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A Novel Multi-Objective Efficient Offloading Decision Framework in Cloud Computing for Mobile Computing Applications

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Abstract

Mobile cloud computing is the emerging paradigm to improve mobile device computation issues using cloud resources. Computation offloading is an efficient way of transferring certain tasks from mobile devices to the cloud. The computationally intensive task of the mobile application executes on the remote cloud. In computational offloading, the decision making plays a vital role to decide whether a task to be offloaded to the cloud or to execute in the local side. The existing research focused either on the offloading part of the cloud side or the context of mobile devices. However, this paper considered both the cloud side and the mobiles side to make the efficient decision offloading decision. This paper proposes a novel multi-objective efficient offloading decision framework for supporting computational offloading based on the mobile applications’ complexity and the context of mobile devices. The main purpose of this framework is to improve the mobile devices, which executes the high computational task that consumes the high battery power and CPU utilization. The proposed framework dynamically explores and decides the optimal cloud by using the enhanced particle swarm optimization algorithm. Moreover, this paper reduces the battery power consumption, virtual machine cost and makespan of the task for providing the quality of services.

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References

  1. Battery Life Concerns Mobile Users. (2005). http://edition.cnn.com/2005/TECH/ptech/09/22/phone.study. Accessed 23 Sept 2005.

  2. Shi, T., Yang, M., Li, X., Lei, Q., & Jiang, Y. (2014). An energy-efficient, time-constrained scheduling scheme in local mobile cloud. Pervasive and Mobile Computing. https://doi.org/10.1016/j.pmcj.2015.07.005.

    Article  Google Scholar 

  3. Kim, Y., Lee, K., & Shroff, N. B. (2014). An analytical framework to characterize the efficiency and delay in a mobile data offloading system. In Proceedings of the 15th ACM international symposium on Mobile ad hoc networking and computing (pp. 267–276). ACM.

  4. Gu, X., Nahrstedt, K., Messer, A., Greenberg, I., & Milojicic, D. (2003). Adaptive offloading inference for delivering applications in pervasive computing environments. In Proceedings of the first IEEE international conference on pervasive computing and communications (pp 107–114).

  5. Xiang, T., Zhang, W., Zhong, S., & Yang, J. (2017). Verifiable outsourcing of constrained nonlinear programming by particle swarm optimization in cloud. Soft Computing, 22, 1–13. https://doi.org/10.1007/s00500-017-2569-8.

    Article  Google Scholar 

  6. Kosta, S., Aucinas, A., Hui, P., Mortier, R., & Zhang, X. (2011) Unleashing the power of mobile cloud computing using thinkair. Computing Research Repository. CORR arXiv preprint arXiv:1105.3232.

  7. Cheung, M. H., & Huang, J. (2015). Dawn: Delay-aware Wi-Fi offloading and network selection. IEEE Journal on Selected Areas in Communications, 33(6), 1214–1223.

    Article  Google Scholar 

  8. Lee, K., & Shin, I. (2013). User mobility-aware decision making for mobile computation offloading. In Cyber-physical systems, networks, and applications (CPSNA) (pp. 116–119). IEEE.

  9. Mukherjee, A., Gupta, P. & De, D. (2014). Mobile cloud computing based energy efficient offloading strategies for femtocell network. In 2014 applications and innovations in mobile computing (AIMoC) (pp. 28–35). IEEE.

  10. App drain battery power. (2010). www.droidforums.net/threads/battery-drops-40-after-playing-game-for-hour.18301/. Accessed 25 Jan 2010.

  11. Balan, R. K., Satyanarayanan, M., Park, S. Y., & Okoshi, T. (2003). Tactics-based remote execution for mobile computing. In Proceedings of the 1st international conference on mobile systems, applications and services (pp. 273–286).

  12. Amoretti, M., Grazioli, A., & Zanichelli, F. (2015). A modeling and simulation framework for mobile cloud computing. In Simulation modelling practice and theory, (pp. 140–156).

  13. Yal, S., & Carter, J. (2004). A lightweight secure cyber foraging infrastructure for resource-constrained devices. In Proceedings of the sixth IEEE workshop on mobile computing systems and applications (pp. 186–195). IEEE Computer Society.

  14. Rajesh, B., Jason, F., Satyanarayanan, M., Shafeeq, S., & Hen-I, Y. (2002). The case for cyber foraging. In Proceedings of the 10th workshop on ACM SIGOPS European workshop (pp. 87–92).

  15. Kumar, K., Liu, J., Lu, Y. H., & Bhargava, B. (2013). A survey of computation offloading for mobile systems. Mobile Network Applications, 18, 129–140.

    Article  Google Scholar 

  16. Hyytia, E., Spyropoulos, T., & Ott, J. (2015). Offload (only) the right jobs: Robust offloading using the Markov decision processes. In 2015 IEEE 16th international symposium on world of wireless, mobile and multimedia networks (WoWMoM) (pp. 1–9). IEEE.

  17. Martin, P., Elgazzar, K., & Hassanein, H. S. (2016). Cloud-assisted computation offloading to support mobile services. IEEE Transactions on Cloud Computing, 4(3), 279–292.

    Article  Google Scholar 

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

    Google Scholar 

  19. Pandey, S., Wu, L., Guru, S. M., & Buyya, R. (2010). A particle swarm optimization-based heuristic for scheduling workflow applications in cloud computing environments. In IEEE international conference on advance information networking and applications (pp. 400–407).

  20. Zhou, B., Dastjerdi, A. V., Calheiros, R. N., Srirama, S. N. & Buyya, R. (2015). A context sensitive offloading scheme for mobile cloud computing service. In IEEE 8th international conference on cloud computing (pp. 869–876).

  21. Akcayo, M. A., & Tanriverdi, M. (2015). Context-aware decision making system for mobile cloud offloading. International Journal of Computer Networks and Communications (IJCNC), 7(6), 68–85.

    Google Scholar 

  22. Paradiso, J. A., & Starner, T. (2005). Energy scavenging for mobile and wireless electronics. IEEE Pervasive Computing, 4(1), 18–27.

    Article  Google Scholar 

  23. Mukherjee, A., & De, D. (2016). Low power offloading strategy for femto-cloud mobile network. Engineering Science Technology International Jouranl, 19, 260–270.

    Google Scholar 

  24. Xu, C., Jiao, L., Li, W., & Xiaoming, F. (2015). Efficient multi-user computation offloading for mobile-edge cloud computing. IEEE/ACM Transactions on Networking, 24(5), 2795–2808.

    Google Scholar 

  25. Xu, C. (2015). Decentralized computation offloading game for mobile cloud computing. IEEE Transactions on Parallel and Distributed Systems, 26(4), 974–983.

    Article  Google Scholar 

  26. Kemp, R., Palmer, N., Kielmann, T. & Bal, H. (2012). Cuckoo: A computation offloading framework for smartphones. In LNICS, social informatics and telecommunication engineering (Vol. 76, pp. 59–79). Springer.

  27. Roy, D. G., De, D., Mukherjee, A., & Buyya, R. (2016). Application-aware cloudlet selection for computation offloading in multi-cloudlet environment. Journal of Supercomputing. https://doi.org/10.1007/s11227-016-1872-2016.

    Article  Google Scholar 

  28. Chen, X., Chen, S., Zeng, X., Zhang, Y., Zheng, X., & Rong, C. (2017). Framework for context-aware computation offloading in mobile cloud computing. Journal of Cloud Computing: Advances, Systems and Applications, 6(1), 1–17.

    Article  Google Scholar 

  29. Saraswathi, A. T., Kalaashri, Y., & Padmavathi, S. (2015). Dynamic resource allocation scheme in cloud computing. Procedia Computer Science, 47, 30–36.

    Article  Google Scholar 

  30. Viswanathan, H., Lee, E. K., Rodero, I., & Pompili, D. (2015). Uncertainty-aware autonomic resource provisioning for mobile cloud computing. IEEE Transactions on Parallel and Distributed Systems, 26, 2363–2372.

    Article  Google Scholar 

  31. Ab Wahab, M. N., Nefti-Meziani, S., & Atyabi, A. (2015). A comprehensive review of swarm optimization algorithms. PLoS ONE, 10(5), e0122827.

    Article  Google Scholar 

  32. Pandey, V., Singh, S., & Tapaswi, S. (2015). Energy and time efficient algorithm for cloud offloading using dynamic profiling. Wireless Personal Communications, 80, 1687–1701.

    Article  Google Scholar 

  33. Ostermann, S., Iosup, A., Yigitbasi, N., Prodan, R., Fahringer, T., & Epema, D. (2009). A performance analysis of EC2 cloud computing services for scientific computing. In Proceeding on 1st international conference cloud compuing (pp. 115–131).

  34. Khan, A. R., Othman, M., Khan, A. N., Shuja, J., & Mustafa, S. (2017). Computation offloading cost estimation in mobile cloud application models. Wireless Personal Communications, 97, 4897–4920.

    Article  Google Scholar 

  35. Delange, J., Hudak, J., Nichols, W., McHale, J., & Nam, M.-Y. (2015). Evaluating and mitigating the impact of complexity in software models. Software Engineering Institute | carnegie mellon university, cmu/sei-2015-tr-013.

  36. Altamimi, M., Abdrabou, A., Naik, K., & Nayak, A. (2015). Energy cost models of smartphones for task offloading to the cloud. IEEE Transactions on Emerging Topics in Computing, 3(3), 384–398.

    Article  Google Scholar 

  37. Mavromoustakis, C. X., Andreou, A., Mastorakis, G., Bourdena, A., Batalla, J. M., & Dobre, C. (2015). On the performance evaluation of a novel offloading-based energy conservation mechanism for wireless devices. In Mobile networks and management (pp. 179–191). Springer.

  38. Atencio, L. (2012). Measuring code complexity. https://dzone.com/articles/measuring-code-complexity. Accessed 30 March 2012.

  39. Huang, J., Wu, K., Leong, L. K., Ma, S., & Moh, M. (2013). A tunable workflow scheduling algorithm based on particle swarm optimization for cloud computing. In Proceeding of international conference on soft computing and software engineering [SCSE’13].

  40. Kalra, M., & Singh, S. (2015). A review of metaheuristic scheduling techniques in cloud computing. Egyptian Informatics Journal, 16(3), 275–295.

    Article  Google Scholar 

  41. Adrian, A. M., Utamima, A., & Wang, K.-J. (2015). A comparative study of GA, PSO and ACO for solving construction site layout optimization. KSCE Journal of Civil Engineering, 19(3), 520–527.

    Article  Google Scholar 

  42. Masdari, M., Salehi, F., Jalali, M., & Bidaki, M. (2017). A survey of PSO-Based scheduling algorithms in cloud computing. Journal of Network Systems Management, 25, 122–158.

    Article  Google Scholar 

  43. Chun, B. G., Ihm, S., Maniatis, P., Naik, M., & Patti, A. (2011). CloneCloud: Elastic execution between mobile device and cloud. In Proceeding ACM EuroSys’11, Salzburg, Austria (pp. 301–314).

  44. Chun B. G., & Maniatis, P. (2009). Augmented smartphone applications through clone cloud execution. In Proceeding HotOS’09, Monte Verit`a, Switzerland (pp. 8–14).

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Acknowledgements

This work was supported by Ministry of Electronics & Information Technology (MeitY), Government of India and the authors would like to thank for sanctioning “Visvesvaraya PhD Scheme for Electronics and IT” funding scheme with reference awardee number is VISPHD-MEITY-2559. The authors would also like to thank the anonymous reviewers and the editor for their valuable comments and suggestions.

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  1. Department of Information Science and Technology, College of Engineering Guindy, Anna University, Chennai, India

    Shanthi Thangam Manukumar & Vijayalakshmi Muthuswamy

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  1. Shanthi Thangam Manukumar
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  2. Vijayalakshmi Muthuswamy
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Correspondence to Shanthi Thangam Manukumar.

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Manukumar, S.T., Muthuswamy, V. A Novel Multi-Objective Efficient Offloading Decision Framework in Cloud Computing for Mobile Computing Applications. Wireless Pers Commun 107, 1625–1642 (2019). https://doi.org/10.1007/s11277-019-06348-4

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