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QoS-Aware Revenue-Cost Optimization for Latency-Sensitive Services in IaaS Clouds

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5 Author(s)
Ta Nguyen Binh Duong ; Comput. Sci. Dept., A*STAR Inst. of High Performance Comput., Singapore, Singapore ; Xiaorong Li ; Goh, R.S.M. ; Xueyan Tang
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Recently, application service providers have been employing Infrastructure-as-a-Service (IaaS) clouds such as Amazon EC2 to scale their computing resources on-demand to adapt to dynamic workloads. Existing research has been focusing more on cloud resource scaling in batch processing, non latency-sensitive applications. In this paper, we consider the problem of revenue-cost optimization in cloud-based application service providers with stringent QoS requirements, e.g., online gaming services. We propose an integrated approach which combines resource provisioning algorithms and request scheduling disciplines. The main goal is to maximize the service provider's revenue via satisfying pre-defined QoS requirements, and at the same time, to minimize cloud resource cost. We have implemented the proposed resource provisioning algorithms and scheduling disciplines into a cloud scaling framework developed in our previous work. Extensive experiments have been conducted with a fully functional implementation and realistic workloads modeled after real traces of popular online game servers. The results demonstrated the effectiveness of our proposed approach.

Published in:

Distributed Simulation and Real Time Applications (DS-RT), 2012 IEEE/ACM 16th International Symposium on

Date of Conference:

25-27 Oct. 2012