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CLC number: TP393

On-line Access: 2011-11-30

Received: 2011-04-14

Revision Accepted: 2011-09-15

Crosschecked: 2011-11-04

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Journal of Zhejiang University SCIENCE C 2011 Vol.12 No.12 P.951-964

10.1631/jzus.C1100097


Optimizing storage performance in public cloud platforms


Author(s):  Jian-zong Wang, Peter Varman, Chang-sheng Xie

Affiliation(s):  School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China, Wuhan National Laboratory for Optoelectronics, Wuhan 430074, China, Department of Electrical and Computer Engineering, Rice University, Houston 77005, USA

Corresponding email(s):   jzwang@smail.hust.edu.cn, pjv@rice.edu, cs_xie@hust.edu.cn

Key Words:  Cloud storage, Performance fluctuation, Middleware, Service-level agreement


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Jian-zong Wang, Peter Varman, Chang-sheng Xie. Optimizing storage performance in public cloud platforms[J]. Journal of Zhejiang University Science C, 2011, 12(12): 951-964.

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Abstract: 
Cloud computing is an elastic computing model where users can lease computing and storage resources on demand from a remote infrastructure. It is gaining popularity due to its low cost, high reliability, and wide availability. With the emergence of public cloud storage platforms like Amazon, Microsoft, and Google, individual applications and enterprise storage are being deployed on Clouds. However, a serious impediment to its wider deployment is the relative lack of effective data management services. Our experiments, as well as industry reports, have shown that the performance and service-level agreement (SLA) cannot be guaranteed when the data is served over public Clouds. The relatively slow access to persistent data and large variability in cloud storage I/O performance can significantly degrade the performance of data-intensive applications. This paper addresses the issue of I/O performance fluctuation over public cloud platforms and we propose a middleware called CloudMW between the cloud storage and clients to provide the storage services with better performance and SLA satisfaction. Some technologies, including data virtualization, data chunking, caching, and replication, are integrated into CloudMW to achieve a more stable and predictable performance, and permit flexible sharing of storage among the virtual machines (VMs). Experimental results based on Amazon Web Services (AWS) show that CloudMW is able to improve the stability and help provide better SLAs and data sharing for cloud storage.

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