Dynamic Virtual Resource Renting Method for Maximizing the Profits of a Cloud Service Provider in a Dynamic Pricing Model

Abstract

With an increasing number of cloud service providers (CSP) delivering services to customers from the cloud, maximizing the profits of CSPs becomes a critical problem. Existing methods are difficult to solve the problem because they do not make full use of temporal price differences. This paper introduces a dynamic virtual resource renting method that attempts to dynamically adjust the virtual resource rental strategy according to price distribution and task urgency. We first pretreat the historical price series and adopt the outlier detection technique to filter the extreme price. Then, considering task urgency and price distribution, we design a weak equilibrium operator to calculate the acceptable price for each type of virtual resource. All types of virtual resources that are at an acceptable price are inserted into a set. Finally, we design a novel rental decision-making algorithm to select the most profitable resource from the set. We provide an extensive evaluation of our method using Amazon EC2 spot price dataset and normally distributed price dataset. The results demonstrate the effectiveness of our method.