Skip to main content
SpringerLink
Log in

An Approach to Automatic Performance Prediction for Cloud-Enhanced Mobile Applications with Sparse Data

  • Regular Paper
  • Published:
Journal of Computer Science and Technology Aims and scope Submit manuscript

Abstract

In mobile cloud computing (MCC), offloading compute-intensive parts of a mobile application onto the cloud is an attractive method to enhance application performance. To make good offloading decisions, history-based machinelearning techniques are proposed to predict application performance under various offloading schemes. However, the data sparsity problem is common in a realistic MCC scenario but is rarely the concern of existing work. In this paper, we employ a two-phase hybrid framework to predict performance for cloud-enhanced mobile applications, which is designed to be robust to the data sparsity. By training several multi-layer neural networks with historical execution records, the first phase automatically predicts some intermediate parameters for each execution of an application. The models learned by these neural networks can be shared among different applications, thus alleviating the data sparsity. Based on these predicted intermediate parameters and the application topology, the second phase deterministically calculates the estimated values of the performance metrics. The deterministic algorithm can partially guarantee the prediction accuracy of newly published applications even with no execution records. We evaluate our approach with a cloud-enhanced object recognition application and show that our approach can precisely predict the application performance and is robust to data sparsity.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Cuervo E, Balasubramanian A, Cho D K, Wolman A, Saroiu S, Chandra R, Bahl P. MAUI: Making smartphones last longer with code offload. In Proc. the 8th International Conference on Mobile Systems, Applications, and Services, Jun. 2010, pp.49-62.

  2. Chun B G, Ihm S, Maniatis P, Naik M, Patti A. CloneCloud: Elastic execution between mobile device and cloud. In Proc. the 6th Conference on Computer Systems, Apr. 2011, pp.301-314.

  3. Gordon M S, Jamshidi D A, Mahlke S, Mao Z M, Chen X. COMET: Code offload by migrating execution transparently. In Proc. the 10th USENIX Symposium on Operating Systems Design and Implementation, Oct. 2012, pp.93-106.

  4. Ra M R, Sheth A, Mummert L, Pillai P, Wetherall D, Govindan R. Odessa: Enabling interactive perception applications on mobile devices. In Proc. the 9th International Conference on Mobile Systems, Applications, and Services, Jun. 28-Jul. 1, 2011, pp.43-56.

  5. Kosta S, Aucinas A, Hui P, Mortier R, Zhang X W. ThinkAir: Dynamic resource allocation and parallel execution in the cloud for mobile code offloading. In Proc. IEEE INFOCOM, Mar. 2012, pp.945-953.

  6. Zhang X W, Kunjithapatham A, Jeong S, Gibbs S. Towards an elastic application model for augmenting the computing capabilities of mobile devices with cloud computing. Mobile Networks and Applications, 2011, 16(3): 270–284.

    Article  Google Scholar 

  7. Kumar K, Lu Y H. Cloud computing for mobile users: Can offloading computation save energy? Computer, 2010, 43(4): 51–56.

    Article  Google Scholar 

  8. Wang C, Li Z Y. Parametric analysis for adaptive computation offloading. ACM SIGPLAN Notices, 2004, 39(6): 119–130.

    Article  Google Scholar 

  9. Ipek E, de Supinski B R, Schulz M, McKee S A. An approach to performance prediction for parallel applications. In Lecture Notes in Computer Science 3648, Cunha J C, Medeiros P D (eds.), Springer-Verlag, 2005, pp.196-205.

  10. Narayanan D, Flinn J, Satyanarayanan M. Using history to improve mobile application adaptation. In Proc. the 3rd IEEE Workshop on Mobile Computing Systems and Applications, Dec. 2000, pp.31-40.

  11. Lee B C, Brooks D M, de Supinski B R, Schulz M, Singh K, McKee S A. Methods of inference and learning for performance modeling of parallel applications. In Proc. the 12th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, Mar. 2007, pp.249-258.

  12. Flinn J, Park S, Satyanarayanan M. Balancing performance, energy, and quality in pervasive computing. In Proc. the 22nd International Conference on Distributed Computing Systems, Jul. 2002, pp.217-226.

  13. Hecht-Nielsen R. Theory of the back-propagation neural network. Neural Networks, 1988, 1(Supplement1): 445–448.

  14. Satyanarayanan M, Bahl P, Caceres R, Davies N. The case for VM-based cloudlets in mobile computing. IEEE Pervasive Computing, 2009, 8(4): 14–23.

    Article  Google Scholar 

  15. Giurgiu I, Riva O, Alonso G. Dynamic software deployment from clouds to mobile devices. In Lecture Notes in Computer Science 7662, Narasimhan P, Triantafillou P (eds.), Springer, 2012, pp.394-414.

  16. Newton R, Toledo S, Girod L, Balakrishnan H, Madden S. Wishbone: Profile-based partitioning for sensornet applications. In Proc. the 6th USENIX Symposium on Networked Systems Design and Implementation, Apr. 2009, pp.395-408.

  17. Kim K, De La Garza J M. Evaluation of the resourceconstrained critical path method algorithms. Journal of Construction Engineering and Management, 2005, 131(5): 522–532.

    Article  Google Scholar 

  18. Bengio Y, Ducharme R, Vincent P, Janvin C. A neural probabilistic language model. Journal of Machine Learning Research, 2003, 3: 1137–1155.

    MATH  Google Scholar 

  19. Collobert R, Weston J. A unified architecture for natural language processing: Deep neural networks with multitask learning. In Proc. the 25th International Conference on Machine Learning, Jul. 2008, pp.160-167.

  20. Carbone M, Rizzo L. Dummynet revisited. ACM SIGCOMM Computer Communication Review, 2010, 40(2): 12–20.

  21. Jain M, Dovrolis C. End-to-end available bandwidth: Measurement methodology, dynamics, and relation with TCP throughput. IEEE/ACM Transactions on Networking, 2003, 11(4): 537–549.

    Article  Google Scholar 

  22. Bergstra J S, Bardenet R, Benjio Y, Kégl B. Algorithms for hyper-parameter optimization. In Proc. the 24th International Conference on Neural Information Processing Systems, Dec. 2011, pp.2546-2554.

  23. Bergstra J, Bengio Y. Random search for hyper-parameter optimization. The Journal of Machine Learning Research, 2012, 13: 281–305.

    MathSciNet  MATH  Google Scholar 

  24. Balan R K, Satyanarayanan M, Park S Y, Okoshi T. Tactics-based remote execution for mobile computing. In Proc. the 1st International Conference on Mobile Systems, Applications and Services, May 2003, pp.273-286.

  25. Cormen T H, Leiserson C E, Rivest R L, Stein C. Introduction to Algorithms (3rd edition). The MIT Press, 2009.

  26. Huang L, Jia J Z, Yu B, Chun B G, Maniatis P, Naik M. Predicting execution time of computer programs using sparse polynomial regression. In Proc. the 23rd International Conference on Neural Information Processing Systems, Dec. 2010, pp.883-891.

  27. Kwon Y, Lee S, Yi H, Kwon D, Yang S, Chun B G, Huang L, Maniatis P, Naik M, Paek Y. Mantis: Automatic performance prediction for smartphone applications. In Proc. the USENIX Conference on Annual Technical Conference, Jun. 2013, pp.297-308.

  28. Shi C, Habak K, Pandurangan P, Ammar M, Naik M, Zegura E. COSMOS: Computation offloading as a service for mobile devices. In Proc. the 15th ACM International Symposium on Mobile Ad Hoc Networking and Computing, Aug. 2014, pp.287-296.

Download references

Author information

Authors and Affiliations

  1. School of Computer Science and Technology, University of Science and Technology of China, Hefei, 230026, China

    Wei-Qing Liu & Jing Li

Authors
  1. Wei-Qing Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jing Li
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jing Li.

Electronic supplementary material

Below is the link to the electronic supplementary material.

ESM 1

(PDF 53 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Liu, WQ., Li, J. An Approach to Automatic Performance Prediction for Cloud-Enhanced Mobile Applications with Sparse Data. J. Comput. Sci. Technol. 32, 936–956 (2017). https://doi.org/10.1007/s11390-017-1774-3

Download citation

  • Received:

  • Revised:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11390-017-1774-3

Keywords

Search

Navigation