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An efficient system model to minimize signal interference and delay in mobile cloud environment

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Abstract

The integration of mobile computing with cloud computing has increased advantages to the users to access the applications from anywhere at any time. The traditional cellular system is overlaid by number of low power nodes which allows users to attain services at any time. But the increase of femto cells causes intra-tier, inter-tier interference and delay in the network. To overcome the bottleneck a sub-modular optimizing based offloading algorithm is proposed in this paper where the task is divided into two functions and processed. The main aim is to optimize execution time and interference. The simulation results show that the execution time has been reduced by 57.89% and performs well to obtain optimum solution compared to other existing methods.

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References

  1. Peng M, Wang C, Li J, Xiang H, Lau V (2015) Recent advances in underlay heterogeneous networks: interference control, resource allocation, and self-organization. IEEE Commun Surv Tutor 17:700–729

    Article  Google Scholar 

  2. Kovachev D (2012) Framework for computation offloading in mobile cloud computing. IJIMAI 1:6–15

    Article  Google Scholar 

  3. Nan X, He Y, Guan L (2011) Multimedia signal processing (MMSP). In: 2011 IEEE 13th international workshop, IEEE, pp 1–6

  4. Li C, Li L (2014) Phased scheduling for resource-constrained mobile devices in mobile cloud computing. Wirel Personal Commun 77:2817–2837

    Article  Google Scholar 

  5. Jararweh Y, Tawalbeh L, Ababneh F, Dosari F (2013) Resource efficient mobile computing using cloudlet infrastructure. In: 2013 IEEE ninth international conference mobile ad-hoc and sensor networks (MSN), pp 373–377

  6. Kumar N, Iqbal R, Misra S, Rodrigues JJ (2015) Bayesian coalition game for contention-aware reliable data forwarding in vehicular mobile cloud. Future Gener Comput Syst 48:60–72

    Article  Google Scholar 

  7. Verbelen T, Stevens T, De Turck F, Dhoedt B (2013) Graph partitioning algorithms for optimizing software deployment in mobile cloud computing. Future Gener Comput Syst 29:451–459

    Article  Google Scholar 

  8. Beloglazov A, Buyya R (2010) Energy efficient resource management in virtualized cloud data centers. In: Proceedings of the 2010 10th IEEE/ACM international conference on cluster, cloud and grid computing, IEEE Computer Society, pp 826–831

  9. Liang H, Xing T, Cai LX, Huang D, Peng D, Liu Y (2013) Adaptive computing resource allocation for mobile cloud computing. Int J Distrib Sens Netw 9:181426

    Article  Google Scholar 

  10. Liu W, Nishio T, Shinkuma R, Takahashi T (2014) Adaptive resource discovery in mobile cloud computing. Comput Commun 50:119–129

    Article  Google Scholar 

  11. Rahimi MR, Venkata Subramanian N, Vasilakos AV (2013) On optimal and fair service allocation in mobile cloud computing. In: 2013 IEEE sixth international conference on cloud computing, pp 75–82

  12. Niyato D, Wang P, Hossain E, Saad W, Han Z (2012) Game theoretic modeling of cooperation among service providers in mobile cloud computing environments. In: 2012 IEEE wireless communications and networking conference (WCNC), IEEE, pp 3128–3133

  13. Pompili D, Hajisami A, Tran TX (2016) Elastic resource utilization framework for high capacity and energy efficiency in cloud RAN. IEEE Commun Mag 54:26–32

    Article  Google Scholar 

  14. Ren J, Yu G, Cai Y, He Y (2018) Latency optimization for resource allocation in mobile-edge computation offloading. IEEE Trans Wirel Commun 17:5506–5519

    Article  Google Scholar 

  15. Chen M, Zhang Y, Li Y, Mao S, Leung VCM (2015) EMC: emotion-aware mobile cloud computing in 5G. IEEE Netw 29:32–38

    Article  Google Scholar 

  16. Papagianni C, Leivadeas A, Papavassiliou S, Maglaris V, Cervello-Pastor C, Monje A (2013) On the optimal allocation of virtual resources in cloud computing networks. IEEE Trans Comput 62:1060–1071

    Article  MathSciNet  Google Scholar 

  17. Praveen SP, Rao KT, Janakiramaiah B (2018) Effective allocation of resources and task scheduling in cloud environment using social group optimization. Arab J Sci Eng 43(8):4265–4272

    Article  Google Scholar 

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Correspondence to Praveena Akki.

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Akki, P., Vijayarajan, V. An efficient system model to minimize signal interference and delay in mobile cloud environment. Evol. Intel. 14, 509–517 (2021). https://doi.org/10.1007/s12065-019-00285-8

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  • DOI: https://doi.org/10.1007/s12065-019-00285-8

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