Nothing Special   »   [go: up one dir, main page]

skip to main content
10.1145/566726.566735acmconferencesArticle/Chapter ViewAbstractPublication PagesewConference Proceedingsconference-collections
Article
Free access

Every joule is precious: the case for revisiting operating system design for energy efficiency

Published: 17 September 2000 Publication History

Abstract

By some estimates, there will be close to one billion wireless devices capable of Internet connectivity within five years, surpassing the installed base of traditional wired compute devices. These devices will take the form of cellular phones, personal digital assistants (PDA's), embedded processors, and "Internet appliances". This proliferation of networked computing devices will enable a number of compelling applications, centering around ubiquitous access to global information services, just in time delivery of personalized content, and tight synchronization among compute devices/appliances in our everyday environment. However, one of the principal challenges of realizing this vision in the post-PC environment is the need to reduce the energy consumed in using these next-generation mobile and wireless devices, thereby extending the lifetime of the batteries that power them. While the processing power, memory, and network bandwidth of post-PC devices are increasing exponentially, their battery capacity is improving at a more modest pace.
Thus, to ensure the utility of post-PC applications, it is important to develop low-level mechanisms and higher-level policies to maximize energy efficiency. In this paper, we propose the systematic re-examination of all aspects of operating system design and implementation from the point of view of energy efficiency rather than the more traditional OS metric of maximizing performance. In [7], we made the case for energy as a first-class OS-managed resource. We emphasized the benefits of higher-level control over energy usage policy and the application/OS interactions required to achieve them. This paper explores the implications that this major shift in focus can have upon the services, policies, mechanisms, and internal structure of the OS itself based on our initial experiences with rethinking system design for energy efficiency.
Our ultimate goal is to design an operating system where major components cooperate to explicitly optimize for energy efficiency. A number of research efforts have recently investigated aspects of energy-efficient operating systems (a good overview is available at [16, 20]) and we intend to leverage existing "best practice" in our own work where such results exist. However, we are not aware of any systems that systematically revisit system structure with energy in mind. Further, our examination of operating system functionality reveals a number of opportunities that have received little attention in the literature. To illustrate this point, Table 1 presents major operating system functionality, along with possible techniques for improving power consumption characteristics. Several of the techniques are well studied, such as disk spindown policies or adaptively trading content fidelity for power [8]. For example, to reduce power consumption for MPEG playback, the system could adapt to a smaller frame rate and window size, consuming less bandwidth and computation.
One of the primary objectives of operating systems is allocating resources among competing tasks, typically for fairness and performance. Adding energy efficiency to the equation raises a number of interesting issues. For example, competing processes/users may be scheduled to receive a fair share of battery resources rather than CPU resources (e.g., an application that makes heavy use of DISK I/O may be given lower priority relative to a compute-bound application when energy resources are low). Similarly, for tasks such as ad hoc routing, local battery resources are often consumed on behalf of remote processes. Fair allocation dictates that one battery is not drained in preference to others. Finally, for the communication subsystem, a number of efforts already investigate adaptively setting the polling rate for wireless networks (trading latency for energy).
Our efforts to date have focused on the last four areas highlighted in Table 1. For memory allocation, our work explores how to exploit the ability of memory chips to transition among multiple power states. We also investigate metrics for picking energy-efficient routes in ad hoc networks, energy-efficient placement of distributed computation, and flexible RPC/name binding that accounts for power consumption.
These last two points of resource allocation and remote communication highlight an interesting property for energy-aware OS design in the post-PC environment. Many tasks are distributed across multiple machines, potentially running on machines with widely varying CPU, memory, and power source characteristics. Thus, energy-aware OS design must closely cooperate with and track the characteristics of remote computers to balance the often conflicting goals of optimizing for energy and speed.
The rest of this paper illustrates our approach with selected examples extracted from our recent efforts toward building an integrated hardware/software infrastructure that incorporates cooperative power management to support mobile and wireless applications. The instances we present in subsequent sections cover the resource management policies and mechanisms necessary to exploit low power modes of various (existing or proposed) hardware components, as well as power-aware communications and the essential role of the wide-area environment. We begin our discussion with the resources of a single machine and then extend it to the distributed context.

References

[1]
J. Broch, D. A. Maltz, D. B. Johnson, Y.-C. Hu, and J. Jetcheva. A Performance Comparison of Multi-Hop Wireless Ad Hoc Network Routing Protocols. In Proceedings of the Fourth Annual ACM/IEEE International Conference on Mobile Computing and Networking (MobiCom), October 1998.]]
[2]
S. Chandra and C. S. Ellis. JPEG Compression Metric as a Quality Aware Image Transcoding. In 2nd Symposium on Internet Technologies and Systems, Boulder, CO, October 1999. USENIX.]]
[3]
S. Chandra, C. S. Ellis, and A. Vahdat. Multimedia Web Services for Mob ile Clients Using Quality Aware Transcoding. In Proceedings of the Second ACM/IEEE International Conference on Wireless and Mobile Multimedia (WoWMoM'99), Seattle, WA, August 1999. ACM SIGMOBILE.]]
[4]
S. Chandra, C. S. Ellis, and A. Vahdat. Managing the storage and battery resources in an image capture device (digital camera) using dynamic transcoding. In Proceedings of the Second ACM/IEEE International Conference on Wireless and Mobile Multimedia (WoWMoM'00), August 2000.]]
[5]
F. Douglis, P. Krishnan, and B. Bershad. Adaptive Disk Spin-down Policies for Mobile Computers. In 2nd USENIX Symposium on Mobile and Location-Independent Computing, April 1995. Monterey CA.]]
[6]
F. Douglis, P. Krishnan, and B. Marsh. Thwarting the Power Hungry Disk. In Proceedings of the 1994 Winter USENIX Conference, pages 293-306, January 1994.]]
[7]
C. S. Ellis. The Case for Higher-Level Power Management. In Proceedings of the 7th Workshop on Hot Topics in Operating Systems, Rio Rico, AZ, March 1999.]]
[8]
J. Flinn and M. Satyanarayanan. Energy-aware adaptation for mobile applications. In Symposium on Operating Systems Principles (SOSP), pages 48-63, December 1999.]]
[9]
J. Haartsen, M. Naghshineh, J. Inouye, O. J. Joeresson, and W. Allen. Bluetooth: Vision, Goals, and Architecture. ACM Mobile Computing and Communications Review, 2(4):38-45, October 1998.]]
[10]
T. Halfhill. Transmeta breaks x86 low-power barrier. Microprocessor Report, February 2000.]]
[11]
D. Helmbold, D. Long, and B. Sherrod. A Dynamic Disk Spin-Down Technique for Mobile Computing. In Proc. of the 2nd ACM International Conf. on Mobile Computing (MOBICOM96), pages 130-142, November 1996.]]
[12]
Intel Corporation. Mobile Power Guidelines 2000. ftp://download.intel.com/design/mobile/intelpower/mpg99r1.pdf, December 1998.]]
[13]
R. Kravets and P. Krishnan. Power Management Techniques for Mobile Communication. In Proc. of the 4th International Conf. on Mobile Computing and Networking (MOBICOM98), pages 157-168, October 1998.]]
[14]
P. Krishnan, P. Long, and J. Vitter. Adaptive Disk Spin-Down via Optimal Rent-to-Buy in Probabilistic Environments. In Proceedings of the 12th International Conference on Machine Learning, pages 322-330, July 1995.]]
[15]
A. R. Lebeck, X. Fan, H. Zeng, and C. S. Ellis. Power aware page allocation. Technical Report CS-2000-08, Department of Computer Science, Duke University, June 2000.]]
[16]
D. Lee. Energy Management Issues for Computer Systems. //www.cs.washington.edu/homes/dlee/frontpage/mypapers/generals.ps.gz.]]
[17]
D. C. Lee, P. J. Crowley, J.-L. Baer, T. E. Anderson, and B. N. Bershad. Execution characteristics of desktop applications on Windows NT. In Proceedings of the 25th Annual International Symposium on Comput er Architecture, pages 27-38, June 1998.]]
[18]
K. Li, R. Kumpf, P. Horton, and T. Anderson. A Quantitative Analysis of Disk Drive Power Management in Portable Computers. In USENIX Association Winter Technical Conference Proceedings, pages 279-291, 1994.]]
[19]
J. Lorch and A. J. Smith. Scheduling Techniques for Reducing Processor Energy Use in MacOS. Wireless Networks, 3(5):311-324, October 1997.]]
[20]
J. Lorch and A. J. Smith. Software Strategies for Portable Computer Energy Management. IEEE Personal Communications Magazine, 5(3):60-73, June 1998.]]
[21]
MicroOptical Corp. Eyeglass Display, 1999.]]
[22]
T. Pering, T. Burd, and R. Brodersen. Dynamic Voltage Scaling and the Design of a Low-Power Microprocessor System. In Power Driven Microarchitecture Workshop, attached to ISCA98, June 1998.]]
[23]
Rambus. RDRAM, 1999. http://www.rambus.com/.]]
[24]
T. S. Rappaport. Wireless Communications: Principles and Practice. Prentice Hall, 1996.]]
[25]
A. Rudendo, P. Reiher, G. Popek, and G. Kuenning. Saving portable computer battery power through remote process execution. Mobile Computing and Communications Review, SIGMOBILE, 2(1):19-26, January 1998.]]
[26]
A. Rudendo, P. Reiher, G. Popek, and G. Kuenning. The remote processing framework for portable computer power saving. In Proceedings of ACM Symposium on Applied Computing, Feb 1999.]]
[27]
S. Singh, M. Woo, and C. S. Raghavendra. Power-Aware Routing in Mobile Ad Hoc Networks. In The Fourth Annual ACM/IEEE International Conference on Mobile Computing and Networking, pages 181-190, 1998.]]
[28]
M. Stemm and R. Katz. Measuring and Reducing Energy Consumption of Network Interfaces in Hand-Held Devices. In Proceedings of 3rd International Workshop on Mobile Multimedia Communications (MoMuC-3), September 1996.]]
[29]
V. Tiwari, S. Malik, and A. Wolfe. Power analysis of embedded software: A first step towards software power minimization. IEEE Transactions on Very Large Scale Integration, 2(4):437-445, December 1994.]]
[30]
A. Vahdat, T. Anderson, M. Dahlin, E. Belani, D. Culler, P. Eastham, and C. Yoshikawa. WebOS: Operating System Services for Wide-Area Applications. In Proceedings of the Seventh IEEE Symposium on High Performance Distributed Systems, Chicago, Illinois, July 1998.]]
[31]
A. Vahdat and D. Becker. Epidemic Routing for Partially Connected Ad Hoc Networks. Technical Report CS-2000-06, Duke University, April 2000.]]
[32]
M. Weiser, B. Welch, A. Demers, and S. Shenker. Scheduling for Reduced CPU Energy. In USENIX Association, Proceedings of First Symposium on Operating Systems Design and Implementation (OSDI), November 1994. Monterey CA.]]
[33]
H. Yu and A. Vahdat. Design and Evaluation of a Continuous Consistency Model for Replicated Services. In Proceedings of Operating Systems Design and Implementation, October 2000.]]

Cited By

View all
  • (2024)Intelligent architecture and platforms for private edge cloud systems: A reviewFuture Generation Computer Systems10.1016/j.future.2024.06.024160(457-471)Online publication date: Nov-2024
  • (2019)Optimization of Network Service Scheduling with Resource Sharing and Preemption2019 IEEE 20th International Conference on High Performance Switching and Routing (HPSR)10.1109/HPSR.2019.8808118(1-6)Online publication date: May-2019
  • (2019)A Power-Aware Routing Algorithm in Fat-tree Date Center Networks2019 IEEE 20th International Conference on High Performance Switching and Routing (HPSR)10.1109/HPSR.2019.8807996(1-6)Online publication date: May-2019
  • Show More Cited By
  1. Every joule is precious: the case for revisiting operating system design for energy efficiency

      Recommendations

      Comments

      Please enable JavaScript to view thecomments powered by Disqus.

      Information & Contributors

      Information

      Published In

      cover image ACM Conferences
      EW 9: Proceedings of the 9th workshop on ACM SIGOPS European workshop: beyond the PC: new challenges for the operating system
      September 2000
      249 pages
      ISBN:9781450373562
      DOI:10.1145/566726
      • General Chair:
      • Marc Shapiro
      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]

      Sponsors

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      Published: 17 September 2000

      Permissions

      Request permissions for this article.

      Check for updates

      Qualifiers

      • Article

      Conference

      EW00
      Sponsor:

      Acceptance Rates

      Overall Acceptance Rate 37 of 37 submissions, 100%

      Contributors

      Other Metrics

      Bibliometrics & Citations

      Bibliometrics

      Article Metrics

      • Downloads (Last 12 months)187
      • Downloads (Last 6 weeks)37
      Reflects downloads up to 19 Nov 2024

      Other Metrics

      Citations

      Cited By

      View all
      • (2024)Intelligent architecture and platforms for private edge cloud systems: A reviewFuture Generation Computer Systems10.1016/j.future.2024.06.024160(457-471)Online publication date: Nov-2024
      • (2019)Optimization of Network Service Scheduling with Resource Sharing and Preemption2019 IEEE 20th International Conference on High Performance Switching and Routing (HPSR)10.1109/HPSR.2019.8808118(1-6)Online publication date: May-2019
      • (2019)A Power-Aware Routing Algorithm in Fat-tree Date Center Networks2019 IEEE 20th International Conference on High Performance Switching and Routing (HPSR)10.1109/HPSR.2019.8807996(1-6)Online publication date: May-2019
      • (2018)New combined method for low energy consumption in Wireless Sensor Network applicationsSimulation10.1177/003754971875943294:10(873-885)Online publication date: 1-Oct-2018
      • (2017)Design and experimentation of a low-power IoT embedded system for wireless underwater sensing2017 International Conference on Wireless Networks and Mobile Communications (WINCOM)10.1109/WINCOM.2017.8238145(1-6)Online publication date: Nov-2017
      • (2015)Modeling energy-efficient secure communications in multi-mode wireless mobile devicesJournal of Computer and System Sciences10.1016/j.jcss.2014.12.02281:8(1464-1478)Online publication date: 1-Dec-2015
      • (2015)Blue Gene/Q defragmentation for energy waste minimisationThe Journal of Supercomputing10.1007/s11227-014-1293-871:1(202-216)Online publication date: 1-Jan-2015
      • (2014)An experimental survey of energy management across the stackACM SIGPLAN Notices10.1145/2714064.266019649:10(329-344)Online publication date: 15-Oct-2014
      • (2014)An experimental survey of energy management across the stackProceedings of the 2014 ACM International Conference on Object Oriented Programming Systems Languages & Applications10.1145/2660193.2660196(329-344)Online publication date: 15-Oct-2014
      • (2013)Energy overhead of the graphical user interface in server operating systemsProceedings of the 41st annual ACM SIGUCCS conference on User services10.1145/2504776.2504781(65-68)Online publication date: 3-Nov-2013
      • Show More Cited By

      View Options

      View options

      PDF

      View or Download as a PDF file.

      PDF

      eReader

      View online with eReader.

      eReader

      Login options

      Media

      Figures

      Other

      Tables

      Share

      Share

      Share this Publication link

      Share on social media