AHEAD: Adaptable data hardening for on-the-fly hardware error detection during database query processing
Publikation: Beitrag in Buch/Konferenzbericht/Sammelband/Gutachten › Beitrag in Konferenzband › Beigetragen › Begutachtung
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
Originalsprache | Englisch |
---|---|
Titel | SIGMOD '18: Proceedings of the 2018 International Conference on Management of Data |
Herausgeber (Verlag) | Association for Computing Machinery (ACM), New York |
Seiten | 1619-1634 |
Seitenumfang | 16 |
ISBN (Print) | 978-1-4503-4703-7 |
Publikationsstatus | Veröffentlicht - 27 Mai 2018 |
Peer-Review-Status | Ja |
Publikationsreihe
Reihe | MOD: International Conference on Management of Data (SIGMOD) |
---|
Konferenz
Titel | 44th ACM SIGMOD International Conference on Management of Data, SIGMOD 2018 |
---|---|
Dauer | 10 - 15 Juni 2018 |
Stadt | Houston |
Land | USA/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