2017/03/24 by Yinghao Yu, Yu, Yinghao, Wei Wang +6
Computer Science · #Advanced Data Storage Technologies #Cloud Computing and Resource Management #Distributed #Distributed systems and fault tolerance #FOS: Computer and information sciences #Parallel #and Cluster Computing (cs.DC) #cs.DC
paper · pdf · doi:10.48550/arxiv.1703.08280
9 pages
arxiv created 2017/03/24 · openalex publication_date 2017/03/24 · arxiv updated 2017/03/27 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Memory caches are being aggressively used in today's data-parallel systems such as Spark, Tez, and Piccolo. However, prevalent systems employ rather simple cache management policies--notably the Least Recently Used (LRU) policy--that are oblivious to the application semantics of data dependency, expressed as a directed acyclic graph (DAG). Without this knowledge, memory caching can at best be performed by "guessing" the future data access patterns based on historical information (e.g., the access recency and/or frequency), which frequently results in inefficient, erroneous caching with low hit ratio and a long response time. In this paper, we propose a novel cache replacement policy, Least Reference Count (LRC), which exploits the application-specific DAG information to optimize the cache management. LRC evicts the cached data blocks whose reference count is the smallest. The reference count is defined, for each data block, as the number of dependent child blocks that have not been computed yet. We demonstrate the efficacy of LRC through both empirical analysis and cluster deployments against popular benchmarking workloads. Our Spark implementation shows that, compared with LRU, LRC speeds up typical applications by 60%.