{"title":"Temporal Based Intelligent LRU Cache Construction","authors":"Pavan Nittur, Anuradha Kanukotla, Narendra Mutyala","doi":"10.1109/HiPC50609.2020.00045","DOIUrl":null,"url":null,"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.","PeriodicalId":375004,"journal":{"name":"2020 IEEE 27th International Conference on High Performance Computing, Data, and Analytics (HiPC)","volume":"111 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 IEEE 27th International Conference on High Performance Computing, Data, and Analytics (HiPC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/HiPC50609.2020.00045","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
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.