Energy-Aware Multi-Server Mobile Edge Computing: A Deep Reinforcement Learning Approach

@article{Naderializadeh2019EnergyAwareMM,
  title={Energy-Aware Multi-Server Mobile Edge Computing: A Deep Reinforcement Learning Approach},
  author={Navid Naderializadeh and Morteza Hashemi},
  journal={2019 53rd Asilomar Conference on Signals, Systems, and Computers},
  year={2019},
  pages={383-387}
}
  • Navid Naderializadeh, Morteza Hashemi
  • Published in
    53rd Asilomar Conference on…
    2019
  • Mathematics, Computer Science
  • We investigate the problem of computation offloading in a mobile edge computing architecture, where multiple energy-constrained users compete to offload their computational tasks to multiple servers through a shared wireless medium. We propose a multi-agent deep reinforcement learning algorithm, where each server is equipped with an agent, observing the status of its associated users and selecting the best user for offloading at each step. We consider computation time (i.e., task completion… CONTINUE READING

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