Cloud Computing has emerged as a computing paradigm where services are provided through the internet in recent years. Offering on-demand services has transformed the IT companies' working environment, leading to a linearly increasing trend of its usage. The provisioning of the Computing infrastructure is achieved with the help of virtual machines. A great figure of physical devices is required to satisfy the users' resource requirements. To meet the requirements of the submitted workloads that are usually dynamic, the cloud data centers cause the over-provisioning of cloud resources. The result of this over-provisioning is the resource wastage with an increase in the levels of energy consumption, causing a raised operational cost. High CO 2 emissions result from this huge energy consumption by data centers, posing a threat to environmental stability. The environmental concern demands for the controlled energy consumption, which can be attained by optimal usage of resources to achieve in the server load, by minimizing the number of active nodes, and by minimizing the frequency of switching between active and de-active server mode in the data center. Motivated by these actualities, we discuss numerous statistical, deterministic, probabilistic, machine learning and optimization based computational solutions for the cloud computing environment. A comparative analysis of the computational methods, on the basis of architecture, consolidation step involved, objectives achieved, simulators involved and resources utilized, has also been presented. A taxonomy for virtual machine (VM) consolidation has also been derived in this research article followed by emerging challenges and research gaps in the field of VM consolidation in cloud computing environment.

Fundamentally, a strategy considering the effective utilization of resources results in the better energy efficiency of the system. The aroused interest of users in cloud computing has led to an increased power consumption making the network operation costly. The frequent requests from the users asking for computing resources can lead to instability in the load of the computing system. To perform the load balancing in the host, migration of the virtual machines from the overloaded and underloaded hosts needs to be done, which is considered an important facet concerning energy consumption. The proposed Particle Swarm Optimization based Resource Aware VM Placement (RAPSO_VMP) scheme aims to place the migrated virtual machines. RAPSO_VMP takes into consideration multiple resources like CPU, storage, and memory while trying to optimize the overall resource utilization of the system. According to the simulation analysis, the proposed RAPSO_VMP scheme shows an improvement of 5.51% in energy consumption, reduced the number of migrations by 9.12%, and the number of hosts shutdowns 22.74%.

Author affiliations were forgotten to be included during initial submission/proofing and has been requested to be included as: Bhagyalakshmi Magotra: MIET: Model