Revenue Maximization Based on Slowdown in Cloud Computing Environments

Abstract

Background and Objective: Previous pricing mechanisms have been based on response time. The challenge with response time is that it only focuses on the time when a request terminates and does not focus on the size of the request, thus response time tends to be representative of the performance of just a few big requests and not all the requests since they count the most in the mean. On the other hand, slowdown measures the responsiveness of the system with respect to the length of the request that is, requests are completed within the time proportional to request demand. The main objective of this study is to maximize revenue using resource allocation in cloud computing environments based on mean slowdown and instant slowdown customer-oriented pricing mechanisms. Methodology: To overcome the challenge of pricing based on response time, two customer-oriented pricing mechanisms Mean Slowdown (MS) and Instant Slowdown (IS) are proposed, in which the customers are charged according to achieved service performance in terms of slowdown. Analytical models of pricing mechanisms based on slowdown are developed for cloud computing under First Come First Served and Processor Sharing scheduling policies. Lagrange multiplier composite functions are then differentiated and equated to zero to determine the number of se`rvers that give maximum revenue. Results: The numerical results obtained from the derived models show that revenue generated under slowdown pricing mechanisms are higher than revenue generated under response time pricing mechanisms. It is further observed that processor sharing policy generally generates more revenue than first come first served scheduling policy especially when there are more servers. Conclusion: It is concluded that pricing mechanisms based on slowdown can generate more revenue for the service provider than pricing mechanism based on response time.