• DocumentCode
    3717179
  • Title

    Benchmarking key-value stores on high-performance storage and interconnects for web-scale workloads

  • Author

    Dipti Shankar;Xiaoyi Lu;Md. Wasi-ur-Rahman;Nusrat Islam;Dhabaleswar K. Panda

  • Author_Institution
    Department of Computer Science and Engineering, The Ohio State University
  • fYear
    2015
  • Firstpage
    539
  • Lastpage
    544
  • Abstract
    Leveraging a distributed key-value based caching layer has proven to be invaluable for scalable data-intensive web applications. With the emergence of high-performance storage (e.g. SSD) and interconnects (e.g. InfiniBand) on modern clusters, several efforts are being made to design high-performance key-value stores that can operate well with `RAM+SSD´ hybrid storage architecture. This has made it essential for us to design micro-benchmarks that are tailored to evaluate these upcoming, hybrid designs. In this paper, we study popular web-scale and cloud serving workloads, to identify different application-specific aspects, including commonly occurring data request distributions, update patterns, and environmental factors, that affect the performance of hybrid key-value stores. Based on these characterization studies, we propose a micro-benchmark suite that can be used to study high-performance, hybrid key-value stores on modern clusters, from the perspectives of both the application and the key-value store. We demonstrate its ease-of-use using database-integrated and stand-alone execution modes. Performance evaluations with different Memcached distributions, such as SSD-Assisted RDMA-Memcached, fatcache, and twemcache, over different networks/protocols, show that `SSD+RDMA´ can significantly enhance the performance of Memcached for various read-only and read-heavy workloads, that are representative of several common web-scale workloads.
  • Keywords
    "Benchmark testing","Databases","Servers","Internet","Environmental factors","Twitter"
  • Publisher
    ieee
  • Conference_Titel
    Big Data (Big Data), 2015 IEEE International Conference on
  • Type

    conf

  • DOI
    10.1109/BigData.2015.7363797
  • Filename
    7363797