Nowadays, Cloud computing is an emerging technique to improve the computations strongly, and to access storage resources easily. During the provision of demand-on service to utilize virtual resources, an efficient load predicting virtual machine aware model is required to improve the quality of services (QoS) and Data Center performance. The main objective of paper is to distribute the load to the available Virtual Machine efficiently so that requestand response time can be improved and delay time will be minimized. The existing systems are designed to maintain the load, which allow for providing and releasing resources on demand, therefore, providing elastic abilities to the entire environments. However, the techniques failed to optimize load balancing where part of the servers could have heavy load after an execution of application. The response time on the servers take long time to process the service due to the additional long response latency, which is unfavorable for real-time webbased applications. To alleviate the problem, the novel framework designs Efficient Load Predicting Virtual Machine Aware (ELPVMA) Algorithm is proposed to maintain job arrival process and System Queue. The algorithms comprise the thousands of resources and flexible to represent different policies and cloud-specific strategies. It also reduces the bandwidth issues and minimizes the data centre response delay. The technique minimizes the energy consumption for improving the quality of service. Based on Experimental evaluations, proposed algorithm reduced the104.47 seconds Data center Processing Time and 102.47 seconds Response Time of proposed system compared than existing methods on Closest Data Center, Optimize Response Time, and Reconfigure dynamically with load policy.
Volume 9 | 17-Special Issue
Pages: 320-333