Learning Curves and Stochastic Models for Pricing and Provisioning Cloud Computing Services

Abstract

The paradigm of cloud computing has started a new era of service computing. While there are many research efforts on developing enabling technologies for cloud computing, few focuses on how to strategically set price and capacity and what key components are leading to success in this emerging market. In this paper, we present quantitative modeling and optimization approaches for assisting such decisions in cloud computing services. We first show that learning curve models can be helpful to capture the providers' cost reduction with economy of scale. Such models also help understand the potential market of cloud services and explain quantitatively why cloud computing is most attractive to small and medium businesses. We then present a stochastic model and a revenue management formulation to address the pricing and resource provisioning decisions for the cloud service providers. The approach enables the cloud service provider a quantitative framework to obtain management solutions and to learn and react to the critical parameters in the operation management process by gaining useful business insights. [Service Science, ISSN 2164-3962 (print), ISSN 2164-3970 (online), was published by Services Science Global (SSG) from 2009 to 2011 as issues under ISBN 978-1-4276-2090-3.]