Pricing Scheme for Heterogeneous Multiserver Cloud Computing System.

Abstract

Background and Objective: Previous works on pricing in cloud computing environments assumed cloud servers are homogeneous. The assumption of homogeneous servers was not realistic and cannot accurately model practical deployment scenarios of cloud servers since cloud providers deploy heterogeneous servers with different service rates and capacities. The objective of this study was to model a pricing scheme for heterogeneous cloud computing servers based on response time and slow down. Methodology: To overcome the above challenge, this study proposed a pricing model for heterogeneous multiserver cloud computing system. Heterogeneous multiserver cloud computing systems had different capacities in terms of service rate and processing power. The proposed pricing mechanism was charged based on mean response time and mean slowdown. Mean slowdown was introduced as a performance metric because it was representative of the size of all requests in the system unlike mean response time used in previous studies which was representative of the size of requests which were larger in size and not representative of all requests. Queueing theory was employed to derive expressions for revenue in terms of mean response time and mean slowdown. The performance of the heterogeneous multiserver system was compared to homogeneous system using MATLAB. Results: Numerical results showed that heterogeneous multiserver system generated more revenue than homogeneous multiserver system especially at high load and high arrival rate values for both pricing mechanisms based on response time and slow down. It was further observed that more revenue generated when mean slowdown was used as a charging metric than when mean response time was used, especially at high load values and high arrival rates. Conclusion: Heterogeneous multiserver system generated more revenue than homogeneous multiserver system. In addition, mean slowdown generated more revenue when used as a charging metric than mean response time.