Xiao et al., 2024 - Google Patents
Adaptive joint routing and caching in knowledge-defined networking: An actor-critic deep reinforcement learning approachXiao et al., 2024
- Document ID
- 15689120069428127561
- Author
- Xiao Y
- Yu H
- Yang Y
- Wang Y
- Liu J
- Ansari N
- Publication year
- Publication venue
- IEEE Transactions on Mobile Computing
External Links
Snippet
By integrating the software-defined networking (SDN) architecture with the machine learning- based knowledge plane, knowledge-defined networking (KDN) is revolutionizing established traffic engineering (TE) methodologies. This paper investigates the challenging …
- 230000006855 networking 0 title abstract description 17
Classifications
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L12/00—Data switching networks
- H04L12/54—Store-and-forward switching systems
- H04L12/56—Packet switching systems
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network-specific arrangements or communication protocols supporting networked applications
- H04L67/10—Network-specific arrangements or communication protocols supporting networked applications in which an application is distributed across nodes in the network
- H04L67/1002—Network-specific arrangements or communication protocols supporting networked applications in which an application is distributed across nodes in the network for accessing one among a plurality of replicated servers, e.g. load balancing
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L12/00—Data switching networks
- H04L12/02—Details
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L41/00—Arrangements for maintenance or administration or management of packet switching networks
- H04L41/50—Network service management, i.e. ensuring proper service fulfillment according to an agreement or contract between two parties, e.g. between an IT-provider and a customer
- H04L41/5041—Service implementation
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L45/00—Routing or path finding of packets in data switching networks
- H04L45/12—Shortest path evaluation
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L47/00—Traffic regulation in packet switching networks
- H04L47/10—Flow control or congestion control
- H04L47/24—Flow control or congestion control depending on the type of traffic, e.g. priority or quality of service [QoS]
- H04L47/2441—Flow classification
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L45/00—Routing or path finding of packets in data switching networks
- H04L45/04—Interdomain routing, e.g. hierarchical routing
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L45/00—Routing or path finding of packets in data switching networks
- H04L45/02—Topology update or discovery
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network-specific arrangements or communication protocols supporting networked applications
- H04L67/32—Network-specific arrangements or communication protocols supporting networked applications for scheduling or organising the servicing of application requests, e.g. requests for application data transmissions involving the analysis and optimisation of the required network resources
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L45/00—Routing or path finding of packets in data switching networks
- H04L45/30—Special provisions for routing multiclass traffic
- H04L45/302—Route determination based on requested QoS
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L69/00—Application independent communication protocol aspects or techniques in packet data networks
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L63/00—Network architectures or network communication protocols for network security
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L49/00—Packet switching elements
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Wei et al. | Network slice reconfiguration by exploiting deep reinforcement learning with large action space | |
| Liu et al. | DRL-OR: Deep reinforcement learning-based online routing for multi-type service requirements | |
| Sun et al. | Cooperative computation offloading for multi-access edge computing in 6G mobile networks via soft actor critic | |
| Sun et al. | Autonomous resource slicing for virtualized vehicular networks with D2D communications based on deep reinforcement learning | |
| Zhou et al. | Learning from peers: Deep transfer reinforcement learning for joint radio and cache resource allocation in 5G RAN slicing | |
| Chen et al. | Joint resource allocation for software-defined networking, caching, and computing | |
| Chen et al. | Reinforcement learning–based QoS/QoE‐aware service function chaining in software‐driven 5G slices | |
| Chen et al. | ALBRL: Automatic Load‐Balancing Architecture Based on Reinforcement Learning in Software‐Defined Networking | |
| Ghosh et al. | A cognitive routing framework for reliable communication in IoT for industry 5.0 | |
| Suzuki et al. | Multi-agent deep reinforcement learning for cooperative computing offloading and route optimization in multi cloud-edge networks | |
| Mai et al. | Multi-agent actor-critic reinforcement learning based in-network load balance | |
| Ye et al. | Mitigating routing update overhead for traffic engineering by combining destination-based routing with reinforcement learning | |
| Xiao et al. | Adaptive joint routing and caching in knowledge-defined networking: An actor-critic deep reinforcement learning approach | |
| Xiao et al. | Scalable QoS-aware multipath routing in hybrid knowledge-defined networking with multiagent deep reinforcement learning | |
| Ye et al. | FlexDATE: Flexible and disturbance-aware traffic engineering with reinforcement learning in software-defined networks | |
| Wei et al. | GRL-PS: Graph embedding-based DRL approach for adaptive path selection | |
| He et al. | RTHop: Real-time hop-by-hop mobile network routing by decentralized learning with semantic attention | |
| Chen et al. | Fault tolerance oriented SFC optimization in SDN/NFV-enabled cloud environment based on deep reinforcement learning | |
| Chiu et al. | Reinforcement Learning‐Based Service‐Oriented Dynamic Multipath Routing in SDN | |
| Guo et al. | Intelligent edge network routing architecture with blockchain for the IoT | |
| Khoramnejad et al. | Distributed multi-agent learning for service function chain partial offloading at the edge | |
| Wei et al. | An adaptive service function chains mapping with multi-task deep reinforcement learning | |
| Han et al. | Multi-SP network slicing parallel relieving edge network conflict | |
| Feng et al. | CaRCS: Joint optimization of computing-aware routing and collaborative scheduling in computing power networks | |
| Li et al. | Online coordinated NFV resource allocation via novel machine learning techniques |