Abstract
As the concept of merging the capabilities of mobile devices and cloud computing is becoming increasingly popular, an important question arises: how to optimally schedule services/tasks between the device and the cloud. The main objective of this paper is to investigate the possibilities for using a decision module on mobile devices in order to autonomously optimize the execution of services within the framework of Mobile Cloud Computing while taking context into account. A novel model of the decision module with learning capabilities, service-oriented architecture, and service selection optimization algorithm are proposed to solve this problem. To achieve autonomous, online learning on mobile devices, we apply supervised learning. Information about the context, task description, the decision made and its results such as calculation time or power consumption are stored and form training data for a supervised learning algorithm, which updates the knowledge used by the decision module to determine the optimal place for the execution of a given type of task. To verify the solution proposed, service-oriented mobile processing systems for multimedia file conversion have been developed and series of experiments have been executed. Results show that the decision module has become more efficient in assigning the task to either the mobile device or cloud resources.
Article PDF
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.Avoid common mistakes on your manuscript.
References
Roberts, J. R., Incorporated, M.: Mobile Tech Report 2014: Technology news from 2013 and predictions and insights about 2014. Mindwarm Incorporated (2014)
Buyya, R., Yeo, C. S., Venugopal, S., Broberg, J., Brandic, I.: Cloud computing and emerging IT platforms: vision, hype, and reality for delivering computing as the 5th utility. Futur. Gener. Comput. Syst. 25(6), 599–616 (2009)
Cushing, R., Koulouzis, S., Belloum, A., Bubak, M.: Applying workflow as a service paradigm to application farming. Concurrency and Computation: Practice and Experience 26(6), 1297–1312 (2014)
Cushing, R., Putra, G. H. H., Koulouzis, S., Belloum, A., Bubak, M., De Laat, C.: Distributed computing on an ensemble of browsers. IEEE Internet Comput. 17(5), 54–61 (2013)
Kumar, K., Lu, Y. -H.: Cloud computing for mobile users: can offloading computation save energy? Computer 43(4), 51–56 (2010)
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)
Shiraz, M., Gani, A., Shamim, A., Khan, S., Ahmad, R. W.: Energy efficient computational offloading framework for mobile cloud computing. Journal of Grid Computing 13(1), 1–18 (2015)
Fernando, N., Loke, S. W., Rahayu, W.: Mobile cloud computing: a survey. Future Gener. Comput. Syst. 29(1), 84–106 (2013)
Ma, R. K. K., Wang, C. -L.: Lightweight application-level task migration for mobile cloud computing. In: IEEE 26th International Conference on Advanced Information Networking and Applications (AINA), 2012, pp 550–557 (2012)
Nawrocki, P., Sobon, M.: Public cloud computing for software as a service platforms. Comput. Sci. 15(1), 89–103 (2014)
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, EuroSys ’11, pp 301–314. ACM, New York, NY, USA (2011)
Satyanarayanan, M.: Mobile computing: the next decade, pp 5:1–5:6. ACM, New York, NY, USA (2010)
Dinh, H. T., Lee, C., Niyato, D., Wang, P.: A survey of mobile cloud computing: architecture, applications, and approaches. Wirel. Commun. Mob. Comput. 13(18), 1587–1611 (2013)
Yang, X., Pan, T., Shen, J.: On 3g mobile e-commerce platform based on cloud computing. In: 3rd IEEE International Conference on Ubi-Media Computing (U-Media), 2010, pp 198–201 (2010)
Li, J.: Study on the development of mobile learning promoted by cloud computing. In: 2nd International Conference on Information Engineering and Computer Science (ICIECS), 2010, pp 1–4 (2010)
Doukas, C., Pliakas, T., Maglogiannis, I.: Mobile healthcare information management utilizing cloud computing and android os. In: Engineering in Medicine and Biology Society (EMBC), 2010 Annual International Conference of the IEEE, pp 1037–1040 (2010)
Tang, W. -T., Hu, C. -M., Hsu, C. -Y.: A mobile phone based homecare management system on the cloud. In: 3rd International Conference on Biomedical Engineering and Informatics (BMEI), 2010, vol. 6, pp 2442–2445 (2010)
Wang, S., Dey, S.: Rendering adaptation to address communication and computation constraints in cloud mobile gaming. In: Global Telecommunications Conference (GLOBECOM 2010), 2010 IEEE, pp 1–6 (2010)
Karadimce, A., Davcev, D.: Building context-rich mobile cloud services for mobile cloud applications. In: Mesquita, A., Peres, P. (eds.) Proceedings of the 2nd European Conference on Social Media 2015: ECSM 2015, pp 505–513 (2015)
Ye, Z., Chen, X., Li, Z.: Video based mobile location search with large set of sift points in cloud. In: Proceedings of the 2010 ACM Multimedia Workshop on Mobile Cloud Media Computing, MCMC ’10, pp 25–30. ACM, New York, NY, USA (2010)
Ahmed, E., Gani, A., Sookhak, M., Hamid, S. H. A., Xia, F.: Application optimization in mobile cloud computing. J. Netw. Comput. Appl. 52(C), 52–68 (2015)
Ivesic, K., Skorin-Kapov, L., Matijasevic, M.: Cross-layer QoE-driven admission control and resource allocation for adaptive multimedia services in LTE. J. Netw. Comput. Appl. 46(0), 336–351 (2014)
Panait, L., Luke, S.: Cooperative multi-agent learning: the state of the art. Auton. Agent. Multi-Agent Syst. 11, 2005 (2005)
Stone, P., Veloso, M.: Multiagent systems: a survey from the machine learning perspective. Auton. Robot. 8, 345–383 (2000)
Sniezynski, B.: A strategy learning model for autonomous agents based on classification. Int. J. Appl. Math. Comput. Sci. 25(3), 471–482 (2015)
Bagchi, S.: The software architecture for efficient distributed interprocess communication in mobile distributed systems. Journal of Grid Computing 12(4), 615–635 (2014)
Verbelen, T., Stevens, T., De Turck, F., Dhoedt, B.: Graph partitioning algorithms for optimizing software deployment in mobile cloud computing. Futur. Gener. Comput. Syst. 29(2), 451–459 (2013). Special section: Recent advances in e-Science
Khan, A. N., Mat Kiah, M. L., Khan, S. U., Madani, S. A.: Towards secure mobile cloud computing: a survey. Future Gener. Comput. Syst. 29(5), 1278–1299 (2013)
Lin, H., Xu, L., Mu, Y., Wu, W.: A reliable recommendation and privacy-preserving based cross-layer reputation mechanism for mobile cloud computing. Future Gener. Comput. Syst. (2014)
Huang, D., Zhou, Z., Xu, L., Xing, T., Zhong, Y.: Secure data processing framework for mobile cloud computing. In: IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), 2011, pp 614– 618 (2011)
Khan, A. N., Mat Kiah, M. L., Ali, M., Shamshirband, S., Khan, A. U. R.: A cloud-manager-based re-encryption scheme for mobile users in cloud environment: a hybrid approach. J. Grid Comput. 13(4), 651–675 (2015)
Huerta-Canepa, G., Lee, D.: A virtual cloud computing provider for mobile devices. In: Proceedings of the 1st ACM Workshop on Mobile Cloud Computing & Services: Social Networks and Beyond, MCS ’10, pp 6:1–6:5. ACM, New York, NY, USA (2010)
Cheng, R. K. Balan J., Satyanarayanan, M.: Exploiting Rich Mobile Environment. Technical Report Technical Report Carnegie Mellon university-CS-05-199. Carnegie Mellon University (2005)
Liu, R., Yuan, X., Xu, J., Chen, J., Zeng, Y., Cao, M., Liu, J., Xu, L., Fang, Q.: A novel server selection approach for mobile cloud streaming service. Simul. Model. Pract. Theory 50, 72–82 (2015). Special Issue on Resource Management in Mobile Clouds
Nawrocki, P., Reszelewski, W.: Resource usage optimization in mobile cloud computing. Comput. Commun. 99(C), 1–12 (2017)
Park, K. -L., Yoon, U. H., Kim, S. -D.: Personalized service discovery in ubiquitous computing environments. IEEE Pervasive Comput. 8(1), 58–65 (2009)
Khan, A. U. R., Othman, M., Khan, A. N., Abid, S. A., Madani, S. A.: Mobibyte: an application development model for mobile cloud computing. J. Grid Comput. 13(4), 605–628 (2015)
Kumar, N., Iqbal, R., Misra, S., Rodrigues, J. J. P. C.: Bayesian coalition game for contention-aware reliable data forwarding in vehicular mobile cloud. Futur. Gener. Comput. Syst. 48, 60–72 (2015). Special Section: Business and Industry Specific Cloud
Liu, Q., Jian, X., Hu, J., Zhao, H., Zhang, S.: An optimized solution for mobile environment using mobile cloud computing. In: 5th International Conference on Wireless Communications, Networking and Mobile Computing, 2009. Wicom ’09, pp 1–5 (2009)
Skalkowski, K., Zielinski, K.: Automatic adaptation of soa systems supported by machine learning. In: Camarinha-Matos, L. M., Tomic, S., Graça, P. (eds.) Technological Innovation for the Internet of Things, vol. 394 of IFIP Advances in Information and Communication Technology, pp 61–68. Springer, Berlin (2013)
Sniezynski, B.: Agent-based adaptation system for service-oriented architectures using supervised learning. Procedia Computer Science 29, 1057–1067 (2014)
Anagnostopoulos, T., Anagnostopoulos, C., Hadjiefthymiades, S.: Mobility prediction based on machine learning. In: 2011 IEEE 12th International Conference on Mobile Data Management, vol. 2, pp 27–30 (2011)
Shi, C., Pandurangan, P., Ni, K., Yang, J., Ammar, M., Naik, M., Zegura, E.: Ic-cloud: Computation Offloading to an Intermittently-Connected Cloud. Technical report (2013)
Donohoo, B. K., Ohlsen, C., Pasricha, S., Xiang, Y., Anderson, C.: Context-aware energy enhancements for smart mobile devices. IEEE Trans. Mob. Comput. 13(8), 1720–1732 (2014)
Zhang, Y., Niyato, D., Wang, P.: Offloading in mobile cloudlet systems with intermittent connectivity. IEEE Trans. Mob. Comput. 14(12), 2516–2529 (2015)
Eom, H., Figueiredo, R., Cai, H., Zhang, Y., Huang, G.: Malmos: machine learning-based mobile offloading scheduler with online training. In: 2015 3rd IEEE International Conference on Mobile Cloud Computing, Services, and Engineering, pp 51–60 (2015)
Nawrocki, P., Sniezynski, B., Czyzewski, J.: Learning agent for a service-oriented context-aware recommender system in heterogeneous environment. Computing and Informatics 35(5), 1005–1026 (2016)
Malawski, M., Kuźniar, M., Wójcik, P., Bubak, M.: How to use google app engine for free computing. IEEE Internet Comput. 17(1), 50–59 (2013)
Witten, I. H., Frank, E.: Data mining: Practical machine learning tools and techniques with java implementations. Morgan Kaufmann (1999)
Marsan, R.J.: Weka for android. https://github.com/rjmarsan/weka-for-android
Zhang, L., Tiwana, B., Qian, Z., Wang, Z., Dick, R. P., Mao, Z. M., Yang, L.: Accurate online power estimation and automatic battery behavior based power model generation for smartphones. In: Proceedings of the Eighth IEEE/ACM/IFIP International Conference on Hardware/Software Codesign and System Synthesis, CODES/ISSS ’10, pp 105–114. ACM, New York, NY, USA (2010)
Acknowledgements
The research presented in this paper was supported by the Polish Ministry of Science and Higher Education under AGH University of Science and Technology Grant 11.11.230.124. We thank Małgorzata PłaŻek, Jakub CzyŻewski and Daniel Olszowski for assistance with implementation and testing.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
About this article
Cite this article
Nawrocki, P., Sniezynski, B. Autonomous Context-Based Service Optimization in Mobile Cloud Computing. J Grid Computing 15, 343–356 (2017). https://doi.org/10.1007/s10723-017-9406-2
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10723-017-9406-2