Machine Learning Based Workload Prediction in Cloud Computing

Abstract

As a widely used IT service, more and more companies shift their services to cloud datacenters. It is important for cloud service providers (CSPs) to provide cloud service resources with high elasticity and cost-effectiveness and then achieve good quality of service (QoS) for their clients. However, meeting QoS with cost-effective resource is a challenging problem for CSPs because the workloads of Virtual Machines (VMs) experience variation over time. It is highly necessary to provide an accurate VMs workload prediction method for resource provisioning to efficiently manage cloud resources. In this paper, we first compare the performance of representative state-of-the-art workload prediction methods. We suggest a method to conduct the prediction a certain time before the predicted time point in order to allow sufficient time for task scheduling based on predicted workload. To further improve the prediction accuracy, we introduce a clustering based workload prediction method, which first clusters all the tasks into several categories and then trains a prediction model for each category respectively. The trace-driven experiments based on Google cluster trace demonstrates that our clustering based workload prediction methods outperform other comparison methods and improve the prediction accuracy to around 90% both in CPU and memory.