Maximizing the profits of cloud service providers via dynamic virtual resource renting approach

Abstract

Promoted by the leading industrial companies, cloud computing has gained widespread concern recently. With an increasing number of cloud service providers (CSPs) delivering services to customers from the cloud, maximizing the profits of CSPs becomes a critical problem. Existing approaches 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 approach that attempts to dynamically adjust the virtual resource rental strategy according to price distribution and task urgency. 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. Then, a price prediction algorithm is presented to predict the price of virtual resources at the next price interval. Finally, we design a novel rental decision-making algorithm to select the most profitable resource from the set. We have implemented our approach and conducted experiments on both real and synthetic datasets. The results demonstrate that our approach obtain the better profit than other five approaches.