Workload and SLA Violation Prediction in Cloud Computing
R. Anitha, C Vidyaraj · 2019
Recently, cloud computing has come up as an evolving technology in various real-time computing field and widely adopted in several real-time computing systems. Due to its advantages of pay-as-you go services where users need not consider other factors such as hardware and platforms. Due to increasing demand of these applications, providing efficient resource and managing these resources is a crucial task because wastages of resources can lead towards the economic loss to the users and service providers. In order to deal with these issues, we focus on the workload prediction and SLA violation prediction approach for improving the overall performance. According to the proposed model, we develop a workload prediction model using clustering approach where similar workload patterns are grouped together to reduce the overhead and later SLA violation scheme is applied which is used for reducing the violations using Naïve Bayes classification approach. Finally, comparative experimental study is carried out which shows that the proposed approach archives better performance when compared with the state-of-art techniques.