Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
skip to main content
article

Energy-aware lossless data compression

Published: 01 August 2006 Publication History

Abstract

Wireless transmission of a single bit can require over 1000 times more energy than a single computation. It can therefore be beneficial to perform additional computation to reduce the number of bits transmitted. If the energy required to compress data is less than the energy required to send it, there is a net energy savings and an increase in battery life for portable computers. This article presents a study of the energy savings possible by losslessly compressing data prior to transmission. A variety of algorithms were measured on a StrongARM SA-110 processor. This work demonstrates that, with several typical compression algorithms, there is a actually a net energy increase when compression is applied before transmission. Reasons for this increase are explained and suggestions are made to avoid it. One such energy-aware suggestion is asymmetric compression, the use of one compression algorithm on the transmit side and a different algorithm for the receive path. By choosing the lowest-energy compressor and decompressor on the test platform, overall energy to send and receive data can be reduced by 11% compared with a well-chosen symmetric pair, or up to 57% over the default symmetric zlib scheme.

References

[1]
Advanced RISC Machines Ltd. (ARM). 1998. Writing Efficient C for ARM. Application note 34. Go online to www.arm.com/pdfs.]]
[2]
Agilent Technologies. 2000. Agilent 34401A Multimeter: User's Guide, 5th ed. Palo Alto, CA.]]
[3]
Austin, T. M. and Burger, D. C. 2001. SimpleScalar version 4.0 release (tutorial). In Proceedings of the 34th Annual International Symposium on Microarchitecture.]]
[4]
Banerjee, S. and Misra, A. 2004. Power adaptation based optimization for energy efficient reliable wireless paths. Tech. rep. Department of Computer Sciences, University of Wisconsin-Madison, Madison, WI.]]
[5]
Bell, T. and Kulp, D. 1989. Longest match string searching for Ziv-Lempel compression. Tech. Rep. 06/89. Department of Computer Science, University of Canterbury, Christchurch New Zealand.]]
[6]
Bell, T., Powell, M., Horlor, J., and Arnold, R. 1997. The Canterbury Corpus. Go online to http://www.corpus.canterbury.ac.nz/.]]
[7]
Bell, T., Witten, I. H., and Cleary, J. G. 1989. Modeling for text compression. ACM Comput. Surv. 21, 4, 557--591.]]
[8]
Bilmes, J., Asanović, K., Chin, C.-W., and Demmel, J. 1997. Optimizing matrix multiply using PHiPAC: A portable, high-performance, ANSI C coding methodology. In Proceedings of the 11th ACM International Conference on Supercomputing.]]
[9]
Burger, D. C. and Austin, T. M. 1997. The SimpleScalar tool set, version 2.0. Tech. Rep. CS-TR-97-1342. University of Wisconsin, Madison, Madison, WI.]]
[10]
Burrows, M. and Wheeler, D. J. 1994. A block-sorting lossless data compression algorithm. Tech. Rep. 124. Digital Systems Research Center, Palo Alto, CA.]]
[11]
Chang, F., Farkas, K., and Ranganathan, P. 2002. Energy-driven statistical profiling: Detecting software hotspots. In Proceedings of the 2nd Workshop on Power-Aware Computer Systems (HPCA-8).]]
[12]
Chang, J.-H. and Tassiulas, L. 2000. Energy conserving routing in wireless ad-hoc networks. In Proceedings of IEEE INFOCOM. 22--31.]]
[13]
Cleary, J. G. and Witten, I. H. 1984. Data compression using adaptive coding and partial string matching. IEEE Trans. Commun. 32, 4 (Apr.), 396--402.]]
[14]
Craft, D. J. 1998. Data compression in ASIC cores. IBM J. Res. Devel. 42, 6.]]
[15]
Effros, M. 2000. PPM performance with BWT complexity: A new method for lossless data compression. In Proceedings of the Data Compression Conference.]]
[16]
Flinn, J. 2001. Extending mobile computer battery life through energy-aware adaptation. Ph.D. dissetation. Carnegie Mellon University, Pittsburgh, PA. Also Tech. rep. TR No. CMU-CS-01-171, Computer Science Depatment, Carnegie Mellan University.]]
[17]
Flinn, J., Farkas, K. I., and Anderson, J. 2000. Power and energy characterization of the Itsy pocket computer (version 1.5). Tech. Rep. TN-56. Compaq Computer Corporation, Houston, TX.]]
[18]
Flinn, J. and Satyanarayanan, M. 1999. Powerscope: A tool for profiling the energy usage of mobile applications. In Proceedings of the 2nd IEEE Workshop on Mobile Computing Systems and Applications.]]
[19]
Gailly, J. 1999. Go online to comp.compression Internet newsgroup: Frequently Asked Questions.]]
[20]
Gailly, J. and Adler, M. 2002. zlib. Go online to http://www.gzip.org/zlib.]]
[21]
Gilchrist, J. 2002. Archive comparison test. Go online to http://compression.ca.]]
[22]
Havinga, P. J. 1999. Energy efficiency of error correction on wireless systems. In Proceedings of the IEEE Wireless Communications and Networking Conference.]]
[23]
Hicks, J. 2005. Director, MIT-Quanta T-Party Project. Personal communication.]]
[24]
Hicks, J. et al. 1999. Compaq personal server project. Go online to http://crl.research.compaq.com/projects/personalserver/default.htm.]]
[25]
Hohlt, B., Doherty, L., and Brewer, E. 2004. Flexible power scheduling for sensor networks. In Proceedings of the IEEE and ACM Third International Symposium on Information Processing in Sensor Networks.]]
[26]
Housel, B. C. and Lindquist, D. B. 1996. Webexpress: A system for optimizing Web browsing in a wireless environment. In Proceedings of the Second Annual International Conference on Mobile Computing and Networking. 108--116.]]
[27]
Hunt, J. J., Vo, K.-P., and Tichy, W. F. 1996. An empirical study of delta algorithms. In Software Configuration Management: ICSE 96 SCM-6 Workshop. Springer, Berlin, Germany, 49--66.]]
[28]
IBM. 2001. IBM J. Res. Devel. 45, 2. Preface by Richard E. Harper, Guest Editor.]]
[29]
Intel Corporation. 2000. SA-110 Microprocessor Technical Reference Manual. Intel Corporation, Santa Clara, CA.]]
[30]
Intel Corporation. 2001. Intel StrongARM SA-1110 Microprocessor Developer's Manual. Intel Corporation, Santa Clara, CA.]]
[31]
Jacobson, V. 1990. RFC 1144: Compressing TCP/IP headers for low-speed serial links. Available online at www.rfe-editer.org.]]
[32]
Jamieson, K. 2002. Implementation of a power-saving protocol for ad hoc wireless networks. M.S. thesis. Massachusetts Institute of Technology, Cambridge, MA.]]
[33]
Jannesen, P. et. al 1996. (n)compress. Available, among other places, in Redhat 7.2 distribution of Linux.]]
[34]
Jung, B. and Burleson, W. P. 1994. A VLSI systolic array architecture for Lempel-Ziv based data compression. In Proceedings of the International Symposium on Circuits and Systems.]]
[35]
Jung, B. and Burleson, W. P. 1995. Real-time VLSI compression for high-speed wireless local area networks. In Proceedings of the Data Compression Conference.]]
[36]
Krashinsky, R. 2003. Efficient Web browsing for mobile clients using HTTP compression. Tech. Rep. MIT-LCS-TR-882. MIT Laboratory for Computer Science, Combridge, MA.]]
[37]
Lekatsas, H., Henkel, J., and Wolf, W. 2000. Low-power techniques for code compression in embedded systems. In Proceedings of the Design Automation Conference.]]
[38]
Lelewer, D. A. and Hirschberg, D. S. 1987. Data compression. ACM Comput. Serv. 19, 3, 261--297.]]
[39]
Lilley, J., Yang, J., Balakrishnan, H., and Seshan, S. 2000. A unified header compression framework for low-bandwidth links. In Proceedings of the 6th ACM MOBICOM.]]
[40]
Lycos. 2002. Lycos 50. Top 50 searches on Lycos for the week ending September 21, 2002.]]
[41]
McEliece, R. 1977. The theory of information and coding. In Encyclopedia of Mathematics and Its Application. Vol. 3. Addison-Wesley, Reading, MA.]]
[42]
Miyoshi, A., Lefurgy, C., Hensbergen, E. V., Rajamony, R., and Rajkumar, R. 2002. Critical power slope: Understanding the runtime effects of frequency scaling. In Proceedings of the International Conference on Supercomputing.]]
[43]
Mogul, J. C. 1999. Trace-based analysis of duplicate suppression in HTTP. Tech. Rep. 99.2. Compaq Computer Corporation, Houston, TX.]]
[44]
Mogul, J. C., Douglis, F., Feldmann, A., and Krishnamurthy, B. 1997. Potential benefits of delta encoding and data compression for HTTP. Tech. Rep. 97/4a. Compaq Computer Corporation, Houston, TX.]]
[45]
Montanaro et al., J. 1996. A 160-MHz, 32-b, 0.5-W CMOS RISC microprocessor. IEEE J. Sol.-State Circ. 31, 11 (Nov.), 1703--1714.]]
[46]
Motgi, N. and Mukherjee, A. 2001. Network conscious text compression systems (NCTCSys). In Proceedings of the International Conference on Information and Theory: Coding and Computing.]]
[47]
Muthitacharoen, A., Chen, B., and Mazières, D. 2001. A low-bandwidth network file system. In Proceedings of the 18th ACM Symposium on Operating Systems Principles (SOSP '01, Chateau Lake Louise, Banff, Alta., Canada). 174--187.]]
[48]
Nathuji, R. 2000. Characterization of DRAM. MIT Advanced Undergraduate Project. Massachusetts Institute of Technology, Cambridge, MA.]]
[49]
Nielsen NetRatings Audience Measurement Service. 2002. Top 25 U.S Properties; Week of Sept. 15th. Go online to www.nielsen-netratings.com.]]
[50]
Noble, B. D. and Satyanarayanan, M. 1999. Experience with adaptive mobile applications in odyssey. Mobile Netw. Appl. 4, 4, 245--254.]]
[51]
Oberhumer, M. F. 2000. Lzo. Go on line to http://www.oberhumer.com/opensource/lzo/.]]
[52]
Peymandoust, A., Simunić, T., and Micheli, G. D. 2002. Low power embedded software optimization using symbolic algebra. In Proceedings of the Conference on Design, Automation and Test in Europe.]]
[53]
Santos, J. and Wetherall, D. 1998. Increasing effective link bandwidth by suppressing replicated data. In Proceedings of the USENIX Annual Technical Conference. 213--224.]]
[54]
Sayood, K. 2002. Introduction to Data Compression, 2nd ed. Morgan Kaufman San Francisco, CA.]]
[55]
Seward, J. 1999. bzip2. Go online to http://www.spec.org/osg/cpu2000/CINT2000/256.bzip2/docs/256.bzip2.html.]]
[56]
Seward, J. 2000. e2comp bzip2 library. Go online to http://cvs.bofh.asn.au/e2compr/ index.html.]]
[57]
Shacham, A., Monsour, B., Pereira, R., and Thomas, M. 2001. RFC 3173: IP payload compression protocol. Available online at www.rfc-editor.org/.]]
[58]
Shannon, C. E. 1948. A mathematical theory of communication. Bell Syst. Tech. J. 27, 379--423 and 623--656.]]
[59]
Shkarin, D. 2002a. PPM: One step to practicality. In Proceedings of the Data Compression Conference.]]
[60]
Shkarin, D. 2002b. PPMd. Go online to ftp://ftp.elf.stuba.sk/pub/pc/pack/ppmdi1.rar.]]
[61]
Simunić, T., Benini, L., and Micheli, G. D. 1999. Energy-efficient design of battery-powered embedded systems. In Proceedings of the IEEE International Symposium on Low Power Electronics and Design.]]
[62]
Simunić, T., Benini, L., Micheli, G. D., and Hans, M. 2000. Source code optimization and profiling of energy consumption in embedded systems. In Proceedings of the International Symposium on System Synthesis.]]
[63]
Sinha, A. and Chandrakasan, A. 2001. Jouletrack---a Web based tool for software energy profiling. In Proceedings of the 38th Design Automation Conference.]]
[64]
Sinha, A., Wang, A., and Chandrakasan, A. 2000. Algorithmic transforms for efficient energy scalable computation. In Proceedings of the IEEE International Symposium on Low Power Electronics and Design.]]
[65]
Standard Performance Evaluation Corporation. 2000. CPU2000. Go online to www.spec.org.]]
[66]
Taylor, C. N. and Dey, S. 2001. Adaptive image compression for wireless multimedia communication. In Proceedings of the IEEE International Conference on Communication.]]
[67]
Thomborson, C. 1992. The V.42bis standard for data-compressing modems. IEEE Micro 12, 5.]]
[68]
Tridgell, A. 2000. Efficient algorithms for sorting and synchronization. Ph.D. dissertation. Australian National University, Canberra, Australia.]]
[69]
Viredaz, M. A. and Wallach, D. A. 2000. Power evaluation of Itsy version 2.3. Tech. Rep. TN-57. Compaq Computer Corporation, Houston, TX.]]
[70]
Viredaz, M. A. and Wallach, D. A. 2001. Power evaluation of Itsy version 2.4. Tech. Rep. TN-59. Compaq Computer Corporation, Houston, TX.]]
[71]
Welch, T. A. 1984. A technique for high-performance data compression. IEEE Comput. 17, 6, 8--19.]]
[72]
Wolfe, A. and Chanin, A. 1992. Executing compressed programs on an embedded RISC architecture. In Proceedings of the 25th Annual International Symposium on Microarchitecture.]]
[73]
Yang, H., Gao, G. R., Marquez, A., Cai, G., and Hu, Z. 2001. Power and energy impact of loop transformations. In Proceedings of the Workshop on Compilers and Operating Systems for Low Power 2001, Parallel Architecture and Compilation Techniques.]]
[74]
Ziv, J. and Lempel, A. 1977. A universal algorithm for data compression. IEEE Trans. Inform. Theor. 23, 3 (May), 337--343.]]
[75]
Ziv, J. and Lempel, A. 1978. Compression of individual sequences via variable rate coding. IEEE Trans. Inform. Theor. 24, 5 (Sep.), 530--536.]]

Cited By

View all
  • (2024)An Energy Efficient and Scalable WSN with Enhanced Data Aggregation AccuracyJournal of Telecommunications and Information Technology10.26636/jtit.2024.2.1510(48-57)Online publication date: 7-May-2024
  • (2024)Leveraging Edge Computing for Minimizing Base Station Energy Consumption in Multi-Cell (N)OMA Downlink SystemsIEEE Open Journal of the Communications Society10.1109/OJCOMS.2024.33540745(885-907)Online publication date: 2024
  • (2024)AdaEdge: A Dynamic Compression Selection Framework for Resource Constrained Devices2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00124(1506-1519)Online publication date: 13-May-2024
  • Show More Cited By

Index Terms

  1. Energy-aware lossless data compression

    Recommendations

    Comments

    Information & Contributors

    Information

    Published In

    cover image ACM Transactions on Computer Systems
    ACM Transactions on Computer Systems  Volume 24, Issue 3
    August 2006
    121 pages
    ISSN:0734-2071
    EISSN:1557-7333
    DOI:10.1145/1151690
    Issue’s Table of Contents

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 01 August 2006
    Published in TOCS Volume 24, Issue 3

    Permissions

    Request permissions for this article.

    Check for updates

    Author Tags

    1. Compression
    2. energy-aware
    3. lossless
    4. low-power
    5. power-aware

    Qualifiers

    • Article

    Contributors

    Other Metrics

    Bibliometrics & Citations

    Bibliometrics

    Article Metrics

    • Downloads (Last 12 months)79
    • Downloads (Last 6 weeks)5
    Reflects downloads up to 22 Sep 2024

    Other Metrics

    Citations

    Cited By

    View all
    • (2024)An Energy Efficient and Scalable WSN with Enhanced Data Aggregation AccuracyJournal of Telecommunications and Information Technology10.26636/jtit.2024.2.1510(48-57)Online publication date: 7-May-2024
    • (2024)Leveraging Edge Computing for Minimizing Base Station Energy Consumption in Multi-Cell (N)OMA Downlink SystemsIEEE Open Journal of the Communications Society10.1109/OJCOMS.2024.33540745(885-907)Online publication date: 2024
    • (2024)AdaEdge: A Dynamic Compression Selection Framework for Resource Constrained Devices2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00124(1506-1519)Online publication date: 13-May-2024
    • (2024)FedSZ: Leveraging Error-Bounded Lossy Compression for Federated Learning Communications2024 IEEE 44th International Conference on Distributed Computing Systems (ICDCS)10.1109/ICDCS60910.2024.00060(577-588)Online publication date: 23-Jul-2024
    • (2024)Autoencoder-based image compression for wireless sensor networksScientific African10.1016/j.sciaf.2024.e0215924(e02159)Online publication date: Jun-2024
    • (2023)Identity-Based Proxy Signature with Message Recovery over NTRU LatticeEntropy10.3390/e2503045425:3(454)Online publication date: 4-Mar-2023
    • (2023)Ocellus: Highly Parallel Convolution-in-Pixel Scheme Realizing Power-Delay-Efficient Edge Intelligence2023 IEEE/ACM International Symposium on Low Power Electronics and Design (ISLPED)10.1109/ISLPED58423.2023.10244476(1-6)Online publication date: 7-Aug-2023
    • (2023)Time and Cost-Efficient Cloud Data Transmission based on Serverless Computing CompressionIEEE INFOCOM 2023 - IEEE Conference on Computer Communications10.1109/INFOCOM53939.2023.10229090(1-10)Online publication date: 17-May-2023
    • (2023)A Review of Energy Hole Mitigating Techniques in Multi-Hop Many to One Communication and its Significance in IoT Oriented Smart City InfrastructureIEEE Access10.1109/ACCESS.2023.332731111(121340-121367)Online publication date: 2023
    • (2023)Anomaly Detection in Coastal Wireless Sensors via Efficient Deep Sequential LearningIEEE Access10.1109/ACCESS.2023.332237011(110260-110271)Online publication date: 2023
    • Show More Cited By

    View Options

    Get Access

    Login options

    Full Access

    View options

    PDF

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    Media

    Figures

    Other

    Tables

    Share

    Share

    Share this Publication link

    Share on social media