A Two-Phase Cloud Resource Provisioning Algorithm for Cost Optimization

Abstract

Cloud computing is a new computing paradigm to deliver computing resources as services over the Internet. Under such a paradigm, cloud users can rent computing resources from cloud providers to provide their services. The goal of cloud users is to minimize the resource rental cost while meeting the service requirements. In reality, cloud providers often offer multiple pricing models for virtual machine (VM) instances, including on-demand and reserved pricing models. Moreover, the workload of cloud users varies with time and is not known a priori. Therefore, it is challenging for cloud users to determine the optimal cloud resource provisioning. In this paper, we propose a two-phase cloud resource provisioning algorithm. In the first phase, we formulate the resource reservation problem as a two-stage stochastic programming problem, and solve it by the sample average approximation method and the dual decomposition method. In the second phase, we propose a hybrid ARIMA-Kalman model to predict the workload, and determine the number of on-demand instances based on the predicted workload. The effectiveness of the proposed two-phase algorithm is evaluated using a real-world workload trace and Amazon EC2’s pricing models. The simulation results show that the proposed algorithm can significantly reduce the operational cost while guaranteeing the service level agreement (SLA).