research-article

Cutting the electric bill for internet-scale systems

Authors:

Asfandyar Qureshi,

Hari Balakrishnan,

Bruce MaggsAuthors Info & Claims

ACM SIGCOMM Computer Communication Review, Volume 39, Issue 4

Pages 123 - 134

https://doi.org/10.1145/1594977.1592584

Published: 16 August 2009 Publication History

Abstract

Energy expenses are becoming an increasingly important fraction of data center operating costs. At the same time, the energy expense per unit of computation can vary significantly between two different locations. In this paper, we characterize the variation due to fluctuating electricity prices and argue that existing distributed systems should be able to exploit this variation for significant economic gains. Electricity prices exhibit both temporal and geographic variation, due to regional demand differences, transmission inefficiencies, and generation diversity. Starting with historical electricity prices, for twenty nine locations in the US, and network traffic data collected on Akamai's CDN, we use simulation to quantify the possible economic gains for a realistic workload. Our results imply that existing systems may be able to save millions of dollars a year in electricity costs, by being cognizant of locational computation cost differences.

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Cited By

Ali AÖzkasap Ö(2024)Spatial and thermal aware methods for efficient workload management in distributed data centersFuture Generation Computer Systems10.1016/j.future.2023.12.006153(360-374)Online publication date: Apr-2024
https://doi.org/10.1016/j.future.2023.12.006
Zhang PYang PZhao ZLai CLai LShahidehpour M(2023)A Framework for Several Electricity Retailers Cooperatively Implement Demand Response to Distributed Data CenterIEEE Transactions on Smart Grid10.1109/TSG.2022.318983514:1(277-289)Online publication date: Jan-2023
https://doi.org/10.1109/TSG.2022.3189835
Lou JTang ZJia W(2023)Energy-Efficient Joint Task Assignment and Migration in Data Centers: A Deep Reinforcement Learning ApproachIEEE Transactions on Network and Service Management10.1109/TNSM.2022.321020420:2(961-973)Online publication date: 1-Jun-2023
https://dl.acm.org/doi/10.1109/TNSM.2022.3210204
Show More Cited By

Index Terms

Cutting the electric bill for internet-scale systems
1. Computer systems organization
  1. Architectures
    1. Distributed architectures
2. Software and its engineering
  1. Software organization and properties
    1. Software system structures
      1. Distributed systems organizing principles

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Information

Published In

cover image ACM SIGCOMM Computer Communication Review

ACM SIGCOMM Computer Communication Review Volume 39, Issue 4

SIGCOMM '09

October 2009

325 pages

ISSN:0146-4833

DOI:10.1145/1594977

Issue’s Table of Contents

SIGCOMM '09: Proceedings of the ACM SIGCOMM 2009 conference on Data communication
August 2009
340 pages
ISBN:9781605585949
DOI:10.1145/1592568
General Chairs:
Pablo Rodriguez
Telefonica Research, Spain
,
Ernst Biersack
Eurecom, France
,
Program Chairs:
Konstantina Papagiannaki
Intel Labs Pittsburgh, USA
,
Luigi Rizzo
Università di Pisa, Italy

Copyright © 2009 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]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 16 August 2009

Published in SIGCOMM-CCR Volume 39, Issue 4

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Cited By

Ali AÖzkasap Ö(2024)Spatial and thermal aware methods for efficient workload management in distributed data centersFuture Generation Computer Systems10.1016/j.future.2023.12.006153(360-374)Online publication date: Apr-2024
https://doi.org/10.1016/j.future.2023.12.006
Zhang PYang PZhao ZLai CLai LShahidehpour M(2023)A Framework for Several Electricity Retailers Cooperatively Implement Demand Response to Distributed Data CenterIEEE Transactions on Smart Grid10.1109/TSG.2022.318983514:1(277-289)Online publication date: Jan-2023
https://doi.org/10.1109/TSG.2022.3189835
Lou JTang ZJia W(2023)Energy-Efficient Joint Task Assignment and Migration in Data Centers: A Deep Reinforcement Learning ApproachIEEE Transactions on Network and Service Management10.1109/TNSM.2022.321020420:2(961-973)Online publication date: 1-Jun-2023
https://dl.acm.org/doi/10.1109/TNSM.2022.3210204
Khalid SAhmad I(2023)Dual Optimization of Revenue and Expense in Geo-Distributed Data Centers Using Smart GridIEEE Transactions on Cloud Computing10.1109/TCC.2022.315098511:2(1622-1635)Online publication date: 1-Apr-2023
https://doi.org/10.1109/TCC.2022.3150985
Han ODing TMu CJia WMa Z(2023)Coordinative Optimization Between Multiple Data Center Operators and a System Operator Based on Two-Level Distributed Scheduling AlgorithmIEEE Internet of Things Journal10.1109/JIOT.2022.318835310:9(7517-7527)Online publication date: 1-May-2023
https://doi.org/10.1109/JIOT.2022.3188353
Mollaeivaneghi SBarbosa JSteinke F(2023)DER Pricing Power in the Presence of Multi-Location Consumers with Load Migration Capabilities2023 IEEE PES Innovative Smart Grid Technologies Europe (ISGT EUROPE)10.1109/ISGTEUROPE56780.2023.10408445(1-5)Online publication date: 23-Oct-2023
https://doi.org/10.1109/ISGTEUROPE56780.2023.10408445
Han ODing TMu CJia WMa Z(2023)Waste heat reutilization and integrated demand response for decentralized optimization of data centersEnergy10.1016/j.energy.2022.126101264(126101)Online publication date: Feb-2023
https://doi.org/10.1016/j.energy.2022.126101
Radovanovic AChen BTalukdar SRoy BDuarte AShahbazi M(2022)Power Modeling for Effective Datacenter Planning and Compute ManagementIEEE Transactions on Smart Grid10.1109/TSG.2021.312527513:2(1611-1621)Online publication date: Mar-2022
https://doi.org/10.1109/TSG.2021.3125275
Li XYu WRuiz RZhu J(2022)Energy-Aware Cloud Workflow Applications Scheduling With Geo-Distributed DataIEEE Transactions on Services Computing10.1109/TSC.2020.296510615:2(891-903)Online publication date: 1-Mar-2022
https://doi.org/10.1109/TSC.2020.2965106
Chen MGao CShahidehpour MLi Z(2022)Proliferation of Small Data Networks for Aggregated Demand Response in Electricity MarketsIEEE Transactions on Power Systems10.1109/TPWRS.2021.311546737:3(2297-2311)Online publication date: May-2022
https://doi.org/10.1109/TPWRS.2021.3115467
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