Advertisement

An Optimal Offloading Partitioning Algorithm in Mobile Cloud Computing

  • Huaming WuEmail author
  • William Knottenbelt
  • Katinka Wolter
  • Yi Sun
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9826)

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.

Keywords

Mobile cloud computing Communication networks Offloading Cost graph Application partitioning 

References

  1. 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)CrossRefGoogle Scholar
  2. 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)CrossRefGoogle Scholar
  3. 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. 4.
    Wu, H.: Analysis of offloading decision making in mobile cloud computing. Ph.D. thesis, Freie Universität Berlin (2015)Google Scholar
  5. 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. 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. 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. 8.
    Hendrickson, B., Kolda, T.G.: Graph partitioning models for parallel computing. Parallel Comput. 26(12), 1519–1534 (2000)MathSciNetCrossRefzbMATHGoogle Scholar
  9. 9.
    Stoer, M., Wagner, F.: A simple min-cut algorithm. J. ACM (JACM) 44(4), 585–591 (1997)MathSciNetCrossRefzbMATHGoogle Scholar
  10. 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)CrossRefGoogle Scholar
  11. 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)CrossRefGoogle Scholar
  12. 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. 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. 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)CrossRefGoogle Scholar
  15. 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. 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)CrossRefGoogle Scholar
  17. 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. 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. 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. 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)CrossRefGoogle Scholar
  21. 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. 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. 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. 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. 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. 26.
    Soot: a framework for analyzing and transforming Java and androidapplications. http://sable.github.io/soot/

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Huaming Wu
    • 1
    Email author
  • William Knottenbelt
    • 2
  • Katinka Wolter
    • 3
  • Yi Sun
    • 3
  1. 1.Center for Applied MathematicsTianjin UniversityTianjinChina
  2. 2.Department of ComputingImperial College LondonLondonUK
  3. 3.Institut für InformatikFreie Universität BerlinBerlinGermany

Personalised recommendations