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 machine-learning 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 works. 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.
About author: Wei-Qing Liu received his B.E. degree in computer science and technology from the University of Science and Technology of China (USTC), Hefei, in 2011. He is currently a Ph.D. candidate at the School of Computer Science and Technology in USTC, Hefei. His research interests include cloud computing, mobile computing and big data processing.
Cite this article:
Wei-Qing, Liu Jing Li.An Approach to Automatic Performance Prediction for Cloud-enhanced Mobile Applications with Sparse Data[J] Journal of Computer Science and Technology, 2017,V32(5): 936-956
 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. 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. 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. 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. 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. 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. Kumar K, Lu Y H. Cloud computing for mobile users:Can offloading computation save energy? Computer, 2010, 43(4):51-56. Wang C, Li Z Y. Parametric analysis for adaptive computation offloading. ACM SIGPLAN Notices, 2004, 39(6):119-130. 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. 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. 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. 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. Hecht-Nielsen R. Theory of the back-propagation neural network. Neural Networks, 1988, 1(Supplement1):445-448. 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. 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. 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. 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. Bengio Y, Ducharme R, Vincent P, Janvin C. A neural probabilistic language model. Journal of Machine Learning Research, 2003, 3:1137-1155. 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. Carbone M, Rizzo L. Dummynet revisited. ACM SIGCOMM Computer Communication Review, 2010, 40(2):12-20. 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. 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. Bergstra J, Bengio Y. Random search for hyper-parameter optimization. The Journal of Machine Learning Research, 2012, 13:281-305. 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. Cormen T H, Leiserson C E, Rivest R L, Stein C. Introduction to Algorithms (3rd edition). The MIT Press, 2009. 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. 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. 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.