Optimizing Analytical Query Processing on Disaggregated Hardware

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

Beitragende

Abstract

In a world of ever-growing amounts of data, hardware-scalability and energy-efficiency become more important with every year. Traditional scale-up and scale-out database management systems (DBMS) struggle to scale well with their growing analytical workloads. Due to this, the emerging technology of disaggregated hardware becomes more and more popular. However, there is no free ride and specific challenges arise. In my PhD topic, I want (i) to look into these challenges for analytical query workloads on disaggregated hardware and (ii) to provide appropriate solutions. First initial results concerning data movement are promising and show the potential of adapted solutions.

Details

OriginalspracheEnglisch
TitelEDBT/ICDT 2023 Workshops
Redakteure/-innenGeorge Fletcher, Verena Kantere
Seitenumfang4
Band3379
PublikationsstatusVeröffentlicht - 2023
Peer-Review-StatusNein

Publikationsreihe

ReiheCEUR Workshop Proceedings
Band3379
ISSN1613-0073

Externe IDs

Scopus 85158928845

Schlagworte

Forschungsprofillinien der TU Dresden

Fächergruppen, Lehr- und Forschungsbereiche, Fachgebiete nach Destatis

Ziele für nachhaltige Entwicklung

ASJC Scopus Sachgebiete

Schlagwörter

  • RDMA, Disaggregated Memory, Disaggregated Hardware