Task Allocation in Fog-Aided Mobile IoT by Lyapunov Online Reinforcement Learning

Jingjing Yao, Nirwan Ansari

Research output: Contribution to journalArticlepeer-review

11 Scopus citations

Abstract

Fog-aided mobile IoT is proposed to speed up service response by deploying fog nodes at network edges. We investigate the task allocation in fog-aided mobile IoT networks, where mobile users generate computing tasks at different locations and offload them to fog nodes, i.e., to intelligently distribute tasks to different fog nodes in order to adapt to the varying wireless channel conditions and different fog resources. The objective is to minimize the average task completion time constrained by the mobile device's battery capacity and each task's completion deadline. In practice, future tasks are usually unknown in advance owing to the unpredictable environments and hence an online algorithm is required to make decisions on the fly. Moreover, the local task information may be incomplete and hence historical statistics should be utilized to estimate the most appropriate fog node for the current task. Therefore, we design an online reinforcement learning algorithm to address the two challenges. We also derive and analyze the computational complexity and theoretical bound. Simulation results show that our online algorithm achieves the optimal performance asymptotically, illustrate the performances of our online reinforcement learning algorithm as compared with existing works, and validate the theoretical bound analysis.

Original languageEnglish (US)
Article number8917681
Pages (from-to)556-565
Number of pages10
JournalIEEE Transactions on Green Communications and Networking
Volume4
Issue number2
DOIs
StatePublished - Jun 2020

All Science Journal Classification (ASJC) codes

  • Renewable Energy, Sustainability and the Environment
  • Computer Networks and Communications

Keywords

  • Internet of Things (IoT)
  • Lyapunov optimization
  • energy consumption
  • fog computing
  • online reinforcement learning
  • quality of service (QoS)
  • task allocation

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