Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
skip to main content
10.5555/3398761.3398775acmconferencesArticle/Chapter ViewAbstractPublication PagesaamasConference Proceedingsconference-collections
research-article

A General Framework for Energy-Efficient Cloud Computing Mechanisms

Published: 13 May 2020 Publication History

Abstract

We present a general model for the operation of a cloud computing server comprised of one or more speed-scalable processors. Typically, tasks are submitted to such a cloud computing server in an online fashion, and the server operator has to schedule the tasks and decides on payments without knowledge about the tasks arriving in the future. Although very natural, this cloud computing problem on speed-scalable processors has not been studied from a mechanism design perspective in the online setting.
We provide a mechanism for this setting, both for a single and multiprocessor environment, that has several desirable properties: (1) the induced game admits a subgame perfect equilibrium in pure strategies and therefore a pure Nash equilibrium, (2) the Price of Anarchy is constant, (3) the mechanism is budget balanced, i.e., the sum of the payments of the agents is equal to the total energy costs, (4) the communication complexity is low, (5) the mechanism is computationally tractable for both the service operator and the agents, and (6) the agents' payment is also intuitive and easy to communicate to them. We also provide a second mechanism with a better Price of Anarchy, which in turn is more involved to implement.
We are able to extend our mechanisms and results to the Bayesian setting, where the type of each agent is drawn independently from some underlying distribution and agents are minimizing their expected costs. In this setting we also show the same approximation factor of our mechanism as in the basic online setting in both the single and the multiprocessor environment.

References

[1]
Susanne Albers. 2010. Energy-efficient algorithms. Commun. ACM, Vol. 53, 5 (2010), 86--96.
[2]
Susanne Albers and Antonios Antoniadis. 2014. Race to idle: New algorithms for speed scaling with a sleep state. ACM Trans. Algorithms, Vol. 10, 2 (2014), 9:1--9:31.
[3]
Susanne Albers, Antonios Antoniadis, and Gero Greiner. 2015. On multi-processor speed scaling with migration. J. Comput. Syst. Sci., Vol. 81, 7 (2015), 1194--1209.
[4]
Susanne Albers and Hiroshi Fujiwara. 2007. Energy-efficient algorithms for flow time minimization. ACM Trans. Algorithms, Vol. 3, 4 (2007), 49.
[5]
Anders Andrae and Tomas Edler. 2015. On global electricity usage of communication technology: trends to 2030. Challenges, Vol. 6, 1 (2015), 117--157.
[6]
Eric Angel, Evripidis Bampis, Vincent Chau, and Nguyen Kim Thang. 2014. Throughput Maximization in Multiprocessor Speed-Scaling. In Algorithms and Computation - 25th International Symposium, ISAAC 2014 (Lecture Notes in Computer Science), Vol. 8889. Springer, 247--258.
[7]
Antonios Antoniadis and Andrés Cristi. 2018. A Near Optimal Mechanism for Energy Aware Scheduling. In SAGT .
[8]
Antonios Antoniadis, Chien-Chung Huang, and Sebastian Ott. 2015. A Fully Polynomial-Time Approximation Scheme for Speed Scaling with Sleep State. In Proceedings of the Twenty-Sixth Annual ACM-SIAM Symposium on Discrete Algorithms, SODA 2015. SIAM, 1102--1113.
[9]
Adi Avidor, Yossi Azar, and Jir'i Sgall. 2001. Ancient and New Algorithms for Load Balancing in the l(_mboxp ) Norm. Algorithmica, Vol. 29, 3 (2001), 422--441.
[10]
Moshe Babaioff, Yishay Mansour, Noam Nisan, Gali Noti, Carlo Curino, Nar Ganapathy, Ishai Menache, Omer Reingold, Moshe Tennenholtz, and Erez Timnat. 2017. Era: A framework for economic resource allocation for the cloud. In Proceedings of the 26th International Conference on World Wide Web Companion. International World Wide Web Conferences Steering Committee, 635--642.
[11]
Nikhil Bansal, David P. Bunde, Ho-Leung Chan, and Kirk Pruhs. 2011. Average Rate Speed Scaling. Algorithmica, Vol. 60, 4 (2011), 877--889.
[12]
Nikhil Bansal, Ho-Leung Chan, Dmitriy Katz, and Kirk Pruhs. 2012. Improved Bounds for Speed Scaling in Devices Obeying the Cube-Root Rule. Theory of Computing, Vol. 8, 1 (2012), 209--229.
[13]
Nikhil Bansal, Ho-Leung Chan, Tak Wah Lam, and Lap-Kei Lee. 2008. Scheduling for Speed Bounded Processors. In Automata, Languages and Programming, 35th International Colloquium, ICALP 2008, Proceedings, Part I: Tack A: Algorithms, Automata, Complexity, and Games (Lecture Notes in Computer Science), Vol. 5125. Springer, 409--420.
[14]
Nikhil Bansal, Tracy Kimbrel, and Kirk Pruhs. 2007. Speed scaling to manage energy and temperature. J. ACM, Vol. 54, 1 (2007), 3:1--3:39.
[15]
Nikhil Bansal, Kirk Pruhs, and Clifford Stein. 2009. Speed Scaling for Weighted Flow Time. SIAM J. Comput., Vol. 39, 4 (2009), 1294--1308.
[16]
Anton Beloglazov, Rajkumar Buyya, Young Choon Lee, and Albert Y. Zomaya. 2011. A Taxonomy and Survey of Energy-Efficient Data Centers and Cloud Computing Systems. Advances in Computers, Vol. 82 (2011), 47--111.
[17]
David M Brooks, Pradip Bose, Stanley E Schuster, Hans Jacobson, Prabhakar N Kudva, Alper Buyuktosunoglu, John Wellman, Victor Zyuban, Manish Gupta, and Peter W Cook. 2000. Power-aware microarchitecture: Design and modeling challenges for next-generation microprocessors. IEEE Micro, Vol. 20, 6 (2000), 26--44.
[18]
Ho-Leung Chan, Jeff Edmonds, Tak Wah Lam, Lap-Kei Lee, Alberto Marchetti-Spaccamela, and Kirk Pruhs. 2011. Nonclairvoyant Speed Scaling for Flow and Energy. Algorithmica, Vol. 61, 3 (2011), 507--517.
[19]
Shuchi Chawla, Nikhil Devanur, Janardhan Kulkarni, and Rad Niazadeh. 2017. Truth and regret in online scheduling. In Proceedings of the 2017 ACM Conference on Economics and Computation. ACM, 423--440.
[20]
Rajarshi Das, Jeffrey O. Kephart, Charles Lefurgy, Gerald Tesauro, David W. Levine, and Hoi Chan. 2008. Autonomic multi-agent management of power and performance in data centers. In 7th International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS 2008), Estoril, Portugal, May 12-16, 2008, Industry and Applications Track Proceedings. 107--114.
[21]
M. Dayarathna, Y. Wen, and R. Fan. 2016. Data Center Energy Consumption Modeling: A Survey. IEEE Communications Surveys Tutorials, Vol. 18, 1 (Firstquarter 2016), 732--794.
[22]
Christoph Dü rr, Lukasz Jez, and Oscar C. Vá squez. 2015. Scheduling under dynamic speed-scaling for minimizing weighted completion time and energy consumption. Discrete Applied Mathematics, Vol. 196 (2015), 20--27.
[23]
Christoph Dü rr, Lukasz Jez, and Oscar C. Vá squez. 2017. Mechanism design for aggregating energy consumption and quality of service in speed scaling scheduling. Theor. Comput. Sci., Vol. 695 (2017), 28--41.
[24]
Sandy Irani and Kirk Pruhs. 2005. Algorithmic problems in power management. SIGACT News, Vol. 36, 2 (2005), 63--76.
[25]
Elias Koutsoupias and Christos Papadimitriou. 1999. Worst-case Equilibria. In Proceedings of the 16th Annual Conference on Theoretical Aspects of Computer Science (STACS'99). Springer-Verlag, Berlin, Heidelberg, 404--413.
[26]
Tak Wah Lam, Lap-Kei Lee, Isaac Kar-Keung To, and Prudence W. H. Wong. 2013. Online Speed Scaling Based on Active Job Count to Minimize Flow Plus Energy. Algorithmica, Vol. 65, 3 (2013), 605--633.
[27]
Brendan Lucier, Ishai Menache, Joseph Seffi Naor, and Jonathan Yaniv. 2013. Efficient online scheduling for deadline-sensitive jobs. In Proceedings of the twenty-fifth annual ACM symposium on Parallelism in algorithms and architectures. ACM, 305--314.
[28]
Nicole Megow and José Verschae. 2013. Dual Techniques for Scheduling on a Machine with Varying Speed. In Automata, Languages, and Programming - 40th International Colloquium, ICALP 2013, Proceedings, Part I (Lecture Notes in Computer Science), Vol. 7965. Springer, 745--756.
[29]
Hervé Moulin. 1999. Incremental cost sharing: Characterization by coalition strategy-proofness. Social Choice and Welfare, Vol. 16, 2 (1999), 279--320.
[30]
Trevor Mudge. 2001. Power: A first-class architectural design constraint. Computer, Vol. 34, 4 (2001), 52--58.
[31]
Tim Roughgarden. 2015. The Price of Anarchy in Games of Incomplete Information. ACM Trans. Econ. Comput., Vol. 3, 1, Article 6 (March 2015), bibinfonumpages20 pages.
[32]
Bolei Xu, Tao Qin, Guoping Qiu, and Tie-Yan Liu. 2015. Competitive Pricing for Cloud Computing in an Evolutionary Market. In Proceedings of the 2015 International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2015, Istanbul, Turkey, May 4-8, 2015. 1755--1756.
[33]
F. Frances Yao, Alan J. Demers, and Scott Shenker. 1995. A Scheduling Model for Reduced CPU Energy. In 36th Annual Symposium on Foundations of Computer Science, Milwaukee, Wisconsin, 23--25 October 1995. IEEE Computer Society, 374--382.

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences
AAMAS '20: Proceedings of the 19th International Conference on Autonomous Agents and MultiAgent Systems
May 2020
2289 pages
ISBN:9781450375184

Sponsors

Publisher

International Foundation for Autonomous Agents and Multiagent Systems

Richland, SC

Publication History

Published: 13 May 2020

Check for updates

Author Tags

  1. cloud computing
  2. energy efficiency
  3. game theory
  4. mechanism design
  5. price of anarchy
  6. scheduling
  7. speed scaling

Qualifiers

  • Research-article

Funding Sources

  • DFG
  • Conicyt

Conference

AAMAS '19
Sponsor:

Acceptance Rates

Overall Acceptance Rate 1,155 of 5,036 submissions, 23%

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • 0
    Total Citations
  • 98
    Total Downloads
  • Downloads (Last 12 months)8
  • Downloads (Last 6 weeks)1
Reflects downloads up to 12 Feb 2025

Other Metrics

Citations

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media