Efficient Energy-Aware Resource Management Model (EEARMM) Based Dynamic VM Migration

In cloud environment, an efficient resource management establishes the allocation of computational resources of cloud service providers to the requests of users for meeting the user's demands. The proficient resource management and work allocation determines the accomplishment of the cloud infrastructure. However, it is very difficult to persuade the objectives of the Cloud Service Providers (CSPs) and end users in an impulsive cloud domain with random changes of workloads, huge resource availability and complicated service policies to handle them, With that note, this paper attempts to present an Efficient Energy-Aware Resource Management Model (EEARMM) that works in a decentralized manner. Moreover, the model involves in reducing the number of migrations by definite workload management for efficient resource utilization. That is, it makes an effort to reduce the amount of physical devices utilized for load balancing with certain resource and energy consumption management of every machine. The Estimation Model Algorithm (EMA) is given for determining the virtual machine migration. Further, VM-Selection Algorithm (SA) is also provided for choosing the appropriate VM to migrate for resource management. By the incorporation of these algorithms, overloading of VM instances can be avoided and energy efficiency can be improved considerably. The performance evaluation and comparative analysis, based on the dynamic workloads in different factors provides evidence to the efficiency, feasibility and scalability of the proposed model in cloud domain with high rate of resources and workload management.

Automated summary

This paper presents an Efficient Energy-Aware Resource Management Model (EEARMM) that operates in a decentralized manner in the cloud environment. By reducing migration frequency and employing appropriate VM selection algorithms, the model achieves efficient resource utilization and energy efficiency. Performance evaluation and comparative analysis demonstrate the efficiency, feasibility, and scalability of the proposed model in resource and workload management.