NDLI: Dynamic physiological partitioning on a shared-nothing database cluster

Content Provider	IEEE Xplore Digital Library
Author	Schall, D. Harder, T.
Copyright Year	2015
Description	Author affiliation: Databases & Inf. Syst. Group, Univ. of Kaiserslautern, Kaiserslautern, Germany (Schall, D.; Harder, T.)
Abstract	Traditional database management systems (DBMSs) running on powerful single-node servers are usually over-provisioned for most of their daily workloads and, because they do not show good-enough energy proportionality, waste a lot of energy while underutilized. A cluster of small (wimpy) servers, where its size can be dynamically adjusted to the current workload, offers better energy characteristics for those workloads. Yet, data migration, necessary to balance utilization among the nodes, is a non-trivial and time-consuming task that may consume the energy saved. For this reason, a sophisticated and easy to adjust partitioning scheme fostering dynamic reorganization is needed. In this paper, we adapt a technique originally created for SMP systems, called physiological partitioning, to distribute data among nodes that allows to easily repartition data without interrupting transactions. We dynamically partition DB tables based on the nodes' utilization and given energy constraints and compare our approach with physical partitioning and logical partitioning methods. To quantify possible energy saving and its conceivable drawback on query runtimes, we evaluate our implementation on an experimental cluster and compare the results w.r.t. performance and energy consumption. Depending on the workload, we can substantially save energy without sacrificing too much performance.
Starting Page	1095
Ending Page	1106
File Size	443768
Page Count	12
File Format	PDF
ISBN	9781479979646
DOI	10.1109/ICDE.2015.7113359
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-04-13
Publisher Place	South Korea
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Servers Throughput Random access memory Query processing Hardware Physiology
Content Type	Text
Resource Type	Article

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