Outsourcing Decisions in Mobile Image Processing Oslo 27.04.2012 - - PowerPoint PPT Presentation

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Outsourcing Decisions in Mobile Image Processing Oslo 27.04.2012 - - PowerPoint PPT Presentation

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Power and Latency Impacts of Outsourcing Decisions in Mobile Image Processing Oslo 27.04.2012 Outline 5/2/2012 Team members Introduction Application description Test Environment Measurements and results Analysis of the

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Power and Latency Impacts of Outsourcing Decisions in Mobile Image Processing

Oslo 27.04.2012

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Outline

 Team members  Introduction  Application description  Test Environment  Measurements and results  Analysis of the results  Outsourcing decision making algorithm  Future work  Conclusion

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Team Memebers

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 Niklas Dürr and Nicolas Oliver

Stamer

School of Business Informatics and Mathematics

University of Mannheim

A5, 6

68159 Mannheim, Germany

Email: nduerr@mail.uni- mannheim.de

Email: nistamer@mail.uni- mannheim.de

 Ali Zaher and Ali Ahmad

Department of Informatics

Oslo University

P.o.Box 1080, Blindern

NO-0316 Oslo, Norway

Tel: +47 228 45581

Email: alizah@ifi.uio.no

Email: aliaah@ifi.uio.no

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Introduction

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 Early days of mobile phones: Voice and then sms.  Current days: Data Traffic (Video, images, emails,…)  data traffic has taken over voice traffic on mobile

networks already in 2010.

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Introduction

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 The world most selling phone, Nokia 1100. 2 weeks standby time.  2012 mobile phones with Quad core at 1.5 GHz, battery 1800 mAh,

connectivity: Wi-Fi: IEEE 802.11 a/b/g/n, HSDPA 21 Mbps

 2003 mobile phones with CPU ARM-9 104 MHz, battery 850 mAh,

connectivity: GSM 24 - 36 kbps

 The world most selling phone, Nokia 1100, 2 weeks standby time.  2012 mobile phones with Quad core at 1.5 GHz, battery 1800 mAh,

connectivity: Wi-Fi: IEEE 802.11 a/b/g/n, HSDPA 21 Mbps

 2003 mobile phones with CPU ARM-9 104 MHz, battery 850 mAh,

connectivity: GSM 24 - 36 kbps

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Introduction

 How do mobile phone batteries follow related

to Moore’s Law?

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Introduction

 What about sourcing out the power hungry apps to

the cloud?

 More power efficient??  Faster execution???  “Make or buy” decision from economics  Image processing algorithm, why?  April 23: Facebook offers 23 million shares and

$300 million in cash to Instagram (almost 1B$)

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Application desciption- App on Android

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 Mobile device:

"HTC Desire S"

CPU frequency: 1,0 GHz

RAM: 768 MB

 Server:

2,4 GHz Unix-based server

RAM:4 GB.

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Test Environment

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 The remote execution is implemented with Java

Sockets

 Open source image manipulating algorithms of JH

Labs

 A relatively big image with 600x300 pixels and 57

KB and a small image with 400x200 pixels and 29 KB.

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Test Environment - Algorithm selection

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Measurements and Results- Total Energy

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Measurements and Results- Energy divided

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Measurements and Results- Energy with compression

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Measurements and Results- Latency

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Measurements and Results- Latency in 3G vs WiFi

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Measurements and Results- Power and Latency in WiFi with compression

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Measurements and Results- Power and Latency in 3G with compression

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Outsourcing decision making algorithm

 Signal Strength is a factor in 3G.  Other factors: current bandwidth available, number of

users connected to the same base station.

 Log for every execution:

 The image size in bytes  The image algorithm name.  Executing locally or on the server.  Execution time  Signal strength  Connection type (whether 3G or WiFi)  Transmission time

Update the log file gradually to keep it simple. For similar Signal Strength entries, apply:

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Outsourcing decision making algorithm- Example

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Signal Strength Transmission time Image size

  • no. of

transmissions

  • 65.4

11.5 600000 1

  • 90.2

28.222 705821 1

  • 70.6

12 304581 1

  • 80.8

18.0003 242456 1

  • 65.2

13.222 705821 1

  • 71.6

12 304581 1

  • 80.0

18.0003 242456 1

  • 95.5

11.5 600000 1

  • 91.2

28.222 705821 1

  • 70.2

12 304581 1

  • 80.8

18.0003 242456 1

  • 90.2

28.222 705821 1

  • 85.4

12 304581 1

  • 90.8

18.0003 242456 1

  • 65.4

22.5 1000000 10

  • 95.8

12.5 600000 5 Signal Strength Transmission time Image size

  • no. of

transmissions

  • 96

20.583 1000000 6

  • 91

58.826 1000000 2

  • 90

39.985 1000000 2

  • 85

39.398 1000000 1

  • 81

74.242 1000000 2

  • 80

74.242 1000000 1

  • 72

39.398 1000000 1

  • 71

39.398 1000000 1

  • 70

39.398 1000000 1

  • 65

21.967 1000000 12

Logged by the phone Processed log

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Outsourcing decision making algorithm

1) Extract the needed information as log in before. 2) If the algorithm is not complex, then execute locally and log as

described before.

3) In case the algorithm is somehow complicated, we check for the

expected transmission time at the current signal strength and compare it to the recorded execution time locally. If it is smaller, then we execute on the server and wait for the server result to log.

4) In case the expected transmission time is greater than the local

execution time, then execute locally and log as described before.

5) In case the algorithm is complicated, then we check if the phone is in

power saving mode. If it is not, then we execute on the server and wait for the server result to log.

6) If the phone is in power saving mode, and the expected transmission

time at the current signal strength is smaller than local execution time, the user can decide to quit the operation or to outsource the

  • peration to the server

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Outsourcing decision making algorithm

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Future work

1) Apply the outsourcing decision making

algorithm on large data

2) Check for 4G 3) Go for bigger image sizes 4) Add more complicated image filters 5) Look at video algorithms

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Conclusion

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REFERENCES

[1] Commons, John Rogers. 1931. "Institutional Economics", American Economic Review,

  • Vol. 21, pp. 648-657

[2] Williamson, Oliver E. 1981. "The Economics of Organization: The Transaction Cost Approach", The American Journal of Sociology, 87(3), pp. 548-577

[3] Byung-Gon Chun, Sunghwan Ihm, Petros Maniatis, Mayur Naik, and Ashwin Patti.

  • 2011. CloneCloud: elastic execution between mobile device and cloud. In Proceedings of

the sixth conference on Computer systems (EuroSys ’11). ACM, New York, NY, USA, 301- 314.

[4] R. Rana, C.T. Chou, S. Kanhere, N. Bulusu and W. Hu, "Ear-Phone: An End-to-End Participatory Urban Noise Mapping System", in Proceedings of IPSN’10, April 2010.

[5] Ahmed A. Abukmail and Abdelsalam (sumi) Helal. 2007. Energy Management for Mobile Devices through Computation Outsourcing within Pervasive Smart paces.Submitted to the IEEE Transactions on Mobile Computing

[6] Mei, C., et al., Dynamic Outsourcing Mobile Computation to the Cloud. 2011, Department of Computer Science and Engineering, University of Minnesota: Twin Cities.

[7] R. Kemp, N. Palmer, T. Kielmann, and H. Bal. Cuckoo: a Computation Of floading Framework for Smartphones. In MobiCASE ’10: Proceedings of The Second International Conference on Mobile Computing, Applications, and Services, 2010.

[8] Android Interface Definition Language, http://developer.android.com/guide/developing/tools/aidl.html

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REFERENCES

[9] Bernd Girod and Vijay Chandrasekhar Stanford University, Radek Grzeszczuk Nokia Research Center and Yuriy A. Reznik Qualcomm 2011, Mobile Visual Search: Architectures, Technologies, and the Emerging MPEG Standard

[10] Nister, D.; Stewenius, H.; , "Scalable Recognition with a Vocabulary Tree," Computer Vision and Pattern Recognition, 2006 IEEE Computer Society Conference

  • n , vol.2, no., pp. 2161- 2168, 2006

[11] http://www.jhlabs.com/ip/filters/index.html

[12] http://instagr.am/

[13] http://powertutor.org/

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Q&A

Thanks for your attention

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Extra slide

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