An Adaptive Block Management Scheme using On-Line Detection of Block Reference Patterns

Abstract

Recent research has shown that near-optimal performance can be achieved by adaptive block replacement policies that use user-level hints regarding the block reference pattern. However, obtaining user-level hints requires considerable effort from users, making it difficult to apply adaptive replacement policies to diverse kinds of applications. We propose a new adaptive block management scheme that we call DEAR (DEtection-based Adaptive Replacement) which makes online detections of block reference patterns of applications using decision trees without user intervention. Based on the detected reference pattern, DEAR applies an appropriate replacement policy to each application. This scheme is suitable for buffer management in systems such as multimedia servers where data reference patterns of applications may be diverse. Results from trace-driven simulations show that the DEAR scheme can detect the reference patterns of applications and reduce the miss ratio by up to 15% compared to the LRU (least recently used) policy.