Learning for Smart Edge: Cognitive Learning-Based Computation Offloading


With the development of intelligent applications, more and more intelligent applications are computation intensive, data intensive and delay sensitive. Compared with traditional cloud computing, edge computing can reduce communication delay by offloading computing tasks to edge cloud. Furthermore, with the complexity of computing scenarios in edge cloud, deep learning based on computation offloading scheme has attracted wide attention. However, all the learning-based offloading scheme does not consider the where and how to run the offloading scheme itself. Thus, in this paper, we consider the problem of running the learning-based computation offloading scheme for the first time and propose the learning for smart edge architecture. Then, we give the computation offloading optimization problem of mobile devices under multi-user and multi edge cloud scenarios. Furthermore, we propose cognitive learning-based computation offloading (CLCO) scheme for this problem. Finally, experimental results show that compared with other offloading schemes, the CLCO scheme has lower task duration and energy consumption.

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Corresponding author

Correspondence to M. Shamim Hossain.

Additional information

The authors would like to extend their sincere appreciation to the Deanship of Scientific Research at King Saud University, Riyadh, Saudi Arabia, for funding this research group No. (RG-1437-042).

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Hao, Y., Jiang, Y., Hossain, M.S. et al. Learning for Smart Edge: Cognitive Learning-Based Computation Offloading. Mobile Netw Appl 25, 1016–1022 (2020). https://doi.org/10.1007/s11036-018-1119-7

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  • Computation offloading
  • Cognitive learning
  • Edge computing
  • Communication