Energy Efficiency Analysis of e-Commerce Customer Management System Based on Mobile Edge Computing

Chen, Wenxing; Yang, Bin

doi:10.1155/2022/5333346

Cited by 1 publication

(3 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In order to address such problems, a new computing paradigm named edge computing is introduced [22]. In edge computing, the data are preprocessed by intelligent devices, and the edge servers nearby users undertake the majority of tasks for computing data and executing the services, while the remote cloud servers are only responsible for the training of deep neural network models [23,24]. Due to the resource-constrained of end devices, the services should be offloaded on edge servers or cloud servers [25].…”

Section: Related Workmentioning

confidence: 99%

“…end if (19) end for (20) if num � k then (21) break (22) end if (23) end while (24) Compared with previous studies, our approach offloads the interacting services to optimize the service requesting delay and data communication delay. In our previous works [40,41], a parallel frequent pattern-based algorithm is presented to discover the interacting services.…”

Section: Offloading Location Decisionmentioning

confidence: 99%

“…Update θ μ by the sampled policy gradient (20) Soft update the critic target network and actor target network (21) end if (22) end for (23) end for (24) Get μ(s′|θ μ ) (25) for episode � 1, 2, . .…”

Section: Experiments Setupmentioning

confidence: 99%

See 2 more Smart Citations

CSO-DRL: A Collaborative Service Offloading Approach with Deep Reinforcement Learning in Vehicular Edge Computing

Huang

Cao

Zhang

et al. 2022

Scientific Programming

View full text Add to dashboard Cite

In vehicular edge computing, vehicles move along the road and request the services from the nearest edge servers with low latency. Due to the limitation of computation capacity of vehicular devices, the services should be offloaded on RSUs equipped with edge servers to provide service with low latency. Noticed that the location of service offloading may affect the service requesting delay directly, and it may exist some interrelationship between interacting services; all of these are rarely considered in recent studies. To address such problems, we propose a collaborative service offloading approach with deep reinforcement learning in vehicular edge computing named CSO-DRL. Our approach first divides the road segments by k-means-based algorithm through analyzing the trajectory data of vehicles, and then the offloading location is determined by observing the vehicle running status. Secondly, the interacting services are discovered by a parallel frequent pattern-based algorithm efficiently. Furthermore, the collaborative service offloading algorithm is presented by the DDPG model for offloading the interacting services, which can minimize the service requesting delay and data communication delay between interacting services. Finally, the efficiency of the algorithm is evaluated by real-world data-based simulation experimental evaluations. The results show our algorithm can obtain a lower delay than other baseline algorithms in searching for the optimal service offloading strategy.

show abstract