scholarly journals DRSIR: A Deep Reinforcement Learning Approach for Routing in Software-Defined Networking

2021 ◽  
Author(s):  
Daniela Casas Velasco ◽  
Oscar Mauricio Caicedo Rendon ◽  
Nelson Luis Saldanha da Fonseca
Keyword(s):  
Reinforcement Learning ◽  
Routing Protocols ◽  
Routing Algorithm ◽  
Routing Algorithms ◽  
Limited Information ◽  
State Spaces ◽  
Viable Solution ◽  
Slow Adaptation ◽  
Intelligent Routing

Traditional routing protocols employ limited information to make routing decisions which leads to slow adaptation to traffic variability and restricted support to the quality of service requirements of the applications. To address these shortcomings, in previous work, we proposed RSIR, a routing solution based on Reinforcement Learning (RL) in SoftwareDefined Networking (SDN). However, RL-based solutions usually suffer an increase in the learning process when dealing with large action and state spaces. This paper introduces a different routing approach called Deep Reinforcement Learning and SoftwareDefined Networking Intelligent Routing (DRSIR). DRSIR defines a routing algorithm based on Deep RL (DRL) in SDN that overcomes the limitations of RL-based solutions. DRSIR considers path-state metrics to produce proactive, efficient, and intelligent routing that adapts to dynamic traffic changes. DRSIR was evaluated by emulation using real and synthetic traffic matrices. The results show that this solution outperforms the routing algorithms based on the Dijkstra’s algorithm and RSIR, in relation to stretching (stretch), packet loss, and delay. Moreover, the results obtained demonstrate that DRSIR provides a practical and viable solution for routing in SDN.

scholarly journals DRSIR: A Deep Reinforcement Learning Approach for Routing in Software-Defined Networking

2021 ◽  
Author(s):  
Daniela Casas Velasco ◽  
Oscar Mauricio Caicedo Rendon ◽  
Nelson Luis Saldanha da Fonseca
Keyword(s):  
Reinforcement Learning ◽  
Routing Protocols ◽  
Routing Algorithm ◽  
Routing Algorithms ◽  
Limited Information ◽  
State Spaces ◽  
Viable Solution ◽  
Slow Adaptation ◽  
Intelligent Routing

Traditional routing protocols employ limited information to make routing decisions which leads to slow adaptation to traffic variability and restricted support to the quality of service requirements of the applications. To address these shortcomings, in previous work, we proposed RSIR, a routing solution based on Reinforcement Learning (RL) in SoftwareDefined Networking (SDN). However, RL-based solutions usually suffer an increase in the learning process when dealing with large action and state spaces. This paper introduces a different routing approach called Deep Reinforcement Learning and SoftwareDefined Networking Intelligent Routing (DRSIR). DRSIR defines a routing algorithm based on Deep RL (DRL) in SDN that overcomes the limitations of RL-based solutions. DRSIR considers path-state metrics to produce proactive, efficient, and intelligent routing that adapts to dynamic traffic changes. DRSIR was evaluated by emulation using real and synthetic traffic matrices. The results show that this solution outperforms the routing algorithms based on the Dijkstra’s algorithm and RSIR, in relation to stretching (stretch), packet loss, and delay. Moreover, the results obtained demonstrate that DRSIR provides a practical and viable solution for routing in SDN.

scholarly journals Intelligent Routing Control for MANET Based on Reinforcement Learning

2018 ◽  
Vol 232 ◽  
pp. 04002
Author(s):  
Fang Dong ◽  
Ou Li ◽  
Min Tong
Keyword(s):  
Reinforcement Learning ◽  
Control Algorithm ◽  
Rapid Development ◽  
Selection Strategy ◽  
Performance Simulation ◽  
Multiple Parameters ◽  
Intelligent Routing ◽  
Routing Control ◽  
The Cost

With the rapid development and wide use of MANET, the quality of service for various businesses is much higher than before. Aiming at the adaptive routing control with multiple parameters for universal scenes, we propose an intelligent routing control algorithm for MANET based on reinforcement learning, which can constantly optimize the node selection strategy through the interaction with the environment and converge to the optimal transmission paths gradually. There is no need to update the network state frequently, which can save the cost of routing maintenance while improving the transmission performance. Simulation results show that, compared with other algorithms, the proposed approach can choose appropriate paths under constraint conditions, and can obtain better optimization objective.

Propose a new algorithm of routing with information collected locally

2016 ◽  
Vol 2 (1) ◽  
pp. 70
Author(s):  
Tran Minh Anh ◽  
Nguyen Chien Trinh
Keyword(s):  
Routing Algorithm ◽  
Qos Routing ◽  
Local Information ◽  
Routing Algorithms ◽  
Source Node ◽  
Quality Of Services ◽  
Global State ◽  
State Information ◽  
Near Future

The type of algorithm which uses local information collected from source node for Quality of Service (QoS) routing has recently been researched as an alternative to QoS routing algorithms that traditionally use global state information. This algorithm, collecting information from source node only, helps flow routing better and assures more flexibly QoS for network. This trend leads to a new solution for satisfying the higher and higher demand of telecom market in the near future. In this paper, we introduce a new algorithm of routing like that type for assuring the quality of network as well as quality of services. The simulations at last section show the advantages over some other localized routing algorithms and global routing algorithm.

scholarly journals A Routing Algorithm for the Sparse Opportunistic Networks Based on Node Intimacy

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Gang Xu ◽  
Xinyue Wang ◽  
Na Zhang ◽  
Zhifei Wang ◽  
Lin Yu ◽  
...  
Keyword(s):  
Routing Algorithm ◽  
Opportunistic Networks ◽  
Network Routing ◽  
Routing Algorithms ◽  
Time Slice ◽  
Message Delivery ◽  
Opportunistic Network ◽  
Network Resources ◽  
Subsequent Time

Opportunistic networks are becoming more and more important in the Internet of Things. The opportunistic network routing algorithm is a very important algorithm, especially based on the historical encounters of the nodes. Such an algorithm can improve message delivery quality in scenarios where nodes meet regularly. At present, many kinds of opportunistic network routing algorithms based on historical message have been provided. According to the encounter information of the nodes in the last time slice, the routing algorithms predict probability that nodes will meet in the subsequent time slice. However, if opportunistic network is constructed in remote rural and pastoral areas with few nodes, there are few encounters in the network. Then, due to the inability to obtain sufficient encounter information, the existing routing algorithms cannot accurately predict whether there are encounters between nodes in subsequent time slices. For the purpose of improving the accuracy in the environment of sparse opportunistic networks, a prediction model based on nodes intimacy is proposed. And opportunistic network routing algorithm is designed. The experimental results show that the ONBTM model effectively improves the delivery quality of messages in sparse opportunistic networks and reduces network resources consumed during message delivery.

A New Localized Multi-Constraint QoS Routing Algorithm

2017 ◽  
Author(s):  
Tran Minh Anh ◽  
Nguyen Chien Trinh
Keyword(s):  
Routing Algorithm ◽  
Qos Routing ◽  
Routing Algorithms ◽  
Source Node ◽  
Global Network ◽  
Traffic Patterns ◽  
State Information ◽  
Alternative Approach ◽  
Qos Routing Algorithm

The scheme of Quality of Service (QoS) routing algorithms based on local state information has recently been proposed as an alternative approach to the traditional QoS routing algorithms. By implementing this localized QoS routing algorithm, each source node predetermines and maintains a set of candidate paths for each destination. These sets of paths will help the source node to decide the most appropriate path for a connection request. Hence, it helps to avoid the problems associated with the maintenance of the global network state information. In this paper, we propose a new and effective localized QoS routing algorithm, compare its performance with those of other localized algorithms and a traditional QoS routing algorithm under the same type of network topology, QoS requirements and traffic patterns. The simulations results show that our proposed algorithm can perform better than other routing algorithms. DOI: 10.32913/rd-ict.vol3.no14.258

Performance Comparison of Routing Protocols in Bipartite Wireless Sensor Network

2015 ◽  
Vol 5 (6) ◽  
pp. 1417
Author(s):  
Devashish Gosain ◽  
Itu Snigdh
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Network Topology ◽  
Routing Protocols ◽  
Performance Comparison ◽  
Routing Algorithms ◽  
Wireless Sensor ◽  
Mobile Nodes ◽  
Average Delay

This paper evaluates and ranks the suitability of routing algorithms for bipartite wireless sensor network topology. The network considered in this paper, consists of an irregular combination of fixed and mobile nodes, which leads to construction of a bipartite graph among them. A wireless sensor network is usually constrained by the energy limitations and processing capabilities. We therefore, consider the important metrics for analysis namely, carried load, energy consumption and the average delay incurred. We present the possibilities of employing the routing algorithms subject to the quality of service required by the wireless sensor networks applications

An Intelligent Routing Algorithm in Wireless Sensor Networks Based on Reinforcement Learning

2014 ◽  
Vol 678 ◽  
pp. 487-493 ◽  
Author(s):  
Wen Jing Guo ◽  
Cai Rong Yan ◽  
Yang Lan Gan ◽  
Ting Lu
Keyword(s):  
Wireless Sensor Networks ◽  
Reinforcement Learning ◽  
Sensor Networks ◽  
Network Lifetime ◽  
Routing Algorithm ◽  
Wireless Sensor ◽  
Energy Aware ◽  
Hop Count ◽  
Packet Delivery ◽  
Intelligent Routing

Lifetime enhancement has been a hot issue in Wireless Sensor Networks (WSNs). To prolong the network lifetime of WSNs, this paper proposes an intelligent routing algorithm named RLLO. RLLO makes uses of the superiority of reinforcement learning (RL) and considers residual energy and hop count to define the reward function. It is to uniformly distribute the energy consumption and improve the packet delivery without additional cost. This proposed algorithm has been compared with Energy Aware Routing (EAR) and improved EAR (I-EAR). Simulation results show that RLLO gains a significant improvement in terms of network lifetime and packet delivery over these two algorithms.

scholarly journals QoS Routing Algorithm Based on Entropy Granularity in the Network Transmission

2019 ◽  
Vol 19 (4) ◽  
pp. 61-72
Author(s):  
Xiaoling Li ◽  
Hai Hu
Keyword(s):  
Shortest Path ◽  
Rapid Development ◽  
Routing Algorithm ◽  
Routing Algorithms ◽  
Adaptive Genetic Algorithm ◽  
Service Capacity ◽  
Network Resources ◽  
Qos Routing Algorithm

Abstract With the rapid development of computer networks, more hosts are connected to the Internet where they could communicate with each other. The need for network service has exceeded the service capacity of the network, and the Quality of Service (QoS) is gradually declining. Based on existing Shortest Path First (SPF) algorithm, this paper proposes a new QoS required transmission path approach by considering the overhead balance of network resources. This paper uses the entropy granularity as the main line in the application of routing protocols. Firstly, it researches the optimization of routing algorithms for network load balancing resources, routing algorithms based on link traffic distributing weights, link weight optimization based on adaptive genetic algorithm and computational intelligence based on entropy granularity theory. This research proposes a method to apply entropy granularity to Open Shortest Path First (OSPF) routing, including the implementation of the method. After that, a case study is presented by using some examples.

scholarly journals Influence of Self-Similar Traffic Type on Performance of QoS Routing Algorithms

2016 ◽  
Vol 62 (1) ◽  
pp. 81-87 ◽  
Author(s):  
Michał Czarkowski ◽  
Sylwester Kaczmarek ◽  
Maciej Wolff
Keyword(s):  
Routing Algorithm ◽  
Routing Algorithms ◽  
Point Of View ◽  
Telecommunication Networks ◽  
Quality Of Services ◽  
Network Resources ◽  
Performance Point ◽  
New Type ◽  
Self Similar

Abstract Providing a Quality of Services (QoS) into current telecommunication networks based on packet technology is a big challenge nowadays. Network operators have to support a number of new services like voice or video which generate new type of traffic. This traffic serviced with QoS in consequence requires access to appropriate network resources. Additionally, new traffic type is mixed with older one, like best-effort. Analysis of these new and mixed traffic types shows that this traffic is self-similar. Network mechanisms used for delivery of quality of services may depend on traffic type especially from the performance point of view. This paper presents a feasibility study done into the effect of traffic type influence on performance of routing algorithm while the routing algorithm is treated as one of the mechanisms to support QoS in the network.

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