Jung Kwang Chul, Park Ung Kyu

The Transactions of the Korea Information Processing Society (1994 ~ 2000), Vol. 3, No. 7, pp. 1669-1679, Dec. 1996
10.3745/KIPSTE.1996.3.7.1669,   PDF Download:

Abstract

In this paper, we address the problem of how to replace byffers in multimedia database systems with time-varying skewed data access. The access pattern in the multimedia database system to support audio-on-demand and video-on-demand services is generally skewed with a few popular objects. In addition the access pattern of the skewed objects has a time-varying property. In such situations, our analysis indicates that conventional LRU(Least Recently Used) and LFU(Least Frequently Used) schemes for buffer replacement are not suited. We propose a new buffer replacement algorithm(ABRN: Adaptive Buffer Replacement using Neural Network) using a neural network for multimedia database system with time-varing skewed data access. The major role of our neural network classifies multimedia objects into two classes: a hot set frequently accessed with great popularity and a cold set randomly accessed with low popularity. For the classification, the inter-arrival time values of sample objects are employed to train the neural network. Our algorithm partitions buffers into two regions to combine the best properties of LRU and LFU. One region, which contains the hotset objects, is managed by LFU replacement and the other region, which contains the cold set objects, is managed by LRU replacement. We performed simulation experiments in an actual environment with time-varying skewed data access to compare our algorithm provides better performance as compared to the other algorithm. Good performance of the neural network-based replacement scheme means that this new approach can be also suited as an alternative to the existing page replacement and prefetching algorithm in virtual memory systems.

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