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A Data-Mining-Based Prefetching Approach to Caching For Network Storage Systems

Start date:

June 2001


IBM Sillicon Valley Lab, San Jose, Dr. Bala Iyer
IBM Storage Systems Divison, Tucson, Dr. Tarek Makansi

Working staff:

Faculty - Dr. Olivia Sheng, UA-MIS
Students - Xiao Fang and Wei Gao, UA-MIS


The need for network storage has been increasing at an exponential rate owing to the widespread use of the Internet in organizations and the shortage of local storage space due to the increasing size of applications and databases. Proliferation of network storage systems entails a significant increase in the amount of storage objects (e.g., files) stored, the number of concurrent clients, and the size and number of storage objects transferred between the systems and their clients. Performance (e.g., client perceived latency) of these systems becomes a major concern. Previous research has explored techniques for scaling-up of the number of storage servers involved to enhance the performance of network storage systems. However, adding servers to improve system performance is an expensive solution. Moreover, for a WAN-based network storage system, the bottleneck for its performance improvement typically is not caused by the load of storage servers but by the network traffic between clients and storage servers. This paper introduces an Internet-based network storage system named NetShark and proposes a caching-based performance enhancement solution for such a system. The proposed performance enhancement solution is validated using a simulation. Three major contributions of this project are: (1) we have built a caching-based network storage system to expand storage capacities and to enhance access performance of system users; (2) we have proposed and shown that a data-mining-based prefetching approach outperformed other popularly applied caching approaches; (3) we have developed a network storage caching simulator to test the performance of different caching approaches.



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