Deep reinforcement learning-based microservice selection in mobile edge computing
In mobile edge computing environment, due to resources constraints of edge devices, when
user locations continue changing, the network will be delayed or interrupted, which affects …
user locations continue changing, the network will be delayed or interrupted, which affects …
Delay-aware microservice coordination in mobile edge computing: A reinforcement learning approach
As an emerging service architecture, microservice enables decomposition of a monolithic
web service into a set of independent lightweight services which can be executed …
web service into a set of independent lightweight services which can be executed …
Task offloading in multiple-services mobile edge computing: A deep reinforcement learning algorithm
Z Peng, G Wang, W Nong, Y Qiu, S Huang - Computer Communications, 2023 - Elsevier
Abstract Multiple-Services Mobile Edge Computing enables task-relate services cached in
edge server to be dynamically updated, and thus provides great opportunities to offload …
edge server to be dynamically updated, and thus provides great opportunities to offload …
A dynamic service placement based on deep reinforcement learning in mobile edge computing
Mobile edge computing is an emerging paradigm that supplies computation, storage, and
networking resources between end devices and traditional cloud data centers. With …
networking resources between end devices and traditional cloud data centers. With …
Microservice deployment in edge computing based on deep Q learning
W Lv, Q Wang, P Yang, Y Ding, B Yi… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
The microservice deployment strategy is promising in reducing the overall service response
time in the microservice-oriented edge computing platform. However, existing works ignore …
time in the microservice-oriented edge computing platform. However, existing works ignore …
Deep reinforcement learning based approach for online service placement and computation resource allocation in edge computing
Due to the urgent emergence of computation-intensive intelligent applications on end
devices, edge computing has been put forward as an extension of cloud computing, to …
devices, edge computing has been put forward as an extension of cloud computing, to …
Qos-aware mobile service optimization in multi-access mobile edge computing environments
C Li, K Jiang, Y Luo - Pervasive and Mobile Computing, 2022 - Elsevier
With the rapid development of mobile Internet technologies and various new service
services such as virtual reality (VR) and augmented reality (AR), users' demand for network …
services such as virtual reality (VR) and augmented reality (AR), users' demand for network …
Service migration for mobile edge computing based on partially observable Markov decision processes
W Chen, Y Chen, J Liu - Computers and Electrical Engineering, 2023 - Elsevier
With the continuous development of mobile edge computing, people are more willing to
offload tasks to edge servers that are closer to users than cloud services for a better user …
offload tasks to edge servers that are closer to users than cloud services for a better user …
Delay-Aware Optimization of Fine-Grained Microservice Deployment and Routing in Edge via Reinforcement Learning
K Peng, J He, J Guo, Y Liu, J He… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Microservices have exerted a profound impact on the development of internet applications.
Meanwhile, the growing number of mobile terminal user requests has made the …
Meanwhile, the growing number of mobile terminal user requests has made the …
Joint optimization of delay and cost for microservice composition in mobile edge computing
F Guo, B Tang, M Tang - World Wide Web, 2022 - Springer
With the development of software technology, some complex mobile and Internet-of-Things
(IoT) applications can be constituted by a set of microservices. At present, mobile edge …
(IoT) applications can be constituted by a set of microservices. At present, mobile edge …