With the development of the wireless network, increasing mobile applications are emerging and receiving great popularity. These applications cover a wide area, such as traffic monitoring, smart homes, real-time vision processing, objective tracking, and so on, and typically require computation-intensive resources to achieve a high quality of experience. Although the performance of mobile devices (MDs) has been continuously enhanced, running all the applications on a single MD still causes high energy consumption and latency. Fortunately, mobile edge computing (MEC) allows MDs to offload their computation-intensive tasks to proximal eNodeBs (eNBs) to augment computational capabilities. However, the current task offloading schemes mainly concentrate on average-based performance metrics, failing to meet the deadline constraint of the tasks. Based on the deep reinforcement learning (DRL) approach, this paper proposes an Energy-aware Task Offloading with Deadline constraint (DRL-E2D) algorithm for a multi-eNB MEC environment, which is to maximize the reward under the deadline constraint of the tasks. In terms of the actor-critic framework, we integrate the action representation into DRL-E2D to handle the large discrete action space problem, i.e., using the low-complexity k-nearest neighbor as an approximate approach to extract optimal discrete actions from the continuous action space. The extensive experimental results show that DRL-E2D achieves better performance than the comparison algorithms on all parameter settings, indicating that DRL-E2D is robust to the state changes in the MEC environment.
|Journal||Eurasip Journal on Wireless Communications and Networking|
|Publication status||Published - 17 Mar 2021|
Bibliographical noteFunding Information:
This work was supported by the National Natural Science Foundation of China (Nos. 61802095, 61572162), the Zhejiang Provincial Key Science and Technology Project Foundation (No. 2018C01012), the Zhejiang Provincial National Science Foundation of China (No. LQ19F020011), the Open Foundation of State Key Laboratory of Networking and Switching Technology (Beijing University of Posts and Telecommunications) (No. SKLNST-2019-2-15), and the VC Research (No. VCR 0000057).
© 2021, The Author(s).
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