Improved Service Broker Algorithm Based On Weighted Moving Average Forecast Model for Cloud Computing?

Proper load balancing is an important aspect while calculating efficiency of cloud computing environment. In Cloud Computing Scenario Load Balancing is composed of selecting proper Data Center for upcoming request (Service Broker) and Virtual machine management at individual Data Center. In this paper, we proposed and implemented Predictive Service Broker (DC selection) dynamic algorithm based on Improved Weighted Moving Average Forecast Model. This algorithm is developed by combining advantages of both proximity service broker algorithm and DC selection algorithm based on weighted moving average prediction model. This study concludes that proposed predictive DC selection algorithm mainly focus on reducing service response time observed at client side. Thus development of effective load balancing algorithm aids in minimizing resource consumption, implementing fail-over, enabling scalability, avoiding bottlenecks etc in cloud computing. The result shows drastic reduction in Response time at client side by using Predictive Improved Weighted Moving Average Forecast DC selection algorithm. Various parameters are also identified such as Data Center request service times, Data Center hourly loading, total Data Transfer and Virtual machine costing and respective values are calculated.