A Novel Adaptive Database Cache Optimization Algorithm Based on Predictive Working Sets in Cloud Environment

Abstract

Many applications are hosted on cloud databases where several applications share the same database instance. Such database management system exhibits periodic behavior in terms of data references. For example, U.S. customers access data at a particular time while Japanese customers access data at some other time. The periodicity of data references is translated into periodic block references. This periodicity of block references can be used to improve cache performance by improving the block replacement policy. This paper proposes a novel cache replacement policy by considering periodicity of references of database blocks. It also proposes how to estimate the probability of a block reference in a specified time interval using “Parzen Windows,” and determines a working set based on this probability and defines the cache management policy using this working set. The experimentation shows considerable improvement of the hit ratio as the performance measure of the buffer cache management as compared with the other state-of-art algorithms.