High-performance main-memory database systems and modern virtualization: Friends or foes?

Tobias Mühlbauer, Wolf Rödigery, Andreas Kipf, Alfons Kemper, Thomas Neumann

Publikation: Beitrag in Buch/Bericht/KonferenzbandKonferenzbeitragBegutachtung

4 Zitate (Scopus)

Abstract

Virtualization owes its popularity mainly to its ability to consolidate software systems from many servers into a single server without sacrificing the desirable isolation between applications. This not only reduces the total cost of ownership, but also enables rapid deployment of complex software and application-agnostic live migration between servers for load balancing, high-availability, and fault-tolerance. However, virtualization is no free lunch. To achieve isolation, virtualization environments need to add an additional layer of abstraction between the bare metal hardware and the application. This inevitably introduces a performance overhead. High-performance main-memory database systems are specifically susceptible to additional software abstractions as they are closely optimized and tuned for the underlying hardware. In this work, we analyze in detail how much overhead modern virtualization options introduce for high-performance main-memory database systems. We evaluate and compare the performance of HyPer and MonetDB under three modern virtualization environments for analytical as well as transactional workloads. Our experiments show that the overhead depends on the system and virtualization environment being used. We further show that mainmemory database systems can be effciently deployed in virtualized cloud environments such as the Google Compute Engine and that "friendship" between modern virtualization and main-memory database systems is indeed possible.

OriginalspracheEnglisch
TitelProceedings of the 4th Workshop on Data analytics at sCale, DanaC 2015 - in conjunction with ACM SIGMOD/PODS Conference 2015
Redakteure/-innenAsterios Katsifodimos
Herausgeber (Verlag)Association for Computing Machinery, Inc
ISBN (elektronisch)9781450337243
DOIs
PublikationsstatusVeröffentlicht - 31 Mai 2015
Veranstaltung4th Workshop on Data analytics at sCale, DanaC 2015 - Melbourne, Australien
Dauer: 31 Mai 2015 → …

Publikationsreihe

NameProceedings of the 4th Workshop on Data analytics at sCale, DanaC 2015 - in conjunction with ACM SIGMOD/PODS Conference 2015

Konferenz

Konferenz4th Workshop on Data analytics at sCale, DanaC 2015
Land/GebietAustralien
OrtMelbourne
Zeitraum31/05/15 → …

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