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Speed-Scaling-based Job/Tasks Deployment for Energy-efficient Datacenters in Cloud Computing

Published: 01 December 2013 Publication History

Abstract

Power management is one of the most challenging problems in cloud computing. A cloud data center could save the amount of energy used from speed scaling. The traditional theoretical research for speed scaling usually assume the power function as the form Sα. Moreover, more comprehensive support for Quality of Service (QoS) is essential by cloud computing providers. Thus, how to dealing with the power/performance trade-off is a burning question. Motivated by improving energy efficiency of the data center, we study policies by setting the speed of the processor for both goals of minimizing the total energy cost and meeting the specified QoS performance well. We initiate a model of speed scaling with weighted power energy, the QoS parameters can be induced to a qualitative concept as the weighting factor of energy consumptions. Based on this model, we propose a resource allocation policy based on the cooperative game theory for energy-efficient management of clouds. The simulation results show the efficiency of the method.

References

[1]
Nathuji R, Schwan K. VirtualPower: coordinated power management in virtualized enterprise systems{J}. ACM SIGOPS Operating Systems Review, 2007, 41(6): 265-278.
[2]
Pinheiro E, Bianchini R, Carrera E V, et al. Load balancing and unbalancing for power and performance in cluster-based systems{C}//Workshop on compilers and operating systems for low power. 2001, 180: 182-195.
[3]
Chase J S, Anderson D C, Thakar P N, et al. Managing energy and server resources in hosting centers{C}//ACM SIGOPS Operating Systems Review. ACM, 2001, 35(5): 103-116.
[4]
Srikantaiah S, Kansal A, Zhao F. Energy aware consolidation for cloud computing{C}//Proceedings of the 2008 conference on Power aware computing and systems. USENIX Association, 2008, 10.
[5]
Verma A, Ahuja P, Neogi A. pMapper: power and migration cost aware application placement in virtualized systems{M}//Middleware 2008. Springer Berlin Heidelberg, 2008: 243-264.
[6]
Gandhi A, Harchol-Balter M, Das R, et al. Optimal power allocation in server farms{C}//Proceedings of the eleventh international joint conference on Measurement and modeling of computer systems. ACM, 2009: 157-168.
[7]
Beloglazov A, Buyya R. Energy efficient resource management in virtualized cloud data centers{C}//Proceedings of the 2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing. IEEE Computer Society, 2010: 826-831.
[8]
Bansal N, Chan H L, Pruhs K. Speed scaling with an arbitrary power function{C}//Proceedings of the twentieth Annual ACM-SIAM Symposium on Discrete Algorithms. Society for Industrial and Applied Mathematics, 2009: 693-701.
[9]
Kuhn H W. The Hungarian method for the assignment problem{J}. Naval research logistics quarterly, 1955, 2(1 - 2): 83-97.

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  • (2020)Distributed Video Analysis for Mobile Live Broadcasting Services2020 IEEE Wireless Communications and Networking Conference (WCNC)10.1109/WCNC45663.2020.9120783(1-6)Online publication date: May-2020

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cover image ACM Other conferences
ICCC '13: Proceedings of the Second International Conference on Innovative Computing and Cloud Computing
December 2013
285 pages
ISBN:9781450321198
DOI:10.1145/2556871
  • General Chairs:
  • Min Wu,
  • Wei Lee,
  • Program Chairs:
  • Yiyi Zhouzhou,
  • Riza Esa,
  • Xiang Lee
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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  • ACM Wuhan Chapter: ACM Wuhan Chapter

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 December 2013

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Author Tags

  1. Cloud computing
  2. Energy efficiency
  3. Job/tasks deployment
  4. QoS
  5. Speed scaling

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  • (2020)Distributed Video Analysis for Mobile Live Broadcasting Services2020 IEEE Wireless Communications and Networking Conference (WCNC)10.1109/WCNC45663.2020.9120783(1-6)Online publication date: May-2020

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