Neural Network based Mobility aware Prefetch Caching and Replacement Strategies in Mobile Environment

Abstract—The Location Based Services (LBS) have ushered the way mobile applications access and manage Mobile Database System (MDS). Caching frequently accessed data into the mobile database environment, is an effective technique to improve the MDS performance. The cache size limitation enforces an optimized cache replacement algorithm to find a suitable subset of items for eviction from the cache. In wireless environment mobile clients move freely from one location to another and their access pattern exhibits temporal-spatial locality. To ensure efficient cache utilization, it is important to consider the movement direction, current and future location, cache invalidation and optimized prefetching for mobile clients when performing cache replace-ment. This paper proposes a Neural Network based Mobility aware Prefetch Caching and Replacement policy (NNMPCR) in Mobile Environment to manage LBS data. The NNMPCR policy employs a neural network prediction system that is able to capture some of the spatial patterns exhibited by users moving in a wireless environment. It is used to predict the future behavior of the mobile client. A cache-miss-initiated prefetch is used to reduce future misses and valid scope invalidation technique for cache invalidation. This makes the policy adaptive to clients movement behavior and optimizes the performance compared to earlier policies