StoRM: A fast transactional dataplane for remote data structures

Stanko Novakovic, Yizhou Shan, Aasheesh Kolli, Michael Cui, Yiying Zhang, Haggai Eran, Boris Pismenny, Liran Liss, Michael Wei, Dan Tsafrir, Marcos Aguilera

Research output: Chapter in Book/Report/Conference proceedingConference contribution

9 Scopus citations

Abstract

RDMA technology enables a host to access the memory of a remote host without involving the remote CPU, improving the performance of distributed in-memory storage systems. Previous studies argued that RDMA suffers from scalability issues, because the NIC’s limited resources are unable to simultaneously cache the state of all the concurrent network streams. These concerns led to various software-based proposals to reduce the size of this state by trading off performance. We revisit these proposals and show that they no longer apply when using newer RDMA NICs in rack-scale environments. In particular, we find that one-sided remote memory primitives lead to better performance as compared to the previously proposed unreliable datagram and kernel-based stacks. Based on this observation, we design and implement Storm, a transactional dataplane utilizing one-sided read and write-based RPC primitives. We show that Storm outperforms eRPC, FaRM, and LITE by 3.3x, 3.6x, and 17.1x, respectively, on an InfiniBand cluster with Mellanox ConnectX-4 NICs.

Original languageEnglish (US)
Title of host publicationSYSTOR 2019 - Proceedings of the 12th ACM International Systems and Storage Conference
PublisherAssociation for Computing Machinery, Inc
Pages97-108
Number of pages12
ISBN (Electronic)9781450367493
DOIs
StatePublished - May 22 2019
Event12th ACM International Systems and Storage Conference, SYSTOR 2019 - Haifa, Israel
Duration: Jun 3 2019Jun 5 2019

Publication series

NameSYSTOR 2019 - Proceedings of the 12th ACM International Systems and Storage Conference

Conference

Conference12th ACM International Systems and Storage Conference, SYSTOR 2019
CountryIsrael
CityHaifa
Period6/3/196/5/19

All Science Journal Classification (ASJC) codes

  • Hardware and Architecture
  • Electrical and Electronic Engineering
  • Computer Science Applications
  • Software

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