Dynamic service migration and request routing for microservice in multicell mobile-edge computing

X Chen, Y Bi, X Chen, H Zhao, N Cheng… - IEEE Internet of …, 2022 - ieeexplore.ieee.org
Mobile-edge computing (MEC) sinks computation and storage capacities to network edge,
where it is close to users to support delay-sensitive services. However, due to the dynamic …

Collective reinforcement learning based resource allocation for digital twin service in 6G networks

Z Huang, D Li, J Cai, H Lu - Journal of Network and Computer Applications, 2023 - Elsevier
Abstract The 6th generation (6G) mobile communications technology will realize the
interconnection of humans, machines, things as well as virtual space. The development of …

[HTML][HTML] Edge intelligence for service function chain deployment in NFV-enabled networks

MA Khoshkholghi, T Mahmoodi - Computer Networks, 2022 - Elsevier
With evolution of network function virtualization (NFV), network services can be provided as
service function chains (SCs), each consisting of multiple virtual network functions (VNFs) …

Multi-agent distributed reinforcement learning for making decentralized offloading decisions

J Tan, R Khalili, H Karl, A Hecker - IEEE INFOCOM 2022-IEEE …, 2022 - ieeexplore.ieee.org
We formulate computation offloading as a decentralized decision-making problem with
autonomous agents. We design an interaction mechanism that incentivizes agents to align …

Multi-agent deep reinforcement learning for coordinated multipoint in mobile networks

S Schneider, H Karl, R Khalili… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Macrodiversity is a key technique to increase the capacity of mobile networks. It can be
realized using coordinated multipoint (CoMP), simultaneously connecting users to multiple …

Constrained multi-objective optimization with deep reinforcement learning assisted operator selection

F Ming, W Gong, L Wang, Y Jin - IEEE/CAA Journal of …, 2024 - ieeexplore.ieee.org
Solving constrained multi-objective optimization problems with evolutionary algorithms has
attracted considerable attention. Various constrained multi-objective optimization …

Reinforcement Learning vs. Computational Intelligence: Comparing Service Management Approaches for the Cloud Continuum

F Poltronieri, C Stefanelli, M Tortonesi, M Zaccarini - Future Internet, 2023 - mdpi.com
Modern computing environments, thanks to the advent of enabling technologies such as
Multi-access Edge Computing (MEC), effectively represent a Cloud Continuum, a capillary …

Delay constrained sfc orchestration for edge intelligence-enabled iiot: A drl approach

Z Huang, W Zhong, D Li, H Lu - Journal of Network and Systems …, 2023 - Springer
The intelligent edge has accelerated the Internet of Things (IoT) revolution towards next-
generation operational efficiency and massive connectivity. Supporting fast response, agility …

The State of the Art of Emergent Software Systems

A Shatnawi, E Faye, B Rima, Z Al Shara… - IEEE Access, 2024 - ieeexplore.ieee.org
Emergent Software Systems (ESSs) are designed to reduce the initial effort in creating
autonomous solutions and fully adaptive support systems that can autonomously learn the …

Dynamically meeting performance objectives for multiple services on a service mesh

FS Samani, R Stadler - 2022 18th International Conference on …, 2022 - ieeexplore.ieee.org
We present a framework that lets a service provider achieve end-to-end management
objectives under varying load. Dynamic control actions are performed by a reinforcement …