1. Le Wang MASTER THESIS PRESENTATION Evaluation of Compression for Energy- aware Communication in Wireless Networks

2. Master Thesis Presentation Supervisor: Professor Jukka Manner Instructor: Sebastian Siikavirta Department of Communications and Networks Faculty of Electronics, Communications, and Automation Helsinki University of Technology 25th, May, 2009

3. Introduction This study aims to investigate the usages of data compression to reduce the energy consumption in a hand-held device. By conducting experiments as the methodologies, the impacts of transmission on energy consumption are explored on wireless interfaces. 9 lossless compression algorithms are examined on popular Internet traffic in the view of compression ratio, speed and consumed energy. Energy consumption of uplink, downlink and overall system is investigated to achieve a comprehensive understanding of compression in wireless networks.

4. Why is it needed Energy Consumption ICT infrastructure total: power consumption 2.1 TWh -2.3% of all power consumption in Finland ICT user terminals total: power consumption 4.6 TWh -5.1% of all power consumption in Finland Greenhouse gas emissions ICT contribution to Greenhouse Gas emission: 2.5% = 1.0 GtCO2eq Mobile user energy consumption is approximate 29kWh = 55 kgco2eq Battery UMTS, HSDPA, IEEE802.11b/g and Bluetooth Camera, GPS, music, movies EFORE Oy,2008

5. Why is it needed Economics EFORE Oy,2008

6. Motivation Energy consumed on a single bit transmission over wireless is over 1000 times greater than a single 32-bit CPU computation Compression reduces file sizes Trade-off between computation and communication

7. Problems David Salomon-” Data compression is popular for two reasons: (1) People like to accumulate data and hate to throw anything away. No matter how big a storage device one has, sooner or later it is going to overflow. Data compression seems useful because it delays this inevitability. (2) People hate to wait a long time for data transfers.” Data compression is not energy-oriented. Blind or unconditional compressions for energy- aware communication related to wireless networks may result in wasting of energy and even slowing down transmission rate.

8. Compression Lossy compression is one where compressing data and then decompressing it retrieves data that may well be different from the original G.711, G.726 and AMR WMA and MP3 JPEG and PGF MPEG, H.261, H.263 and H.264 Lossless compression is in contrast to represent information which can be recovered into the original data without any mismatch. Text compression

9. Compression algorithms Statistical compression Huffman Coding, Arithmetic Coding Dictionary Compression Static Dictionary, Adaptive Dictionary Predictive Compression prediction with partial matching, Burrows- Wheeler transform and context mixing

10. Methodology Experiment setup

11. Methodology ToolsVersionAlgorithmsLevels gzip1.3.3LZ77+Huffman coding1-9 lzo2.03LZ7737 levels lzma4.32.7LZMA1-9 ncompress4.2.4.2. origLZWdefault lzpxj1.2hLZP + PPM1 flzpv1LZ77+ PPMdefault srank1.1Symbol Ranking in BWT1-8 bzip21.0.5LZ77+BWT1-9 paq9aContext Mixingnot evaluate further

12. Methodology File namesSize(B)File namesSize(B) A10.jpg842468mean.wma1462495 sample.html1058244nb.swf3861613 Flash.pdf4526946qq.exe842468 Heart.mp32313950rafale.bmp4149414 mcmd.bin2760821xslspec.xml1584495

13. RESULTS: Transmission Impact SendingReceiving Packet Sizes (UDP)

14. RESULTS: Transmission Impact SendingReceiving Transmission Rate (UDP)

15. RESULTS: Compression Impact Hard-to-compress files

16. RESULTS: Compression Impact Hard-to-compress files Energy required to compress and send JPG, MP3, WMA and EXE files

17. RESULTS: Compression Impact Hard-to-compress files Energy required to receive and decompress JPG, MP3, WMA and EXE files

18. RESULTS: Compression Impact Hard-to-compress files Total energy required to transmit JPG, MP3, WMA and EXE files

19. RESULTS: Compression Impact The best ratio/time of the compression programs and the corresponding ratio

20. RESULTS: Compression Impact Easy-to-compress files Energy required to send BIN, HTML, BMP and XML files

21. RESULTS: Compression Impact Easy-to-compress files Energy required to receive BIN, HTML, BMP and XML files

22. RESULTS: Compression Impact Easy-to-compree files Total energy required to transmit BIN, HTML, BMP and XML files

23. RESULTS: Compression Impact Compressible files Energy required to compress and send PDF and SWF files

24. RESULTS: Compression Impact Compressible files Energy required to receive and decompress PDF and SWF files

25. RESULTS: Compression Impact Compressible files Total energy required to transmit PDF and SWF files

26. Examples PagesCNNFacebookMSN Size862951B609868B633693B Uplinkcat: 5.592 Jcat: 3.960 Jcat: 4.118 J lzo: 3.205 Jgzip: 1.523 Jlzo: 2.286 J 57.31%38.46%55.51% Downlinkcat: 2.611 Jcat: 1.853 Jcat: 1.928 J lzma: 1.141 Jgzip: 0.536 Jlzo: 0.971 J 43.70%28.93%50.36% Overallcat: 8.203 Jcat: 5.813 Jcat: 6.046 J gzip: 4.023 Jgzip: 2.059 Jlzo: 3.257 J 49.04%35.42%53.87%

27. Conclusions Hard-to-compress files Direct sending -JPG, MP3, EXE and WMA Easy-to-compress files Compressing first -BIN, HTML, BMP and XML Compressible files Depending on circumstance -PDF and SWF Generic compression programs providing great energy savings. -gzip, lzma and lzo Energy saving with proper usage of compression in wireless networks -Uplink: ~57% -Downlink: ~50% -Overall: ~50%

28. Future Study Energy efficiency-driven transmission Other compression algorithms and programs Other traffic, wireless interface behavior Energy consumption of 3G devices Modeling energy consumption of compression

29. QUESTIONS?