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A New Context-Sensitive Decision Making System for Mobile Cloud Offloading


Affiliations
1 Institute of Information, Gazi University, Ankara, Turkey
2 Department of Computer Engineering, Gazi University, Ankara, Turkey
 

Recently, with the rapid spread use of mobile devices, some problems have begun to emerge. The most important of these are that the mobile devices batteries’ life may be short and that these devices may be in some cases. The complex tasks that must be addressed to solve such problems on mobile devices can be transferred to the cloud environment when appropriate conditions are met. The decision to offload to the cloud environment at this stage is very important. In this thesis, a context-aware decision-making system has been developed for offloading to cloud environments. Unlike similar tasks, the processes determined for transfer to the cloud are not run randomly, but rather according to the mobile user's application usage habits. The developed system was implemented in a real environment for one month. According to the results, it was determined that processes transferred to the cloud were completed in less time and consumed less energy.

Keywords

Mobile Cloud Offloading, Mobile Cloud Computing, Context-Aware System, Forecasting, Dynamic Estimation, Energy-Efficiency.
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  • S. Hossain, (2014) “What is mobile cloud computing?”, https://www.ibm.com/blogs/cloudcomputing/ 2014/01/02/what-is-mobile-cloud-computing.

  • T.Y. Lin, T.A. Lin, C.H. Hsu and C.T. King, (2013) “Context-aware decision engine for mobile cloud offloading”, IEEE Wireless Communications and Networking Conference Workshops (WCNCW), Shangai, pp. 111–116.

  • H. Wu, Q. Wang and K. Wolter, (2013) “Tradeoff between performance improvement and energy saving in mobile cloud offloading systems”, IEEE International Conference on Communications Workshops (ICC), Berlin, pp728–732.

  • F. Xia, F. Ding, J. Li, X. Kong, L. T. Yang and J. Ma, (2014) “Phone2Cloud: Exploiting computation offloading for energy saving on smartphones in mobile cloud computing”, Information Systems Frontiers, Vol. 16, pp95-111.

  • Y. Hao, M. Chen, L. Hu, M S. Hossain and A. Ghoneim, (2018) “Energy efficient task caching and offloading for mobile edge computing”, IEEE Access, Vol. 6, pp11365–11373.

  • S. Kosta, A. Aucinas, P. Hui, R. Mortier and X. Zhang, (2012) “ThinkAir: Dynamic resource allocation and parallel execution in the cloud for mobile code offloading”, INFOCOM 2012 Proceedings IEEE, Orlando, pp945–953.

  • B.G. Chun, S. Ihm, P. Maniatis, M. Naik and A. Patti, (2011) “CloneCloud: Elastic execution between mobile device and cloud”, Proceedings of the Sixth Conference on Computer Systems, New York, pp301–314.

  • W. T. Su and K. S. Ng, (2013) “Mobile cloud with smart offloading system”, IEEE/CIC International Conference on Communications in China (ICCC), Xi'an, pp680–685.

  • AsyncTask, (2016), https://developer.android.com/reference/android/os/AsyncTask.html.

  • K. Kumar, J. Liu, Y. H. Lu and B. Bhargava, (2013) “A survey of computation offloading for mobile systems”, Mob. Netw. Appl., Vol. 18, pp129–140.

  • B. Zhou, A. V. Dastjerdi, R. N. Calheiros, S. N. Srirama and R. Buyya, (2015) “A context sensitive offloading scheme for mobile cloud computing service”, IEEE 8th International Conference on Cloud Computing, New York, pp869–876.

  • C. M. Magurawalage, K. Yang, L. Hu and J. Zhang, (2014) “Energy-efficient and network-aware offloading algorithm for mobile cloud computing”, Comput. Netw., Vol. 74, pp22–33.

  • K. H. Lim and B. D. Lee, (2014) “History-based dynamic estimation of energy consumption for mobile applications”, 16th International Conference on Advanced Communication Technology, Pyeongchang, pp714–718.

  • R. Aldmour, S. Yousef, M. Yaghi, S. Tapaswi, K. Pattanaik and M. Cole, (2017) “New cloud offloading algorithm for better energy consumption and process time”, Int. J. Syst. Assur. Eng. Manag., Vol. 8, pp730–733.

  • L. Li, X. Zhang, K. Liu, F. Jiang and J. Peng, (2018) “An energy-aware task offloading mechanism in multiuser mobile-edge cloud computing”, Mobile Information Systems.

  • M. M. Islam, M. A. Razzaque, M. M. Hassan, W. N. Ismail and B. Song, (2017) “Mobile cloudbased big healthcare data processing in smart cities”, IEEE Access, Vol. 5, pp11887–11899.

  • S. M. A. Karim and J. J. Prevost, (2017) “A machine learning based approach to mobile cloud offloading”, Computing Conference, London, pp675–67.

  • M. Jia, J. Cao and L. Yang, (2014) “Heuristic offloading of concurrent tasks for computationintensive applications in mobile cloud computing”, IEEE Conference on Computer Communications Workshops, Toronto, pp352–357.

  • H. Eom, P. S. Juste, R. Figueiredo, O. Tickoo, R. Illikkal and R. Iyer, (2013) “Machine learningbased runtime scheduler for mobile offloading framework”, IEEE/ACM 6th International Conference on Utility and Cloud Computing, Dresden, pp17–25.

  • H. Flores and S. Srirama, (2013) “Adaptive code offloading for mobile cloud applications: exploiting fuzzy sets and evidence-based learning”, Proceeding of the Fourth ACM Workshop on Mobile Cloud Computing and Services, New York, pp9–16.

  • M. R. Ra, (2013) “Cloud-enabled mobile sensing systems”, PhD Thesis, University of Southern California.

  • Gaurav, N. Kaushik and J. Bhardwaj, (2014) “A computation offloading framework to optimize makespan in mobile cloud computing environment”, Int. J. Adv. Comput. Res., Vol. 4, pp442-449.

  • R. R. de Oliveira, N. M. S. Schirmer, M. Machry and T. C. Ferreto, (2017) “A transparent code offloading technique for Android devices”, 13th International Wireless Communications and Mobile Computing Conference, Valencia, pp1078–1083.

  • S. Yang et al., (2013) “Fast dynamic execution offloading for efficient mobile cloud computing”, IEEE International Conference on Pervasive Computing and Communications, San Diego, pp 20–28.

  • E. Meskar, T. D. Todd, D. Zhao and G. Karakostas, (2017) “Energy aware offloading for competing users on a shared communication channel”, IEEE Transactions on Mobile Computing, Vol. 16, pp8796.

  • S. Yang, D. Kwon, H. Yi, Y. Cho, Y. Kwon and Y. Paek, (2014) “Techniques to minimize state transfer costs for dynamic execution offloading in mobile cloud computing”, IEEE Transactions On Mobile Computing, Vol. 13, pp2648-2660.

  • SQLite, (2016) http://www.sqlite.org.

  • M. E. Khoda, M. A. Razzaque, A. Almogren, M. M. Hassan, A. Alamri and A. Alelaiwi, (2016) “Efficient computation offloading decision in mobile cloud computing over 5G network”, Mob. Netw. Appl., Vol. 21, pp777–792.

  • M. R. Rahimi, N. Venkatasubramanian and A. V. Vasilakos, (2013) “MuSIC: Mobility-aware optimal service allocation in mobile cloud computing”, IEEE Sixth International Conference on Cloud Computing, Santa Clara, pp75–82.

  • K. Pandi and H. Charaf., (2013) “Performance metrics based mobile resource management”, IEEE International Conference on System Science and Engineering, Budapest, pp329-333.

  • A. R. S. Nugroho, (2016) “Exploring privacy leakage from the resource usage patterns of mobile apps”, PhD Thesis, University Of Arkansas.

  • E. Cuervo et al., (2010) “MAUI: Making smartphones last longer with code offload”, Proceedings of the 8th International Conference on Mobile Systems, Applications, and Services, New York, pp 49– 62.

  • C. Chang, (2015) “A Framework for Energy-efficient Mobile Cloud Offloading”, University of Tartu.

  • N. I. M. Enzai and M. Tang, (2014) “A taxonomy of computation offloading in mobile cloud computing”, 2nd IEEE International Conference on Mobile Cloud Computing, Services, and Engineering, Oxford, pp19–28.

  • K. Korhonen, (2011) “Predicting mobile devices battery life”, Master Thesis, Aalto University Finland.

  • J. M. Kang, C. P. Park, S. S. Seo, M. J. Choi, J. W.Hong (2008) “User-Centric predicting for battery lifetime of mobile devices”, Challenges for Next Generation Network Operations and Service Management, pp531-534.

  • L. Pu, J. Xu, X. Jin and J. Zhang, (2013) “SmartVirtCloud: virtual cloud assisted application offloading execution at mobile devices' discretion”, Wireless Communications and Networking Conference, Shanghai, pp4398–4403.

  • Y. Tao, Y. Zhang, and Y. Ji, (2015) “Efficient computation offloading strategies for mobile cloud computing”, IEEE 29th International Conference on Advanced Information Networking and Applications, Gwangiu, pp626–633.

  • G. Orsini, D. Bade and W. Lamersdorf, (2018) “CloudAware: Empowering context-aware selfadaptation for mobile applications”, Transactions on Emerging Telecommunications Technologies, Vol. 29.

  • S. Yan, C. Shanzhi and X. Xiang, (2018) “MAGA: A mobility-aware computation offloading decision for distributed mobile cloud computing”, IEEE Internet of Things Journal, Vol. 5, pp164-174.


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  • A New Context-Sensitive Decision Making System for Mobile Cloud Offloading

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Authors

Mustafa Tanrıverdi
Institute of Information, Gazi University, Ankara, Turkey
M. Ali Akcayol
Department of Computer Engineering, Gazi University, Ankara, Turkey

Abstract


Recently, with the rapid spread use of mobile devices, some problems have begun to emerge. The most important of these are that the mobile devices batteries’ life may be short and that these devices may be in some cases. The complex tasks that must be addressed to solve such problems on mobile devices can be transferred to the cloud environment when appropriate conditions are met. The decision to offload to the cloud environment at this stage is very important. In this thesis, a context-aware decision-making system has been developed for offloading to cloud environments. Unlike similar tasks, the processes determined for transfer to the cloud are not run randomly, but rather according to the mobile user's application usage habits. The developed system was implemented in a real environment for one month. According to the results, it was determined that processes transferred to the cloud were completed in less time and consumed less energy.

Keywords


Mobile Cloud Offloading, Mobile Cloud Computing, Context-Aware System, Forecasting, Dynamic Estimation, Energy-Efficiency.

References