research-article

Wimpy node clusters: what about non-wimpy workloads?

Authors:

Jignesh M. Patel,

Srinath ShankarAuthors Info & Claims

DaMoN '10: Proceedings of the Sixth International Workshop on Data Management on New Hardware

Pages 47 - 55

https://doi.org/10.1145/1869389.1869396

Published: 07 June 2010 Publication History

Abstract

The high cost associated with powering servers has introduced new challenges in improving the energy efficiency of clusters running data processing jobs. Traditional high-performance servers are largely energy inefficient due to various factors such as the over-provisioning of resources. The increasing trend to replace traditional high-performance server nodes with low-power low-end nodes in clusters has recently been touted as a solution to the cluster energy problem. However, the key tacit assumption that drives such a solution is that the proportional scale-out of such low-power cluster nodes results in constant scaleup in performance. This paper studies the validity of such an assumption using measured price and performance results from a low-power Atom-based node and a traditional Xeon-based server and a number of published parallel scaleup results. Our results show that in most cases, computationally complex queries exhibit disproportionate scaleup characteristics which potentially makes scale-out with low-end nodes an expensive and lower performance solution.

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Cited By

Zhang LFu ZShi BLi XLai RYang CZhou AMa XWang SXu MBagchi SZhang Y(2024)More is differentProceedings of the 2024 USENIX Conference on Usenix Annual Technical Conference10.5555/3691992.3692009(285-302)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.5555/3691992.3692009
Wilkins GKeshav SMortier R(2024)Hybrid Heterogeneous Clusters Can Lower the Energy Consumption of LLM Inference WorkloadsProceedings of the 15th ACM International Conference on Future and Sustainable Energy Systems10.1145/3632775.3662830(506-513)Online publication date: 4-Jun-2024
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Olivier PMehrab AErrabelly SLankes SKaraoui MLyerly RKim SBarbalace ARavindran B(2024)HEXO: Offloading Long-Running Compute- and Memory-Intensive Workloads on Low-Cost, Low-Power Embedded SystemsIEEE Transactions on Cloud Computing10.1109/TCC.2024.348217812:4(1415-1432)Online publication date: Oct-2024
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Wimpy node clusters: what about non-wimpy workloads?

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cover image ACM Conferences

DaMoN '10: Proceedings of the Sixth International Workshop on Data Management on New Hardware

June 2010

56 pages

ISBN:9781450301893

DOI:10.1145/1869389

Copyright © 2010 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 07 June 2010

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SIGMOD/PODS '10: International Conference on Management of Data

June 7, 2010

Indiana, Indianapolis

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Overall Acceptance Rate 94 of 127 submissions, 74%

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Cited By

Zhang LFu ZShi BLi XLai RYang CZhou AMa XWang SXu MBagchi SZhang Y(2024)More is differentProceedings of the 2024 USENIX Conference on Usenix Annual Technical Conference10.5555/3691992.3692009(285-302)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.5555/3691992.3692009
Wilkins GKeshav SMortier R(2024)Hybrid Heterogeneous Clusters Can Lower the Energy Consumption of LLM Inference WorkloadsProceedings of the 15th ACM International Conference on Future and Sustainable Energy Systems10.1145/3632775.3662830(506-513)Online publication date: 4-Jun-2024
https://dl.acm.org/doi/10.1145/3632775.3662830
Olivier PMehrab AErrabelly SLankes SKaraoui MLyerly RKim SBarbalace ARavindran B(2024)HEXO: Offloading Long-Running Compute- and Memory-Intensive Workloads on Low-Cost, Low-Power Embedded SystemsIEEE Transactions on Cloud Computing10.1109/TCC.2024.348217812:4(1415-1432)Online publication date: Oct-2024
https://doi.org/10.1109/TCC.2024.3482178
Guo BYu JYang DLeng HLiao B(2022)Energy-Efficient Database Systems: A Systematic SurveyACM Computing Surveys10.1145/353822555:6(1-53)Online publication date: 7-Dec-2022
https://dl.acm.org/doi/10.1145/3538225
Crotty AGalakatos ALuckett CCetintemel U(2021)The Case for In-Memory OLAP on "Wimpy" Nodes2021 IEEE 37th International Conference on Data Engineering (ICDE)10.1109/ICDE51399.2021.00069(732-743)Online publication date: Apr-2021
https://doi.org/10.1109/ICDE51399.2021.00069
Jiang QLee YZomaya A(2020)The Power of ARM64 in Public Clouds2020 20th IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing (CCGRID)10.1109/CCGrid49817.2020.00-47(459-468)Online publication date: May-2020
https://doi.org/10.1109/CCGrid49817.2020.00-47
Olivier PMehrab ALankes SKaraoui MLyerly RRavindran BWeissman JButt ASmirni E(2019)HEXOProceedings of the 28th International Symposium on High-Performance Parallel and Distributed Computing10.1145/3307681.3325408(85-96)Online publication date: 17-Jun-2019
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Jia ZGao WShi YMcKee SJi ZZhan JWang LZhang L(2019)Understanding Processors Design Decisions for Data Analytics in Homogeneous Data CentersIEEE Transactions on Big Data10.1109/TBDATA.2017.27587925:1(81-94)Online publication date: 1-Mar-2019
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Niinimäki MAbaunza FNiemi TThanisch PKommeri J(2018)Energy-Efficient Query Processing in a Combined Database and Web Service EnvironmentGreen Computing Strategies for Competitive Advantage and Business Sustainability10.4018/978-1-5225-5017-4.ch004(62-88)Online publication date: 2018
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Barroso LHölzle URanganathan P(2018)The Datacenter as a Computer: Designing Warehouse-Scale Machines, Third EditionSynthesis Lectures on Computer Architecture10.2200/S00874ED3V01Y201809CAC04613:3(i-189)Online publication date: 24-Oct-2018
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