Temporal Based Intelligent LRU Cache Construction

Abstract

In the Android platform, the cache-slots store applications upon their launch, which it later uses for prefetching. The Least Recently Used (LRU) based caching algorithm which governs these cache-slots can fail to maintain essential applications in the slot, especially in scenarios like memory-crunch, temporal-burst or volatile environment situations. The construction of these cache-slots can be ameliorated by selectively storing user critical applications before their launch. This reform would require a successful forecast of the user-app-launch pattern using intelligent machine learning agents without hindering the smooth execution of parallel processes. In this paper, we propose a sophisticated Temporal based Intelligent Process Management (TIPM) system, which learns to predict a Smart Application List (SAL) based on the usage pattern. Using SAL, we construct Intelligent LRU cache-slots, that retains essential user applications in the memory and provide improved launch rates. Our experimental results from testing TIPM with different users demonstrate significant improvement in cache-hit rate (95%) and yielding a gain of 26% to the current baseline (LRU), thereby making it a valuable enhancement to the platform.