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Toward Energy-Efficient Task Offloading Schemes in Fog Computing: A Survey

  • Alasmari, Moteb K. (King Saud University, College of Computer and Information Sciences) ;
  • Alwakeel, Sami S. (King Saud University, College of Computer and Information Sciences) ;
  • Alohali, Yousef (King Saud University, College of Computer and Information Sciences)
  • Received : 2022.03.05
  • Published : 2022.03.30

Abstract

The interconnection of an enormous number of devices into the Internet at a massive scale is a consequence of the Internet of Things (IoT). As a result, tasks offloading from these IoT devices to remote cloud data centers become expensive and inefficient as their number and amount of its emitted data increase exponentially. It is also a challenge to optimize IoT device energy consumption while meeting its application time deadline and data delivery constraints. Consequently, Fog Computing was proposed to support efficient IoT tasks processing as it has a feature of lower service delay, being adjacent to IoT nodes. However, cloud task offloading is still performed frequently as Fog computing has less resources compared to remote cloud. Thus, optimized schemes are required to correctly characterize and distribute IoT devices tasks offloading in a hybrid IoT, Fog, and cloud paradigm. In this paper, we present a detailed survey and classification of of recently published research articles that address the energy efficiency of task offloading schemes in IoT-Fog-Cloud paradigm. Moreover, we also developed a taxonomy for the classification of these schemes and provided a comparative study of different schemes: by identifying achieved advantage and disadvantage of each scheme, as well its related drawbacks and limitations. Moreover, we also state open research issues in the development of energy efficient, scalable, optimized task offloading schemes for Fog computing.

Keywords

References

  1. A. Wasicek, "The future of 5G smart home network security is micro-segmentation," Netw. Secur., vol. 2020, no. 11, pp. 11-13, 2020, doi: 10.1016/S1353-4858(20)30129-X.
  2. M. Mukherjee, L. Shu, and D. Wang, "Survey of fog computing: Fundamental, network applications, and research challenges," IEEE Commun. Surv. Tutorials, vol. 20, no. 3, pp. 1826-1857, 2018, doi: 10.1109/COMST.2018.2814571.
  3. P. Hu, S. Dhelim, H. Ning, and T. Qiu, "Survey on fog computing: architecture, key technologies, applications and open issues," J. Netw. Comput. Appl., vol. 98, no. September, pp. 27-42, 2017, doi: 10.1016/j.jnca.2017.09.002.
  4. C. Puliafito, E. Mingozzi, F. Longo, A. Puliafito, and O. Rana, "Fog computing for the Internet of Things: A survey," ACM Trans. Internet Technol., vol. 19, no. 2, 2019, doi: 10.1145/3301443.
  5. R. K. Naha, S. Garg, and A. Chan, "Fog-computing architecture: survey and challenges," Big Data-Enabled Internet Things, pp. 199-223, 2019, doi: 10.1049/pbpc025e_ch10.
  6. R. K. Naha et al., "Fog computing: Survey of trends, architectures, requirements, and research directions," IEEE Access, vol. 6, pp. 47980-48009, 2018, doi: 10.1109/ACCESS.2018.2866491.
  7. A. A. Alli and M. M. Alam, "The fog cloud of things: A survey on concepts, architecture, standards, tools, and applications," Internet of Things, vol. 9, p. 100177, 2020, doi: 10.1016/j.iot.2020.100177.
  8. H. Sun, H. Yu, G. Fan, and L. Chen, "Energy and time efficient task offloading and resource allocation on the generic IoT-fog-cloud architecture," Peer-to-Peer Netw. Appl., vol. 13, no. 2, pp. 548-563, 2020, doi: 10.1007/s12083-019-00783-7.
  9. Q. Zhu, B. Si, F. Yang, and Y. Ma, "Task offloading decision in fog computing system," China Commun., vol. 14, no. 11, pp. 59-68, 2017, doi: 10.1109/CC.2017.8233651.
  10. J. Kim, T. Ha, W. Yoo, and J. M. Chung, "Task Popularity-Based Energy Minimized Computation Offloading for Fog Computing Wireless Networks," IEEE Wirel. Commun. Lett., vol. 8, no. 4, pp. 1200-1203, 2019, doi: 10.1109/LWC.2019.2911521.
  11. D. Wang, Z. Liu, X. Wang, and Y. Lan, "Mobility-Aware Task Offloading and Migration Schemes in Fog Computing Networks," IEEE Access, vol. 7, pp. 43356-43368, 2019, doi: 10.1109/ACCESS.2019.2908263.
  12. H. Mahini, A. M. Rahmani, and S. M. Mousavirad, "An evolutionary game approach to IoT task offloading in fog-cloud computing," J. Supercomput., vol. 77, no. 6, pp. 5398-5425, 2021, doi: 10.1007/s11227-020-03484-8.
  13. M. Aazam, S. U. Islam, S. T. Lone, and A. Abbas, "Cloud of Things (CoT): Cloud-Fog-IoT Task Offloading for Sustainable Internet of Things," IEEE Trans. Sustain. Comput., vol. 3782, no. c, pp. 1-13, 2020, doi: 10.1109/TSUSC.2020.3028615.
  14. S. Abdullah and A. Jabir, "A Light Weight Multi-Objective Task Offloading Optimization for Vehicular Fog Computing," Iraqi J. Electr. Electron. Eng., vol. 17, no. 1, pp. 1-10, 2021, doi: 10.37917/ijeee.17.1.8.
  15. O. K. Shahryari, H. Pedram, V. Khajehvand, and M. D. TakhtFooladi, "Energy and task completion time trade-off for task offloading in fog-enabled IoT networks," Pervasive Mob. Comput., vol. 74, p. 101395, 2021, doi: 10.1016/j.pmcj.2021.101395.
  16. S. Misra and N. Saha, "Detour: Dynamic Task Offloading in Software-Defined Fog for IoT Applications," IEEE J. Sel. Areas Commun., vol. 37, no. 5, pp. 1159-1166, 2019, doi: 10.1109/JSAC.2019.2906793.
  17. X. Huang, X. Yang, Q. Chen, and J. Zhang, "Task Offloading Optimization for UAV-assisted Fog-enabled Internet of Things Networks," IEEE Internet Things J., vol. 4662, no. c, 2021, doi: 10.1109/JIOT.2021.3078904.
  18. K. Wang, Y. Zhou, J. Li, L. Shi, W. Chen, and L. Hanzo, "Energy-Efficient Task Offloading in Massive MIMO-Aided Multi-Pair Fog-Computing Networks," IEEE Trans. Commun., vol. 69, no. 4, pp. 2123-2137, 2021, doi: 10.1109/TCOMM.2020.3046265.
  19. P. Cai, F. Yang, J. Wang, X. Wu, Y. Yang, and X. Luo, "JOTE: Joint Offloading of Tasks and Energy in Fog-Enabled IoT Networks," IEEE Internet Things J., vol. 7, no. 4, pp. 3067-3082, 2020, doi: 10.1109/JIOT.2020.2964951.
  20. L. Pu, X. Chen, J. Xu, and X. Fu, "D2D Fogging: An Energy-Efficient and Incentive-Aware Task Offloading Framework via Network-Assisted D2D Collaboration," IEEE J. Sel. Areas Commun., vol. 34, no. 12, pp. 3887-39014, 2016, doi: 10.1109/JSAC.2016.2624118.
  21. Y. Yao et al., "KFTO: Kuhn-Munkres based fair task offloading in fog networks," Comput. Networks, vol. 195, p. 108131, 2021, doi: 10.1016/j.comnet.2021.108131.
  22. X. Huang, Y. Cui, Q. Chen, and J. Zhang, "Joint Task Offloading and QoS-Aware Resource Allocation in Fog-Enabled Internet-of-Things Networks," IEEE Internet Things J., vol. 7, no. 8, pp. 7194-7206, 2020, doi: 10.1109/JIOT.2020.2982670.
  23. C. Swain et al., "METO: Matching Theory Based Efficient Task Offloading in IoT-Fog Interconnection Networks," IEEE Internet Things J., vol. 4662, no. c, pp. 1-1, 2020, doi: 10.1109/jiot.2020.3025631.
  24. F. Chiti, R. Fantacci, and B. Picano, "A matching game for tasks offloading in integrated edge-fog computing systems," Trans. Emerg. Telecommun. Technol., vol. 31, no. 2, pp. 1-14, 2020, doi: 10.1002/ett.3718.
  25. M. Keshavarznejad, M. H. Rezvani, and S. Adabi, "Delay-aware optimization of energy consumption for task offloading in fog environments using metaheuristic algorithms," Cluster Comput., vol. 0123456789, 2021, doi: 10.1007/s10586-020-03230-y.
  26. X. Li, Z. Zang, F. Shen, and Y. Sun, "Task Offloading Scheme Based on Improved Contract Net Protocol and Beetle Antennae Search Algorithm in Fog Computing Networks," Mob. Networks Appl., vol. 25, no. 6, pp. 2517-2526, 2020, doi: 10.1007/s11036-020-01593-5.
  27. G. Zhang, F. Shen, Z. Liu, Y. Yang, K. Wang, and M. T. Zhou, "FEMTO: Fair and energy-minimized task offloading for fog-enabled IoT networks," IEEE Internet Things J., vol. 6, no. 3, pp. 4388-4400, 2019, doi: 10.1109/JIOT.2018.2887229.
  28. D. Rahbari and M. Nickray, "Task offloading in mobile fog computing by classification and regression tree," Peer-to-Peer Netw. Appl., vol. 13, no. 1, pp. 104-122, 2020, doi: 10.1007/s12083-019-00721-7.
  29. Z. Zhu, T. Liu, Y. Yang, and X. Luo, "BLOT: Bandit Learning-Based Offloading of Tasks in Fog-Enabled Networks," IEEE Trans. Parallel Distrib. Syst., vol. 30, no. 12, pp. 2636-2649, 2019, doi: 10.1109/TPDS.2019.2927978.
  30. R. I. Ciobanu, C. Dobre, M. Balanescu, and G. Suciu, "Data and task offloading in collaborative mobile fog-based networks," IEEE Access, vol. 7, pp. 104405-104422, 2019, doi: 10.1109/ACCESS.2019.2929683.
  31. S. Iqbal, A. W. Malik, A. U. Rahman, and R. M. Noor, "Blockchain-based reputation management for task offloading in micro-level vehicular fog network," IEEE Access, vol. 8, pp. 52968-52980, 2020, doi: 10.1109/ACCESS.2020.2979248.
  32. P. Wang, R. Yu, N. W. Gao, C. Lin, and Y. Liu, "Task-driven Data Offloading for Fog-enabled Urban IoT Services," IEEE Internet Things J., vol. XX, no. XX, pp. 1-13, 2020, doi: 10.1109/JIOT.2020.3039467.
  33. Y. L. Jiang, Y. S. Chen, S. W. Yang, and C. H. Wu, "Energy-Efficient Task Offloading for Time-Sensitive Applications in Fog Computing," IEEE Syst. J., vol. 13, no. 3, pp. 2930-2941, 2019, doi : 10.1109/JSYST.2018.2877850.