Dynamic Task Offloading in MEC-Enabled IoT Networks: A Hybrid DDPG-D3QN Approach

Hu, Han; Wu, Dingguo; Zhou, Fuhui; Jin, Shi; Hu, Rose Qingyang

doi:10.1109/globecom46510.2021.9685906

Cited by 10 publications

(3 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…DRL-based approaches include value-based and policy-based approaches. Frequently used value-based DRL methods include Deep Q Learning Network (DQN) [9], Double DQN [11], Dueling DQN [12], and Double Dueling DQN (D3QN) [13]. However, when the number of wireless devices grows exponentially, DQN-based approaches are expensive.…”

Section: Related Workmentioning

confidence: 99%

“…DQN-based approaches [9][10][11][12][13] Suitable for dynamic environments. When the number of wireless devices grows exponentially, these approaches are expensive.…”

Section: Approaches Advantages Disadvantagesmentioning

confidence: 99%

“…Sci. 2023, 13,191 2 of 16 In addition, many works have only considered the offloading problem in the singleserver scenario but have not studied the multiserver MEC scenario. In practice, a densely deployed multiple MEC server can perform uninterrupted task offloading to meet the service demand [3].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

DRL-Based Dependent Task Offloading Strategies with Multi-Server Collaboration in Multi-Access Edge Computing

Peng

Chen

2022

Applied Sciences

View full text Add to dashboard Cite

Many applications in Multi-access Edge Computing (MEC) consist of interdependent tasks where the output of some tasks is the input of others. Most of the existing research on computational offloading does not consider the dependency of the task and uses convex relaxation or heuristic algorithms to solve the offloading problem, which lacks adaptability and is not suitable for computational offloading in the dynamic environment of fast fading channels. Therefore, in this paper, the optimization problem is modeled as a Markov Decision Process (MDP) in multi-user and multi-server MEC environments, and the dependent tasks are represented by Directed Acyclic Graph (DAG). Combined with the Soft Actor–Critic (SAC) algorithm in Deep Reinforcement Learning (DRL) theory, an intelligent task offloading scheme is proposed. Under the condition of resource constraint, each task can be offloaded to the corresponding MEC server through centralized control, which greatly reduces the service delay and terminal energy consumption. The experimental results show that the algorithm converges quickly and stably, and its optimization effect is better than existing methods, which verifies the effectiveness of the algorithm.

show abstract

Section: Related Workmentioning

confidence: 99%

“…DQN-based approaches [9][10][11][12][13] Suitable for dynamic environments. When the number of wireless devices grows exponentially, these approaches are expensive.…”

Section: Approaches Advantages Disadvantagesmentioning

confidence: 99%