Article

Energy conservation in heterogeneous server clusters

Authors:

Enrique V. Carrera,

Wagner Meira, Jr.,

Ricardo BianchiniAuthors Info & Claims

PPoPP '05: Proceedings of the tenth ACM SIGPLAN symposium on Principles and practice of parallel programming

Pages 186 - 195

https://doi.org/10.1145/1065944.1065969

Published: 15 June 2005 Publication History

Abstract

The previous research on cluster-based servers has focused on homogeneous systems. However, real-life clusters are almost invariably heterogeneous in terms of the performance, capacity, and power consumption of their hardware components. In this paper, we argue that designing efficient servers for heterogeneous clusters requires defining an efficiency metric, modeling the different types of nodes with respect to the metric, and searching for request distributions that optimize the metric. To concretely illustrate this process, we design a cooperative Web server for a heterogeneous cluster that uses modeling and optimization to minimize the energy consumed per request. Our experimental results for a cluster comprised of traditional and blade nodes show that our server can consume 42% less energy than an energy-oblivious server, with only a negligible loss in throughput. The results also show that our server conserves 45% more energy than an energy-conscious server that was previously proposed for homogeneous clusters.

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Cited By

Manumachu RKhaleghzadeh HLastovetsky A(2023)Acceleration of Bi-Objective Optimization of Data-Parallel Applications for Performance and Energy on Heterogeneous Hybrid PlatformsIEEE Access10.1109/ACCESS.2023.325868411(27226-27245)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3258684
Zhu GHua XYu GChai Z(2022)SNN Simulation Performance Prediction: A Nonempirical MethodJournal of Circuits, Systems and Computers10.1142/S021812662250183331:10Online publication date: 30-Mar-2022
https://doi.org/10.1142/S0218126622501833
Tsakalidou VMitsou PPapakostas G(2022)Machine Learning for Cloud Resources Management—An OverviewComputer Networks and Inventive Communication Technologies10.1007/978-981-19-3035-5_67(903-915)Online publication date: 14-Oct-2022
https://doi.org/10.1007/978-981-19-3035-5_67
Show More Cited By

Index Terms

Energy conservation in heterogeneous server clusters
1. Software and its engineering
  1. Software creation and management
    1. Designing software
  2. Software organization and properties
    1. Contextual software domains
      1. Operating systems

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Published In

cover image ACM Conferences

PPoPP '05: Proceedings of the tenth ACM SIGPLAN symposium on Principles and practice of parallel programming

June 2005

310 pages

ISBN:1595930809

DOI:10.1145/1065944

General Chair:
Keshav Pingali
Cornell University
,
Program Chairs:
Katherine Yelick
University of California, Berkeley and LBNL
,
Andrew Grimshaw
University of Virginia

Copyright © 2005 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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Publication History

Published: 15 June 2005

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PPoPP05: ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming 2005

June 15 - 17, 2005

IL, Chicago, USA

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Overall Acceptance Rate 230 of 1,014 submissions, 23%

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Cited By

Manumachu RKhaleghzadeh HLastovetsky A(2023)Acceleration of Bi-Objective Optimization of Data-Parallel Applications for Performance and Energy on Heterogeneous Hybrid PlatformsIEEE Access10.1109/ACCESS.2023.325868411(27226-27245)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3258684
Zhu GHua XYu GChai Z(2022)SNN Simulation Performance Prediction: A Nonempirical MethodJournal of Circuits, Systems and Computers10.1142/S021812662250183331:10Online publication date: 30-Mar-2022
https://doi.org/10.1142/S0218126622501833
Tsakalidou VMitsou PPapakostas G(2022)Machine Learning for Cloud Resources Management—An OverviewComputer Networks and Inventive Communication Technologies10.1007/978-981-19-3035-5_67(903-915)Online publication date: 14-Oct-2022
https://doi.org/10.1007/978-981-19-3035-5_67
Chen MChau SChen MChau S(2022)Conclusion and ExtensionsOnline Capacity Provisioning for Energy-Efficient Datacenters10.1007/978-3-031-11549-3_7(57-67)Online publication date: 20-Oct-2022
https://doi.org/10.1007/978-3-031-11549-3_7
Chen MChau SChen MChau S(2022)IntroductionOnline Capacity Provisioning for Energy-Efficient Datacenters10.1007/978-3-031-11549-3_1(1-9)Online publication date: 20-Oct-2022
https://doi.org/10.1007/978-3-031-11549-3_1
Khaleghzadeh HFahad MShahid AManumachu RLastovetsky A(2021)Bi-Objective Optimization of Data-Parallel Applications on Heterogeneous HPC Platforms for Performance and Energy Through Workload DistributionIEEE Transactions on Parallel and Distributed Systems10.1109/TPDS.2020.302733832:3(543-560)Online publication date: 1-Mar-2021
https://doi.org/10.1109/TPDS.2020.3027338
Shukla SGhosal DFarrens M(2021)Understanding and Leveraging Cluster Heterogeneity for Efficient Execution of Cloud Services2021 IEEE 10th International Conference on Cloud Networking (CloudNet)10.1109/CloudNet53349.2021.9657128(56-64)Online publication date: 8-Nov-2021
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Shahid AFahad MManumachu RLastovetsky A(2021)Energy Predictive Models of Computing: Theory, Practical Implications and Experimental Analysis on Multicore ProcessorsIEEE Access10.1109/ACCESS.2021.30751399(63149-63172)Online publication date: 2021
https://doi.org/10.1109/ACCESS.2021.3075139
Shahid AFahad MManumachu RLastovetsky A(2020)A Comparative Study of Techniques for Energy Predictive Modeling Using Performance Monitoring Counters on Modern Multicore CPUsIEEE Access10.1109/ACCESS.2020.30138128(143306-143332)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2020.3013812
Fahad MShahid AManumachu RLastovetsky A(2020)Accurate Energy Modelling of Hybrid Parallel Applications on Modern Heterogeneous Computing Platforms Using System-Level MeasurementsIEEE Access10.1109/ACCESS.2020.29949538(93793-93829)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2020.2994953
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