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Energy-aware task scheduling in mobile cloud computing

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Abstract

The limited energy supply, computing, storage and transmission capabilities of mobile devices pose a number of challenges for improving the quality of service (QoS) of various mobile applications, which has stimulated the emergence of many enhanced mobile computing paradigms, such as mobile cloud computing (MCC), fog computing, mobile edge computing (MEC), etc. The mobile devices need to partition mobile applications into related tasks and decide which tasks should be offloaded to remote computing facilities provided by cloud computing, fog nodes etc. It is very important yet tough to decide which tasks to be uploaded and where they are scheduled since this could greatly impact the applications’ timeliness and mobile devices’ lifetime. In this paper, we model the task scheduling problem at the end-user mobile device as an energy consumption optimization problem, while taking into account task dependency, data transmission and other constraint conditions such as task deadline and cost. We further present several heuristic algorithms to solve it. A series of simulation experiments are conducted to evaluate the performance of the algorithms and the results show that our proposed algorithms outperform the state-of-the-art algorithms in energy efficiency as well as response time.

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References

  1. Conti, M., et al.: Research challenges towards the future internet. Comput. Commun. 34(18), 2115–2134 (2011)

    Article  Google Scholar 

  2. Satyanarayanan, M., et al.: The case for VM-based cloudlets in mobile computing. IEEE Pervasive Comput. 8(4), 14–23 (2009)

    Article  Google Scholar 

  3. Xia, Q., Liang, W., Xu, W.: Throughput maximization for online request admissions in mobile cloudlets. In: The 38th IEEE Conference on Local Computer Networks (LCN), pp. 589–596. Sydney (2013)

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

    Article  Google Scholar 

  5. Awad, A.I., El-Hefnawy, N.A., Abdelkader, H.M.: Enhanced particle swarm optimization for task scheduling in cloud computing environments. Procedia Comput. Sci. 65, 920–929 (2015)

    Article  Google Scholar 

  6. Chen, H., Zhu, X., Guo, H., Zhu, J., Qin, X., Wu, J.: Towards energy-efficient scheduling for real-time tasks under uncertain cloud computing environment. J. Syst. Softw. 99, 20–35 (2015)

    Article  Google Scholar 

  7. Wu, X., Deng, M., Zhang, R., Zeng, B., Zhou, S.: A task scheduling algorithm based on QoS-driven in cloud computing. Procedia Comput. 17, 1162–1169 (2013)

    Article  Google Scholar 

  8. Lee, Y.C., Wang, C., Zomaya, A.Y., Zhou, B.B.: Profit-driven scheduling for cloud services with data access awareness. J. Parallel Distrib. Comput. 72, 591–602 (2012)

    Article  Google Scholar 

  9. Panda, S.K., Gupta, I., Jana, P.K.: Allocation-aware task scheduling for heterogeneous multi-cloud systems. Procedia Comput. 50, 176–184 (2015)

    Article  Google Scholar 

  10. Garey, M.R., Johnson, D.S.: Computers and Intractability: A Guide to the Theory of NP-Completeness. W.H. Freeman, New York (1979)

    MATH  Google Scholar 

  11. Hung, P.P., Bui, T.A., Huh, E.N.: A new approach for task scheduling optimization in mobile cloud computing. Lect. Notes Electr. Eng. 301, 211–220 (2014)

    Article  Google Scholar 

  12. Zhao, Y., Chen, L., Li, Y., et al.: RAS: a task scheduling algorithm based on resource attribute selection in a task scheduling framework. In: The International Conference on Internet and Distributed Computing Systems, pp. 106–119. Berlin (2013)

  13. Deng, S., Huang L, Wu H, et al.: Constraints-Driven Service Composition in Mobile Cloud Computing. In: IEEE International Conference on Web Services, pp. 228-235, San Francisco (2016)

  14. Wang, J., Tang, J., Xue, G., et al.: Towards energy-efficient task scheduling on smart phones in mobile crowd sensing systems. Comput. Netw. 115(C), 100–109 (2017)

    Article  Google Scholar 

  15. Razaque, A., Vennapusa, N., Soni, N., Janapati, G.: Task Scheduling in Cloud Computing. In: Proceedings of the IEEE Long Island Systems, Applications and Technology Conference (LISAT), pp. 1–5. Farmingdale (2016)

  16. Sindhu, S.: Task scheduling in cloud computing. Int. J. Adv. Res. Comput. Eng. Technol. 4, 3019–3023 (2015)

    Google Scholar 

  17. Lin, X., Wang, Y., Xie, Q., et al.: Task scheduling with dynamic voltage and frequency scaling for energy minimization in the mobile cloud computing environment. IEEE Trans. Serv. Comput. 8(2), 175–186 (2015)

    Article  Google Scholar 

  18. Mahmood, A., Khan, S.: Hard real-time task scheduling in cloud computing using an adaptive genetic algorithm. Computers 6(2), 15 (2017)

    Article  Google Scholar 

  19. Tsai, J.T., Fang, J.C., Chou, J.H.: Optimized task scheduling and resource allocation on cloud computing environment using improved differential evolution algorithm. Comput. Oper. Res. 40, 3045–3055 (2013)

    Article  MATH  Google Scholar 

  20. Liu, S., Quan, G., Ren, S.: On-line scheduling of real-time services with profit and penalty. In: Proceedings of the 2011 ACM Symposium on Applied Computing, pp. 1476–1481. Taiwan (2011)

  21. Kim, K.H., Beloglazov, A., Buyya, R.: Power-aware provisioning of virtual machines for real-time cloud services. Concurr. Comput. 23, 1491–1505 (2011)

    Article  Google Scholar 

  22. Deniziak, S.; Ciopinski, L.; Pawinski, G.; Wieczorek, K.; Bak, S. Cost optimization of real-time cloud applications using developmental genetic programming. In: Proceedings of the IEEE/ACM 7th International Conference on Utility and Cloud Computing (UCC), pp. 774–779. London (2014)

  23. Guo, S., Xiao, B., Yang, Y., et al.: Energy-efficient dynamic offloading and resource scheduling in mobile cloud computing. In: IEEE International Conference on Computer Communications INFOCOM, pp. 1–9. San Francisco (2016)

  24. Holland, J.H.: Adaptation in Natural and Artificial Systems. MIT Press, Cambridge, MA (1992)

    Google Scholar 

  25. Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning, pp. 2104–2116. Addison-Wesley, Boston (1990)

    Google Scholar 

  26. Tang, C., Xiao, S., Wei, X., Hao, M., Chen, W.: Energy-Efficient and Deadline-satisfied Task Scheduling in Mobile Cloud Computing. In: 2018 IEEE International Conference on Big Data and Smart Computing. Shanghai (2018)

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Acknowledgment

This research was supported in part by the Jiangsu Province Natural Science Foundation of China under Grant No. BK20150201 and by National Natural Science Foundation of China under Grant No. 61402521, in part by the National Natural Science Foundation and Shanxi Provincial Peoples’ Government Jointly Funded Project of China for Coal Base and Low Carbon under Grant U1510115, and in part by the Qing Lan Project and China Postdoctoral Science Foundation under Grant 2013T60574 and Grant 2016M601910.

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Authors and Affiliations

  1. School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, China

    Chaogang Tang, Mingyang Hao & Wei Chen

  2. Nanjing Telecommunication Technology Research Institute, Nanjing, China

    Xianglin Wei

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  1. Chaogang Tang
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  2. Mingyang Hao
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  3. Xianglin Wei
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  4. Wei Chen
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Correspondence to Xianglin Wei.

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Tang, C., Hao, M., Wei, X. et al. Energy-aware task scheduling in mobile cloud computing. Distrib Parallel Databases 36, 529–553 (2018). https://doi.org/10.1007/s10619-018-7231-7

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  • DOI: https://doi.org/10.1007/s10619-018-7231-7

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