Skip to main content
USENIX
  • Conferences
  • Students
Sign in
  • Home
  • Attend
    • Registration Information
    • Registration Discounts
    • Venue, Hotel, and Travel
    • Students and Grants
    • Co-located Events
      • SOUPS 2016
      • HotCloud '16
      • HotStorage '16
  • Program
    • At a Glance
    • Technical Sessions
  • Activities
    • Birds-of-a-Feather Sessions
    • Poster Session
  • Participate
    • Instructions for Authors and Speakers
    • Call for Papers
    • Call for Practitioner Talks
  • Sponsorship
  • About
    • Organizers
    • Help Promote!
    • Questions
    • Past Conferences
  • Home
  • Attend
  • Program
  • Activities
  • Participate
  • Sponsorship
  • About

sponsors

Gold Sponsor
Gold Sponsor
Gold Sponsor
Gold Sponsor
Silver Sponsor
Silver Sponsor
Silver Sponsor
Silver Sponsor
Media Sponsor
Media Sponsor
Media Sponsor
Media Sponsor
Media Sponsor
Media Sponsor
Media Sponsor
Media Sponsor
Media Sponsor
Media Sponsor
Media Sponsor
Industry Partner
Industry Partner
Industry Partner

help promote

USENIX ATC '16

Get
Help Promote graphics!

connect with us


  •  Twitter
  •  Facebook
  •  LinkedIn
  •  Google+
  •  YouTube

twitter

Tweets by @usenix

usenix conference policies

  • Event Code of Conduct
  • Conference Network Policy
  • Statement on Environmental Responsibility Policy

You are here

Home ยป Samsara: Efficient Deterministic Replay in Multiprocessor Environments with Hardware Virtualization Extensions
Tweet

connect with us

Samsara: Efficient Deterministic Replay in Multiprocessor Environments with Hardware Virtualization Extensions

Authors: 

Shiru Ren, Le Tan, Chunqi Li, and Zhen Xiao, Peking University; Weijia Song, Cornell University

Abstract: 

Deterministic replay, which provides the ability to travel backward in time and reconstruct the past execution flow of a multiprocessor system, has many prominent applications. Prior research in this area can be classified into two categories: hardware-only schemes and software-only schemes. While hardware-only schemes deliver high performance, they require significant modifications to the existing hardware which makes them difficult to deploy in real systems. In contrast, software-only schemes work on commodity hardware, but suffer from excessive performance overhead and huge logs caused by tracing every single memory access in the software layer.

In this paper, we present the design and implementation of a novel system, Samsara, which uses the hardware-assisted virtualization (HAV) extensions to achieve efficient and practical deterministic replay without requiring any hardware modification. Unlike prior software schemes which trace every single memory access to record interleaving, Samsara leverages the HAV extensions on commodity processors to track the read-set and write-set for implementing a chunk-based recording scheme in software. By doing so, we avoid all memory access detections, which is a major source of overhead in prior works. We implement and evaluate our system in KVM on commodity Intel Haswell processor. Evaluation results show that compared with prior software-only schemes, Samsara significantly reduces the log file size to 1/70th on average, and further reduces the recording overhead from about 10x, reported by state-of-the-art works, to 2.3x on average.

Shiru Ren, Peking University

Le Tan, Peking University

Chunqi Li, Peking University

Zhen Xiao, Peking University

Weijia Song, Cornell University

Open Access Media

USENIX is committed to Open Access to the research presented at our events. Papers and proceedings are freely available to everyone once the event begins. Any video, audio, and/or slides that are posted after the event are also free and open to everyone. Support USENIX and our commitment to Open Access.

BibTeX
@inproceedings {196257,
author = {Shiru Ren and Le Tan and Chunqi Li and Zhen Xiao and Weijia Song},
title = {Samsara: Efficient Deterministic Replay in Multiprocessor Environments with Hardware Virtualization Extensions},
booktitle = {2016 USENIX Annual Technical Conference (USENIX ATC 16)},
year = {2016},
isbn = {978-1-931971-30-0},
address = {Denver, CO},
pages = {551--564},
url = {https://www.usenix.org/conference/atc16/technical-sessions/presentation/ren},
publisher = {USENIX Association},
month = jun,
}
Download
Ren PDF
View the slides

Presentation Audio

MP3 Download

Download Audio

  • Log in or    Register to post comments

Gold Sponsors

Silver Sponsors

Media Sponsors & Industry Partners

© USENIX

  • Privacy Policy
  • Contact Us