Dynamically meeting performance objectives for multiple services on a service mesh
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 …
objectives under varying load. Dynamic control actions are performed by a reinforcement …
Accelerated resource allocation based on experience retention for B5G networks
ÁG Andrade, A Anzaldo - Journal of Network and Computer Applications, 2023 - Elsevier
Abstract The Beyond-Fifth-Generation (B5G) Wireless Communication Systems will require
efficient resource allocation (RA) policies to fulfill future applications' increasing data rate …
efficient resource allocation (RA) policies to fulfill future applications' increasing data rate …
A multi-strategy multi-objective hierarchical approach for energy management in 5G networks
SKG Peesapati, M Olsson… - GLOBECOM 2022-2022 …, 2022 - ieeexplore.ieee.org
Network optimization for improving energy efficiency is a complex process given the
dynamic nature, numerous performance indicators, and the plethora of dependencies in a …
dynamic nature, numerous performance indicators, and the plethora of dependencies in a …
Qcell: Self-optimization of softwarized 5g networks through deep q-learning
With the unprecedented rise in traffic demand and mobile subscribers, real-time fine-grained
optimization frame-works are crucial for the future of cellular networks. Indeed, rigid and …
optimization frame-works are crucial for the future of cellular networks. Indeed, rigid and …
User allocation in mobile edge computing: A deep reinforcement learning approach
SP Panda, A Banerjee… - 2021 IEEE International …, 2021 - ieeexplore.ieee.org
In recent times, the need for low latency has made it necessary to deploy application
services physically and logically close to the users rather than using the cloud for hosting …
services physically and logically close to the users rather than using the cloud for hosting …
Resource management in wireless networks via multi-agent deep reinforcement learning
We propose a mechanism for distributed resource management and interference mitigation
in wireless networks using multi-agent deep reinforcement learning (RL). We equip each …
in wireless networks using multi-agent deep reinforcement learning (RL). We equip each …
Quality of service optimization in mobile edge computing networks via deep reinforcement learning
Mobile edge computing (MEC) is an emerging paradigm that integrates computing
resources in wireless access networks to process computational tasks in close proximity to …
resources in wireless access networks to process computational tasks in close proximity to …
Resource allocation in information-centric wireless networking with D2D-enabled MEC: A deep reinforcement learning approach
Recently, information-centric wireless networks (ICWNs) have become a promising Internet
architecture of the next generation, which allows network nodes to have computing and …
architecture of the next generation, which allows network nodes to have computing and …
[HTML][HTML] Deep Reinforcement Learning techniques for dynamic task offloading in the 5G edge-cloud continuum
The integration of new Internet of Things (IoT) applications and services heavily relies on
task offloading to external devices due to the constrained computing and battery resources …
task offloading to external devices due to the constrained computing and battery resources …
Adaptive request scheduling and service caching for MEC-assisted IoT networks: An online learning approach
D Ren, X Gui, K Zhang - IEEE Internet of Things Journal, 2022 - ieeexplore.ieee.org
Multiaccess edge computing (MEC) is a new paradigm to meet the demand of resource-
hungry and latency-sensitive services by enabling the placement of services and execution …
hungry and latency-sensitive services by enabling the placement of services and execution …