Prediction-based proactive load balancing approach through VM migration

The ever-growing intricacy and dynamicity of Cloud Computing Systems has created a need for Proactive Load Balancing which is an effective approach to improve the scalability of today’s Cloud services. In order to manage the load proactively on the Cloud system during application execution, load should be predicted through machine learning approaches and handled through VM migration approaches. Thus, this paper formulates an effort to focus on the research problem of designing a prediction-based approach for facilitating proactive load balancing through the prediction of multiple resource utilization parameters in Cloud. The involvement of this paper is twofold. Firstly, various machine learning approaches have been tested and compared for predicting host overutilization as well as underutilization. Secondly, the load prediction model having maximum accuracy from the tested models has been utilized for implementing the proactive VM migration using multiple resource utilization parameters. Further, the proposed technique has been validated through performance evaluation parameters using CloudSim and Weka toolkits. The simulation results clearly demonstrate that the proposed approach is effective for handling VM migration, reducing SLA Violations, VM migrations, execution mean and standard deviation time.