Adaptive and Robust Network Routing Based on Deep Reinforcement Learning with Lyapunov Optimization

2020 ◽  
Author(s):  
Zirui Zhuang ◽  
Jingyu Wang ◽  
Qi Qi ◽  
Jianxin Liao ◽  
Zhu Han
Keyword(s):  
Reinforcement Learning ◽  
Network Routing ◽  
Lyapunov Optimization ◽  
Robust Network

Prioritized Sweeping Reinforcement Learning Based Routing for MANETs

2017 ◽  
Vol 5 (2) ◽  
pp. 383 ◽  
Author(s):  
Rahul M Desai ◽  
B P Patil
Keyword(s):  
Reinforcement Learning ◽  
Shortest Path ◽  
Ad Hoc ◽  
Mobile Ad Hoc Network ◽  
Network Routing ◽  
Delivery Ratio ◽  
Learning Method ◽  
Shortest Path Routing ◽  
Mobile Ad Hoc ◽  
Prioritized Sweeping

<p class="Default">In this paper, prioritized sweeping confidence based dual reinforcement learning based adaptive network routing is investigated. Shortest Path routing is always not suitable for any wireless mobile network as in high traffic conditions, shortest path will always select the shortest path which is in terms of number of hops, between source and destination thus generating more congestion. In prioritized sweeping reinforcement learning method, optimization is carried out over confidence based dual reinforcement routing on mobile ad hoc network and path is selected based on the actual traffic present on the network at real time. Thus they guarantee the least delivery time to reach the packets to the destination. Analysis is done on 50 Nodes Mobile ad hoc networks with random mobility. Various performance parameters such as Interval and number of nodes are used for judging the network. Packet delivery ratio, dropping ratio and delay shows optimum results using the prioritized sweeping reinforcement learning method.</p>

scholarly journals Deep Reinforcement Learning-Based Network Routing Technology for Data Recovery in Exa-Scale Cloud Distributed Clustering Systems

2021 ◽  
Vol 11 (18) ◽  
pp. 8727
Author(s):  
Dong-Jin Shin ◽  
Jeong-Joon Kim
Keyword(s):  
Reinforcement Learning ◽  
Data Transfer ◽  
Block Size ◽  
Network Routing ◽  
Erasure Coding ◽  
Block Transmission ◽  
Other Information ◽  
The Status ◽  
Link Cost ◽  
Network Costs

Research has been conducted to efficiently transfer blocks and reduce network costs when decoding and recovering data from an erasure coding-based distributed file system. Technologies using software-defined network (SDN) controllers can collect and more efficiently manage network data. However, the bandwidth depends dynamically on the number of data transmitted on the network, and the data transfer time is inefficient owing to the longer latency of existing routing paths when nodes and switches fail. We propose deep Q-network erasure coding (DQN-EC) to solve routing problems by converging erasure coding with DQN to learn dynamically changing network elements. Using the SDN controller, DQN-EC collects the status, number, and block size of nodes possessing stored blocks during erasure coding. The fat-tree network topology used for experimental evaluation collects elements of typical network packets, the bandwidth of the nodes and switches, and other information. The data collected undergo deep reinforcement learning to avoid node and switch failures and provide optimized routing paths by selecting switches that efficiently conduct block transfers. DQN-EC achieves a 2.5-times-faster block transmission time and 0.4-times-higher network throughput than open shortest path first (OSPF) routing algorithms. The bottleneck bandwidth and transmission link cost can be reduced, improving the recovery time approximately twofold.

scholarly journals Applications of Multi-Agent Deep Reinforcement Learning: Models and Algorithms

2021 ◽  
Vol 11 (22) ◽  
pp. 10870
Author(s):  
Abdikarim Mohamed Ibrahim ◽  
Kok-Lim Alvin Yau ◽  
Yung-Wey Chong ◽  
Celimuge Wu
Keyword(s):  
Resource Allocation ◽  
Reinforcement Learning ◽  
Network Routing ◽  
Network Resource ◽  
Network Resource Allocation ◽  
Significant Performance ◽  
Multi Agent ◽  
Taxonomic Approach ◽  
And Performance ◽  
Reinforcement Learning Models

Recent advancements in deep reinforcement learning (DRL) have led to its application in multi-agent scenarios to solve complex real-world problems, such as network resource allocation and sharing, network routing, and traffic signal controls. Multi-agent DRL (MADRL) enables multiple agents to interact with each other and with their operating environment, and learn without the need for external critics (or teachers), thereby solving complex problems. Significant performance enhancements brought about by the use of MADRL have been reported in multi-agent domains; for instance, it has been shown to provide higher quality of service (QoS) in network resource allocation and sharing. This paper presents a survey of MADRL models that have been proposed for various kinds of multi-agent domains, in a taxonomic approach that highlights various aspects of MADRL models and applications, including objectives, characteristics, challenges, applications, and performance measures. Furthermore, we present open issues and future directions of MADRL.

scholarly journals Vehicle-Mounted Self-Organizing Network Routing Algorithm Based on Deep Reinforcement Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Shitong Ye ◽  
Lijuan Xu ◽  
Xiaomin Li
Keyword(s):  
Performance Evaluation ◽  
Reinforcement Learning ◽  
Ad Hoc ◽  
Routing Algorithm ◽  
Network Routing ◽  
Technical Problems ◽  
Evaluation Indexes ◽  
Vehicle Communication ◽  
Vehicle Network ◽  
Self Organizing

Through the research on the vehicle-mounted self-organizing network, in view of the current routing technical problems of the vehicle-mounted self-organizing network under the condition of no roadside auxiliary communication unit cooperation, this paper proposes a vehicle network routing algorithm based on deep reinforcement learning. For the problems of massive vehicle nodes and multiple performance evaluation indexes in vehicular ad hoc network, this paper proposes a time prediction model of vehicle communication to reduce the probability of communication interruption and proposes the routing technology of vehicle network by studying the deep reinforcement learning method. This technology can quickly select routing nodes and plan the optimal route according to the required performance evaluation indicators.

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