Advertisement

Task offloading in mobile fog computing by classification and regression tree

  • Dadmehr Rahbari
  • Mohsen NickrayEmail author
Article
First Online:
  • 26 Downloads

Abstract

Fog computing (FC) as an extension of cloud computing provides a lot of smart devices at the network edge, which can store and process data near end users. Because FC reduces latency and power consumption, it is suitable for the Internet of Things (IoT) applications as healthcare, vehicles, and smart cities. In FC, the mobile devices (MDs) can offload their heavy tasks to fog devices (FDs). The selection of best FD for offloading has serious challenges in the time and energy. In this paper, we present a Module Placement method by Classification and regression tree Algorithm (MPCA). We select the best FDs for modules by MPCA. Initially, the power consumption of MDs are checked, if this value is greater than Wi-Fi’s power consumption, then offloading will be done. The MPCA’s decision parameters for selecting the best FD include authentication, confidentiality, integrity, availability, capacity, speed, and cost. To optimize MPCA, we analyze and apply the probability of network’s resource utilization in the module offloading. This method is called by (MPMCP). To evaluate our proposed approach, we simulate MPCA and MPMCP algorithms and compare them with First Fit (FF) and local mobile processing methods in Cloud, FDs, and MDs. The results include the power consumption, response time and performance show that the proposed methods are superior to other compared methods.

Keywords

Mobile fog computing Module placement Task offloading Classification and regression tree Markov chain process 
This is a preview of subscription content, log in to check access.

Notes

References

  1. 1.
    Chen N, Chen Y (2018) Smart city surveillance at the network edge in the era of IoT: opportunities and challenges. In: Smart cities. Springer, pp 153–176Google Scholar
  2. 2.
    Hosseinian-Far A, Ramachandran M, Slack CL (2018) Emerging trends in cloud computing, big data, fog computing, IoT and smart living. In: Technology for smart futures. Springer, pp 29–40Google Scholar
  3. 3.
    Mahmud R, Kotagiri R, Buyya R (2018) Fog computing: a taxonomy, survey and future directions. In: Internet of everything. Springer, pp 103–130Google Scholar
  4. 4.
    Wang D, Ding W, Ma X, Jiang H, Wang F, Liu J (2018) MiFo: a novel edge network integration framework for fog computing. In: Peer-to-peer networking and applications, Springer, pp 1–11Google Scholar
  5. 5.
    Fernando N, Loke SW, Rahayu W (2013) Mobile cloud computing: a survey. Fut Gen Comput Syst 29 (1):84–106CrossRefGoogle Scholar
  6. 6.
    Gusev M, Dustdar S (2018) Going back to the roots the evolution of edge computing, an IoT perspective. IEEE Internet Comput 22(2):5–15CrossRefGoogle Scholar
  7. 7.
    Mach P, Becvar Z (2017) Mobile edge computing: a survey on architecture and computation offloading. IEEE Commun Surv Tutor 19(3):1628–1656CrossRefGoogle Scholar
  8. 8.
    Li C, Xue Y, Wang J, Zhang W, Li T (2018) Edge-oriented computing paradigms: a survey on architecture design and system management. ACM Comput Surv (CSUR) 51(2):39CrossRefGoogle Scholar
  9. 9.
    Liu L, Chang Z, Guo X, Mao S, Ristaniemi T (2018) Multiobjective optimization for computation offloading in fog computing. IEEE Internet Things J 5(1):283–294CrossRefGoogle Scholar
  10. 10.
    Roman R, Lopez J, Mambo M (2018) Mobile edge computing, fog others: a survey and analysis of threats and challenges. Futur Gener Comput Syst 78:680–698CrossRefGoogle Scholar
  11. 11.
    Mitchell T (1997) Machine learning. McGraw-Hill International Editions - Computer Science Series, McGraw-Hill EducationGoogle Scholar
  12. 12.
    Govindan K, Balasundaram R, Baskar N, Asokan P (2017) A hybrid approach for minimizing makespan in permutation flowshop scheduling. J Syst Sci Syst Eng 26(1):50–76CrossRefGoogle Scholar
  13. 13.
    Bishop C (2006) Pattern recognition and machine learning. Information science and statistics. SpringerGoogle Scholar
  14. 14.
    Kowsigan M, Balasubramanie P (2018) An efficient performance evaluation model for the resource clusters in cloud environment using continuous time Markov chain and poisson process. Clust Comput, 1–9Google Scholar
  15. 15.
    Boucherie RJ, Van Dijk NM (2017) Markov decision processes in practice. SpringerGoogle Scholar
  16. 16.
    Davis MH (2018) Markov models & optimization. RoutledgeGoogle Scholar
  17. 17.
    Tang C, Wei X, Xiao S, Chen W, Fang W, Zhang W, Hao M (2018) A mobile cloud based scheduling strategy for industrial internet of things. IEEE Access 6:7262–7275CrossRefGoogle Scholar
  18. 18.
    Shah-Mansouri H, Wong VW, Schober R (2017) Joint optimal pricing and task scheduling in mobile cloud computing systems. IEEE Trans Wirel Commun 16(8):5218–5232CrossRefGoogle Scholar
  19. 19.
    Zhang J, Zhou Z, Li S, Gan L, Zhang X, Qi L, Xu X, Dou W (2018) Hybrid computation offloading for smart home automation in mobile cloud computing. Pers Ubiquit Comput 22(1):121–134CrossRefGoogle Scholar
  20. 20.
    Wang T, Wei X, Tang C, Fan J (2018) Efficient multi-tasks scheduling algorithm in mobile cloud computing with time constraints. Peer-to-Peer Network Appl 11(4):793–807CrossRefGoogle Scholar
  21. 21.
    Geng Y, Yang Y, Cao G (2018) Energy-efficient computation offloading for multicore-based mobile devices.In: IEEE INFOCOM, pp 1–9Google Scholar
  22. 22.
    Sundar S, Liang B (2018) Offloading dependent tasks with communication delay and deadline constraint. IEEE INFOCOM 2018. Honolulu, pp 37–45Google Scholar
  23. 23.
    Wang Z, Zhao Z, Min G, Huang X, Ni Q, Wang R (2018) User mobility aware task assignment for mobile edge computing. Futur Gener Comput Syst 85:1–8CrossRefGoogle Scholar
  24. 24.
    Zhang J, Xia W, Yan F, Shen L (2018) Joint computation offloading and resource allocation optimization in heterogeneous networks with mobile edge computing. IEEE Access 6:19324–19337CrossRefGoogle Scholar
  25. 25.
    Chen W, Wang D, Li K (2018) Multi-user multi-task computation offloading in green mobile edge cloud computing. IEEE Transactions on Services ComputingGoogle Scholar
  26. 26.
    Yu F, Chen H, Xu J (2018) Dmpo: dynamic mobility-aware partial offloading in mobile edge computing. Futur Gener Comput Syst 89:722–735CrossRefGoogle Scholar
  27. 27.
    Huang H, Guo S (2017) Service provisioning update scheme for mobile application users in a cloudlet network. In: 2017 IEEE International conference on communications (ICC). Paris, pp 1–6Google Scholar
  28. 28.
    Huang H, Guo S (2017) Adaptive service provisioning for mobile edge cloud. ZTE Commun 15(2):1–9Google Scholar
  29. 29.
    Xu J, Chen L, Zhou P (2018) Joint service caching and task offloading for mobile edge computing in dense networks. arXiv:1801.05868
  30. 30.
    Elazhary H, Sabbeh S (2018) The w5 framework for computation offloading in the internet of things. IEEE Access 6:23883–23895CrossRefGoogle Scholar
  31. 31.
    Wu S, Mei C, Jin H, Wang D (2018) Android unikernel: gearing mobile code offloading towards edge computing. Futur Gener Comput Syst 86:694–703CrossRefGoogle Scholar
  32. 32.
    Liu L, Chang Z, Guo X (2018) Socially-aware dynamic computation offloading scheme for fog computing system with energy harvesting devices. IEEE Internet Things J 5(3):1869–1879CrossRefGoogle Scholar
  33. 33.
    Tang Z, Zhou X, Zhang F, Jia W, Zhao W (2018) Migration modeling and learning algorithms for containers in fog computing. IEEE Transactions on Services ComputingGoogle Scholar
  34. 34.
    Mohan N, Kangasharju J (2018) Placing it right!: optimizing energy, processing, and transport in edge-fog clouds. Ann Telecommun 73(7–8):463–474CrossRefGoogle Scholar
  35. 35.
    Lyu X, Tian H, Jiang L, Vinel A, Maharjan S, Gjessing S, Zhang Y (2018) Selective offloading in mobile edge computing for the green internet of things. IEEE Netw 32(1):54–60CrossRefGoogle Scholar
  36. 36.
    Du J, Zhao L, Feng J, Chu X (2017) Computation offloading and resource allocation in mixed fog/cloud computing systems with min-max fairness guarantee. IEEE Trans Commun 66(4):1594–1608CrossRefGoogle Scholar
  37. 37.
    Shuja J, Gani A, Ko K, So K, Mustafa S, Madani SA, Khan MK (2018) Simdom: a framework for SIMD instruction translation and offloading in heterogeneous mobile architectures. Trans Emerg Telecommun Technol 29(4):e3174CrossRefGoogle Scholar
  38. 38.
    Cui H, Li Y, Liu X, Ansari N, Liu Y (2017) Cloud service reliability modelling and optimal task scheduling. IET Commun 11(2):161–167CrossRefGoogle Scholar
  39. 39.
    Wang X, Xu W, Jin Z (2017) A hidden Markov model based dynamic scheduling approach for mobile cloud telemonitoring. In: 2017 IEEE EMBS international conference on biomedical & health informatics (BHI). IEEE, Orlando, pp 273–276Google Scholar
  40. 40.
    Alasmari KR, Green RC, Alam M (2018) Mobile edge offloading using Markov decision processes. In: International conference on edge computing. Springer, pp 80–90Google Scholar
  41. 41.
    He X, Liu J, Jin R, Dai H (2017) Privacy-aware offloading in mobile-edge computing. In: GLOBECOM 2017-2017 IEEE global communications conference. IEEE, pp 1–6Google Scholar
  42. 42.
    Liu J, Mao Y, Zhang J, Letaief KB (2016) Delay-optimal computation task scheduling for mobile-edge computing systems. In: 2016 IEEE International symposium on information theory (ISIT). IEEE, Barcelona, pp 1451–1455Google Scholar
  43. 43.
    Xu J, Chen L, Ren S (2017) Online learning for offloading and autoscaling in energy harvesting mobile edge computing. IEEE Trans Cogn Commun Network 3(3):361–373CrossRefGoogle Scholar
  44. 44.
    Ali FA, Simoens P, Verbelen T, Demeester P, Dhoedt B (2016) Mobile device power models for energy efficient dynamic offloading at runtime. J Syst Softw 113:173–187CrossRefGoogle Scholar
  45. 45.
    Hayajneh T, Doomun R, Al-Mashaqbeh G, Mohd BJ (2014) An energy-efficient and security aware route selection protocol for wireless sensor networks. Secur Commun Netw 7(11):2015–2038CrossRefGoogle Scholar
  46. 46.
    Li Z, Ge J, Yang H, Huang L, Hu H, Hu H, Luo B (2016) A security and cost aware scheduling algorithm for heterogeneous tasks of scientific workflow in clouds. Futur Gener Comput Syst 65:140–152CrossRefGoogle Scholar
  47. 47.
    Xie T, Qin X (2006) Scheduling security-critical real-time applications on clusters. IEEE Trans Comput 55(7):864–879CrossRefGoogle Scholar
  48. 48.
    Calheiros RN, Ranjan R, Beloglazov A, De Rose CA, Buyya R (2011) Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Softw Practice Exper 41(1):23–50CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Computer Engineering and Information TechnologyUniversity of QomQomIran

Personalised recommendations

Cite article

Buy options