- Titel
- Adaptive Energy-Control for In-Memory Database Systems
- Zitierfähige Url:
- https://nbn-resolving.org/urn:nbn:de:bsz:14-qucosa2-791673
- Konferenz
- SIGMOD/PODS '18: International Conference on Management of Data. Houston, 10. - 15. Juni 2018
- Quellenangabe
- SIGMOD '18 : Proceedings of the 2018 International Conference on Management of Data
Herausgeber: Gautam Das
Herausgeber: Christopher Jermaine
Erscheinungsort: New York
Verlag: ACM
Erscheinungsjahr: 2018
Seiten: 351-364
ISBN: 978-1-4503-4703-7 - Erstveröffentlichung
- 2018
- Abstract (EN)
- The ever-increasing demand for scalable database systems is limited by their energy consumption, which is one of the major challenges in research today. While existing approaches mainly focused on transaction-oriented disk-based database systems, we are investigating and optimizing the energy consumption and performance of data-oriented scale-up in-memory database systems that make heavy use of the main power consumers, which are processors and main memory. We give an in-depth energy analysis of a current mainstream server system and show that modern processors provide a rich set of energy-control features, but lack the capability of controlling them appropriately, because of missing application-specific knowledge. Thus, we propose the Energy-Control Loop (ECL) as an DBMS-integrated approach for adaptive energy-control on scale-up in-memory database systems that obeys a query latency limit as a soft constraint and actively optimizes energy efficiency and performance of the DBMS. The ECL relies on adaptive workload-dependent energy profiles that are continuously maintained at runtime. In our evaluation, we observed energy savings ranging from 20% to 40% for a real-world load profile.
- Andere Ausgabe
- Link zum Artikel, der zuerst in der ACM Digital Library erschienen ist.
DOI: 10.1145/3183713.3183756 - Freie Schlagwörter (DE)
- In-Memory, Datenbanksysteme, Energieeffizienz, Adaptivität
- Freie Schlagwörter (EN)
- in-memory, database systems, energy efficiency, adaptivity
- Klassifikation (DDC)
- 004
- Verlag
- ACM, New York
- Version / Begutachtungsstatus
- angenommene Version / Postprint / Autorenversion
- URN Qucosa
- urn:nbn:de:bsz:14-qucosa2-791673
- Veröffentlichungsdatum Qucosa
- 30.05.2022
- Dokumenttyp
- Konferenzbeitrag
- Sprache des Dokumentes
- Englisch
- Lizenz / Rechtehinweis
