Efficient Cloud Resource Scaling based on Prediction Approaches

Abstract

Resource Scaling is one of the important job in cloud environment while adapting resource configurations due to elasticity mechanism. In the view of cloud computing, resource scaling mechanism hold the assurance of QoS (Quality of Service), So, one of the key challenging task in cloud environment is, resource scaling. Effective scaling mechanism gives an optimal solutions for computational problems while achieving QoS and avoiding SLA (Service Level Agreement) violations. To enhance resource scaling mechanism in cloud environment, predicting future workload to the each application in different manners like number of physical machines, number of virtual machines, number of requests and resource utilization etc., is an essential step. According to the prediction results, resource scaling can be done in the right time, while preventing QoS dropping and SLA violations. To achieve efficient resource scaling, proposed approach lease advantages of fuzzy time series and machine learning algorithms. The proposed approach is able to reach effective resource scaling mechanism with better results.