A Novel Deep Federated Learning-Based and Profit-Driven Service Caching Method
Z Ouyang, Y Xia, Q Peng, Y Li, P Chen… - International Conference …, 2023 - Springer
Abstract Service caching is an emerging solution to addressing massive service request in a
distributed environment for supporting rapidly growing services and applications. With the …
distributed environment for supporting rapidly growing services and applications. With the …
Deep reinforcement learning-based task offloading and service migrating policies in service caching-assisted mobile edge computing
K Hongchang, W Hui, S Hongbin… - China …, 2024 - ieeexplore.ieee.org
Emerging mobile edge computing (MEC) is considered a feasible solution for offloading the
computation-intensive request tasks generated from mobile wireless equipment (MWE) with …
computation-intensive request tasks generated from mobile wireless equipment (MWE) with …
Hierarchical Deep Reinforcement Learning for Joint Service Caching and Computation Offloading in Mobile Edge-Cloud Computing
Mobile edge-cloud computing networks can provide distributed, hierarchical, and fine-
grained resources, and have become a major goal for future high-performance computing …
grained resources, and have become a major goal for future high-performance computing …
Federated Learning-Based Service Caching in Multi-Access Edge Computing System
Multi-access edge computing (MEC) brings computations closer to mobile users, thereby
decreasing service latency and providing location-aware services. Nevertheless, given the …
decreasing service latency and providing location-aware services. Nevertheless, given the …
Reinforcement learning for cost-effective IoT service caching at the edge
In the edge computing environment, Internet of Things (IoT) application service providers
can rent resources from edge servers to cache their service items such as datasets and code …
can rent resources from edge servers to cache their service items such as datasets and code …
Dima: Distributed cooperative microservice caching for internet of things in edge computing by deep reinforcement learning
Abstract The ubiquitous Internet of Things (IoTs) devices spawn growing mobile services of
applications with computationally-intensive and latency-sensitive features, which increases …
applications with computationally-intensive and latency-sensitive features, which increases …
Content-Aware Caching at the Mobile Edge Network Using Federated Learning
The explosive growth of mobile data traffic generated primarily from resource-hungry video
streaming sessions challenges the service providers to deliver a better Quality of Service to …
streaming sessions challenges the service providers to deliver a better Quality of Service to …
A multi-armed bandits learning-based approach to service caching in edge computing environment
J Li, J Zhao, P Chen, Y Xia, F Li, Y Li, F Zeng… - … Conference on Web …, 2023 - Springer
Mobile edge computing (MEC) is a newly emerging concept that provides significant local
computing power and reduces end-to-end latency. In MEC environments, caching frequently …
computing power and reduces end-to-end latency. In MEC environments, caching frequently …
Service caching decision‐making policy for mobile edge computing using deep reinforcement learning
H Ke, H Wang, K Yang, H Sun - IET Communications, 2023 - Wiley Online Library
Mobile user terminals in 5G networks can generate massive computational workloads,
which require sufficient computation and caching resources, and the processors of user …
which require sufficient computation and caching resources, and the processors of user …
Mobility-aware service caching in mobile edge computing for internet of things
H Wei, H Luo, Y Sun - Sensors, 2020 - mdpi.com
The mobile edge computing architecture successfully solves the problem of high latency in
cloud computing. However, current research focuses on computation offloading and lacks …
cloud computing. However, current research focuses on computation offloading and lacks …