A workload prediction model for reducing service level agreement violations in cloud data centers

Abstract

Cloud computing has become an emerging technology that offers services based on the pay-as-usage model. The cloud provides several advantages, but these advantages come with challenges, such as reducing Service Level Agreement (SLA) violations, efficient resource utilization, reducing energy consumption, etc., needing attention to leverage customer satisfaction and benefit cloud service providers. Workload prediction is a strategy that provides many benefits: reduced SLA violation, resource scaling, and resource optimization by predicting future workload. However, due to the varying workload of cloud applications, it is difficult to predict the workload accurately, and it fails for long-term dependencies. We propose a methodology based on Multiplicative Long Short Term Memory (mLSTM) that allows input-dependent transitions and considers long-term dependencies to predict the workload to address this issue. The proposed method is implemented and compared with other variants of LSTM used in literature for workload prediction purposes. The proposed work outperforms existing variants of LSTM in terms of prediction accuracy.