2020/04/09 by Sudarsun Kannan, Yujie Ren, Kannan, Sudarsun +3
Computer Science · #Advanced Data Storage Technologies #Cloud Computing and Resource Management #Distributed and Parallel Computing Systems #FOS: Computer and information sciences #Operating Systems (cs.OS) #cs.OS
paper · pdf · doi:10.48550/arxiv.2004.04760
arxiv created 2020/04/09 · openalex publication_date 2020/04/09 · arxiv updated 2020/04/13 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Software-controlled heterogeneous memory systems have the potential to improve performance, efficiency, and cost tradeoffs in emerging systems. Delivering on this promise requires an efficient operating system (OS) mechanisms and policies for data management. Unfortunately, modern OSes do not support efficient tiering of data between heterogeneous memories. While this problem is known (and is being studied) for application-level data pages, the question of how best to tier OS kernel objects has largely been ignored. We show that careful kernel object management is vital to the performance of software-controlled tiered memory systems. We find that the state-of-the-art OS page management research leaves considerable performance on the table by overlooking how best to tier, migrate, and manage kernel objects like inodes, dentry caches, journal blocks, network socket buffers, etc., associated with the filesystem and networking stack. In response, we characterize hotness, reuse, and liveness properties of kernel objects to develop appropriate tiering/migration mechanisms and policies. We evaluate our proposal using a real-system emulation framework on large-scale workloads like RocksDB, Redis, Cassandra, and Spark and achieve 1.4X to 4X higher throughput compared to the prior art.