The importance of nature-inspired meta-heuristic algorithms for solving virtual machine consolidation problem in cloud environments

Nowadays, cloud computing is known as an internet-based modern area among emerging technologies that brings up an environment, in which computing resources such as hardware, software, storage, etc. can be rented by cloud users based on a pay per use model. Since the size of cloud computing is widely expanding and the number of cloud users is also increasing day by day, high energy consumption becomes a serious concern in the operation of complex cloud data centers. In this regards, Virtual Machine (VM) consolidation plays a vital role in utilizing cloud resources in an efficient manner. It migrates the running VMs from overloaded Physical Machines (PMs) to other PMs considering multiple factors, such as migration overhead, energy consumption, resource utilization, and migration time. Since the VM consolidation issue is known as an NP-hard problem, various nature-inspired meta-heuristic algorithms aiming to solve this problem have been utilized in recent years. However, a lack of systematic and detailed survey study in this field is obvious. Therefore, this gap motivated us to provide the current paper aiming to highlight the role of nature-inspired meta-heuristic algorithms in the VM consolidation problem, review the existing approaches, offer a detailed comparison of approaches based on important factors, and finally, outline the future directions.

Automated summary

Cloud computing is a modern technology that allows users to rent computing resources based on a pay-per-use model, however, high energy consumption in complex cloud data centers has become a serious concern. Virtual Machine (VM) consolidation plays a vital role in utilizing cloud resources efficiently, but a lack of systematic study in this field is evident. Future research is needed to explore the role of nature-inspired meta-heuristic algorithms and outline the future directions in solving the VM consolidation problem.