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An Optimal Offloading Partitioning Algorithm in Mobile Cloud Computing

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Quantitative Evaluation of Systems (QEST 2016)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9826))

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Abstract

Application partitioning splits the executions into local and remote parts. Through optimal partitioning, the device can obtain the most benefit from computation offloading. Due to unstable resources at the wireless network (bandwidth fluctuation, network latency, etc.) and at the service nodes (different speed of the mobile device and cloud server, memory, etc.), static partitioning solutions in previous work with fixed bandwidth and speed assumptions are unsuitable for mobile offloading systems. In this paper, we study how to effectively and dynamically partition a given application into local and remote parts, while keeping the total cost as small as possible. We propose a novel min-cost offloading partitioning (MCOP) algorithm that aims at finding the optimal partitioning plan (determine which portions of the application to run on mobile devices and which portions on cloud servers) under different cost models and mobile environments. The simulation results show that the proposed algorithm provides a stable method with low time complexity which can significantly reduce execution time and energy consumption by optimally distributing tasks between mobile devices and cloud servers, and in the meantime, it can well adapt to environmental changes, such as network perturbation.

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Notes

  1. 1.

    The face recognition application is built using an open source code http://darnok.org/programming/face-recognition/, which implements the Eigenface face recognition algorithm.

  2. 2.

    An optimal partitioning algorithm, the code can be found in https://github.com/carlosmn/work-offload, thanks to Daniel Seidenstücker and Carlos Martín Nieto for their help.

References

  1. Yang, L., Cao, J., Yuan, Y., Li, T., Han, A., Chan, A.: A framework for partitioning and execution of data stream applications in mobile cloud computing. ACM SIGMETRICS Perform. Eval. Rev. 40(4), 23–32 (2013)

    Article  Google Scholar 

  2. Olteanu, A.-C., Ţăpuş, N.: Tools for empirical and operational analysis of mobile offloading in loop-based applications. Informatica Economica 17(4), 5–17 (2013)

    Article  Google Scholar 

  3. Wu, H., Wang, Q., Wolter, K.: Mobile healthcare systems with multi-cloud offloading. In: 2013 IEEE 14th International Conference on Mobile Data Management (MDM), vol. 2, pp. 188–193. IEEE (2013)

    Google Scholar 

  4. Wu, H.: Analysis of offloading decision making in mobile cloud computing. Ph.D. thesis, Freie Universität Berlin (2015)

    Google Scholar 

  5. Wu, H., Wang, Q., Wolter, K.: Methods of cloud-path selection for offloading in mobile cloud computing systems. In: CloudCom, pp. 443–448 (2012)

    Google Scholar 

  6. Cuervo, E., Balasubramanian, A., Cho, D.-K., Wolman, A., Saroiu, S., Chandra, R., Bahl, P.: Maui: making smartphones last longer with code offload. In: Proceedings of the 8th International Conference on Mobile Systems, Applications, and Services, pp. 49–62. ACM (2010)

    Google Scholar 

  7. Chun, B.-G., Ihm, S., Maniatis, P., Naik, M., Patti, A.: Clonecloud: elastic execution between mobile device and cloud. In: Proceedings of the Sixth Conference on Computer Systems, pp. 301–314. ACM (2011)

    Google Scholar 

  8. Hendrickson, B., Kolda, T.G.: Graph partitioning models for parallel computing. Parallel Comput. 26(12), 1519–1534 (2000)

    Article  MathSciNet  MATH  Google Scholar 

  9. Stoer, M., Wagner, F.: A simple min-cut algorithm. J. ACM (JACM) 44(4), 585–591 (1997)

    Article  MathSciNet  MATH  Google Scholar 

  10. Ali, K., Lhoták, O.: Application-only call graph construction. In: Noble, J. (ed.) ECOOP 2012. LNCS, vol. 7313, pp. 688–712. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

  11. Boykov, Y., Veksler, O., Zabih, R.: Fast approximate energy minimization via graph cuts. IEEE Trans. Pattern Anal. Mach. Intell. 23(11), 1222–1239 (2001)

    Article  Google Scholar 

  12. Liu, Y., Lee, M.J.: An effective dynamic programming offloading algorithm in mobile cloud computing system. In: Wireless Communications and Networking Conference (WCNC), 2014 IEEE, pp. 1868–1873. IEEE (2014)

    Google Scholar 

  13. Wu, H., Wolter, K.: Software aging in mobile devices: partial computation offloading as a solution. In: 2015 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW). IEEE (2015)

    Google Scholar 

  14. Kumar, K., Liu, J., Lu, Y.-H., Bhargava, B.: A survey of computation offloading for mobile systems. Mob. Netw. Appl. 18(1), 129–140 (2013)

    Article  Google Scholar 

  15. Niu, R., Song, W., Liu, Y.: An energy-efficient multisite offloading algorithm for mobile devices. Int. J. Distrib. Sens. Netw. 2013, 1–6 (2013)

    Google Scholar 

  16. Giurgiu, I., Riva, O., Juric, D., Krivulev, I., Alonso, G.: Calling the cloud: enabling mobile phones as interfaces to cloud applications. In: Bacon, J.M., Cooper, B.F. (eds.) Middleware 2009. LNCS, vol. 5896, pp. 83–102. Springer, Heidelberg (2009)

    Chapter  Google Scholar 

  17. Sinha, K., Kulkarni, M.: Techniques for fine-grained, multi-site computation offloading. In: Proceedings of the 2011 11th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, pp. 184–194. IEEE Computer Society (2011)

    Google Scholar 

  18. Kao, B.Y.-H., Krishnamachari, B.: Optimizing mobile computational offloading with delay constraints. In: Proceedings of the Global Communication Conference (Globecom 14), pp. 8–12 (2014)

    Google Scholar 

  19. Wu, H., Wolter, K.: Tradeoff analysis for mobile cloud offloading based on an additive energy-performance metric. In: 2014 8th International Conference on Performance Evaluation Methodologies and Tools (VALUETOOLS). ACM (2014)

    Google Scholar 

  20. Wu, H., Sun, Y., Wolter, K.: Analysis of the energy-response time tradeoff for delayed mobile cloud offloading. ACM SIGMETRICS Perform. Eval. Rev. 43, 33–35 (2015)

    Article  Google Scholar 

  21. Wu, H., Knottenbelt, W., Wolter, K.: Analysis of the energy-response time tradeoff for mobile cloud offloading using combined metrics. In: 2015 27th International Teletraffic Congress (ITC 27), pp. 134–142. IEEE (2015)

    Google Scholar 

  22. Kwon, Y.-W., Tilevich, E.: Energy-efficient and fault-tolerant distributed mobile execution. In: 2012 IEEE 32nd International Conference on Distributed Computing Systems (ICDCS), pp. 586–595. IEEE (2012)

    Google Scholar 

  23. Wu, H., Wang, Q., Wolter, K.: Tradeoff between performance improvement and energy saving in mobile cloud offloading systems. In: 2013 IEEE International Conference on Communications Workshops (ICC), pp. 728–732. IEEE (2013)

    Google Scholar 

  24. Lei, L., Zhong, Z., Zheng, K., Chen, J., Meng, H.: Challenges on wireless heterogeneous networks for mobile cloud computing. In: IEEE Wireless Communications, vol. 20, no. 3 (2013)

    Google Scholar 

  25. Zhang, Y., Liu, H., Jiao, L., Fu, X.: To offload or not to offload: an efficient code partition algorithm for mobile cloud computing. In: 2012 IEEE 1st International Conference on Cloud Networking (CLOUDNET), pp. 80–86. IEEE (2012)

    Google Scholar 

  26. Soot: a framework for analyzing and transforming Java and androidapplications. http://sable.github.io/soot/

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Author information

Authors and Affiliations

  1. Center for Applied Mathematics, Tianjin University, Tianjin, China

    Huaming Wu

  2. Department of Computing, Imperial College London, London, UK

    William Knottenbelt

  3. Institut für Informatik, Freie Universität Berlin, Berlin, Germany

    Katinka Wolter & Yi Sun

Authors
  1. Huaming Wu
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  2. William Knottenbelt
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  3. Katinka Wolter
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  4. Yi Sun
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Corresponding author

Correspondence to Huaming Wu .

Editor information

Editors and Affiliations

  1. University of Illinois , Urbana, Illinois, USA

    Gul Agha

  2. University of Antwerp , Antwerp, Belgium

    Benny Van Houdt

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© 2016 Springer International Publishing Switzerland

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Wu, H., Knottenbelt, W., Wolter, K., Sun, Y. (2016). An Optimal Offloading Partitioning Algorithm in Mobile Cloud Computing. In: Agha, G., Van Houdt, B. (eds) Quantitative Evaluation of Systems. QEST 2016. Lecture Notes in Computer Science(), vol 9826. Springer, Cham. https://doi.org/10.1007/978-3-319-43425-4_21

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  • DOI: https://doi.org/10.1007/978-3-319-43425-4_21

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-43424-7

  • Online ISBN: 978-3-319-43425-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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