2020/08/31 by Stella Mikrou, Anastasios Papagiannis, Mikrou, Stella +7
Computer Science · #Advanced Data Storage Technologies #Caching and Content Delivery #Cloud Computing and Resource Management #Distributed #FOS: Computer and information sciences #Parallel #Performance (cs.PF) #and Cluster Computing (cs.DC) #cs.DC #cs.PF
paper · pdf · doi:10.48550/arxiv.2008.13402
arxiv created 2020/08/31 · openalex publication_date 2020/08/31 · arxiv updated 2020/09/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
With the current rate of data growth, processing needs are becoming difficult to fulfill due to CPU power and energy limitations. Data serving systems and especially persistent key-value stores have become a substantial part of data processing stacks in the data center, providing access to massive amounts of data for applications and services. Key-value stores exhibit high CPU and I/O overheads because of their constant need to reorganize data on the devices. In this paper, we examine the efficiency of two key-value stores on four servers of different generations and with different CPU architectures. We use RocksDB, a key-value that is deployed widely, e.g. in Facebook, and Kreon, a research key-value store that has been designed to reduce CPU overhead. We evaluate their behavior and overheads on an ARM-based microserver and three different generations of x86 servers. Our findings show that microservers have better power efficiency in the range of 0.68-3.6x with a comparable tail latency.