A Linear Regression-Based Resource Utilization Prediction Policy for Live Migration in Cloud Computing

Abstract

A new emerging state-of-the-art challenging research area has been found in cloud computing. Cloud Computing is an idea, rely on service and delivery, it is distributed over the Internet and governed by appropriate set of protocol. In last few decades, Internet is growing rapidly as a result cloud computing and also expanded exponentially. Cloud computing is said to provide resoruces such as Software, Platform, and Infrastructure as services, namely, Software as a Service (SaaS), Platform as a Service (PaaS), and Infrastructure as a Service (IaaS). Cloud profaned the infrastructure resources like CPU, bandwidth, and memory to its end users as a part of its IaaS service. To meet the end users’ heterogeneous needs for resources it profaned and unprofane the resources dynamically, with minimal management effort of the service providers over the Internet. Thus, eliminating the need to manage the expensive hardware resources by companies and institutes. However, to satisfy the need for resources of the users on time, Cloud Service Provider (CSP) must have to maintain the Quality of Service (QoS). Service Level Agreement (SLA) is done between the Datacenters and its end users. Minimization of the violation of the SLA ensures better QoS. Research fraternity has proposed that one of the main reasons for violation of SLA is inefficient load balancing approaches in hosts that fail to ensure QoS, without missing the deadline by the distribution of dynamic workload evenly. In this paper, we propose to extend our previous work of simulated annealing-based optimized load balancing [1] by adding VM migration policy from one host to another on the basis of linear regression-based prediction policy for futuristic resource utilization. In our approach, we are going to predict short-time future resource utilization using linear regression based on the history of the previous utilization of resources by each host. We further use it in migration process to predict the overloaded hosts to underloaded ones. Experiments were simulated in CloudAnalyst and the results are quite encouraging and outperform some previous existing strategies of load balancing for ensuring QoS.