AHEAD: Adaptable data hardening for on-the-fly hardware error detection during database query processing

Publikation: Beitrag in Buch/Konferenzbericht/Sammelband/GutachtenBeitrag in KonferenzbandBeigetragenBegutachtung

Beitragende

Abstract

We have already known for a long time that hardware components are not perfect and soft errors in terms of single bit flips happen all the time. Up to now, these single bit flips are mainly addressed in hardware using general-purpose protection techniques. However, recent studies have shown that all future hardware components become less and less reliable in total and multi-bit flips are occurring regularly rather than exceptionally. Additionally, hardware aging effects will lead to error models that change during run-time. Scaling hardware-based protection techniques to cover changing multi-bit flips is possible, but this introduces large performance, chip area, and power overheads, which will become non-affordable in the future. To tackle that, an emerging research direction is employing protection techniques in higher software layers like compilers or applications. The available knowledge at these layers can be efficiently used to specialize and adapt protection techniques. Thus, we propose a novel adaptable and on-the-fly hardware error detection approach called AHEAD for database systems in this paper. AHEAD provides configurable error detection in an end-to-end fashion and reduces the overhead (storage and computation) compared to other techniques at this level. Our approach uses an arithmetic error coding technique which allows query processing to completely work on hardened data on the one hand. On the other hand, this enables on-the-fly detection during query processing of (i) errors that modify data stored in memory or transferred on an interconnect and (ii) errors induced during computations. Our exhaustive evaluation clearly shows the benefits of our AHEAD approach.

Details

OriginalspracheEnglisch
TitelSIGMOD '18: Proceedings of the 2018 International Conference on Management of Data
Herausgeber (Verlag)Association for Computing Machinery (ACM), New York
Seiten1619-1634
Seitenumfang16
ISBN (Print)978-1-4503-4703-7
PublikationsstatusVeröffentlicht - 27 Mai 2018
Peer-Review-StatusJa

Publikationsreihe

ReiheMOD: International Conference on Management of Data (SIGMOD)

Konferenz

Titel44th ACM SIGMOD International Conference on Management of Data, SIGMOD 2018
Dauer10 - 15 Juni 2018
StadtHouston
LandUSA/Vereinigte Staaten

Externe IDs

dblp conf/sigmod/KolditzHL0B18
Scopus 85048809259
ORCID /0000-0001-8107-2775/work/142253575

Schlagworte

ASJC Scopus Sachgebiete

Schlagwörter

  • Database systems, Error detection, Query processing, Reliability