systems.