Network traffic routing using effective bandwidth theory

2009 ◽  
Vol 20 (7) ◽  
pp. 660-667 ◽  
Author(s):  
Adam Kozakiewicz ◽  
Krzysztof Malinowski
Keyword(s):  
Network Traffic ◽  
Effective Bandwidth ◽  
Traffic Routing

scholarly journals SDN based Network Traffic Routing in Vehicular Networks: A Scheme and Simulation Analysis

2020 ◽  
Author(s):  
Jitendra Bhatia ◽  
Mohammad Obaidat ◽  
Tirath Savasaiya ◽  
Hardik Trivedi ◽  
Sudeep Tanwar ◽  
...  
Keyword(s):  
Network Traffic ◽  
Vehicular Networks ◽  
Simulation Analysis ◽  
Traffic Routing

A STUDY OF ROUTING METHODS IN URBAN WIRELESS MESH NETWORKS BASED ON REINFORCEMENT LEARNING

2021 ◽  
Author(s):  
В.Д. ФАМ ◽  
Р.В. КИРИЧЕК ◽  
А.С. БОРОДИН
Keyword(s):  
Artificial Intelligence ◽  
Reinforcement Learning ◽  
Simulation Model ◽  
Network Traffic ◽  
Mesh Networks ◽  
Mesh Network ◽  
Queueing Model ◽  
Traffic Routing ◽  
Wireless Mesh ◽  
Routing Methods

Приведены результаты исследования методов маршрутизации на основе обучения с подкреплением с помощью имитационной модели. Рассмотрена задача маршрутизации сетевого трафика для фрагмента ячеистой сети городского масштаба, управляемой на основе технологий искусственного интеллекта. Представлена модель системы массового обслуживания для изучения процесса маршрутизации, а также обучения выбора маршрута. Имитационная модель фрагмента ячеистой сети разработана в пакете Anylogic и обучается на основе платформы Microsoft Bonsai. The results of the study of network traffic routing methods based on reinforcement learning using a simulation model are presented. The problem of network traffic routing for a fragment of a city-scale mesh network, controlled on the basis of artificial intelligence technologies, is considered. The article presents a queueing model for studying the routing process, as well as learning how to choose a route. The mesh network fragment simulation model was developed in the Anylogic package and is trained on the basis of the Microsoft Bonsai platform.

scholarly journals Search algorithm for loopless routes

2020 ◽  
pp. 10-25
Author(s):  
Maksim Sergeevich Demichev ◽  
Konstantin Eduardovich Gaipov
Keyword(s):  
Network Topology ◽  
Network Traffic ◽  
Data Transmission ◽  
Search Algorithm ◽  
Heavy Traffic ◽  
Speed Reduction ◽  
C Language ◽  
Traffic Routing ◽  
Route Search ◽  
Formation Phase

The subject of this research is the search algorithm for loopless routes from transmitter to the recipient of network traffic in the conditions of a known network topology. In designing data transmission network, one of the primary problems is the formation of network traffic routing, due to the fact that heavy traffic often cause the occurrence of bottlenecks in form of the overloaded communication node, which results in speed reduction of data transmission. This article provides the search algorithm for loopless routes from transmitter to the recipient of network traffic; the result is presented as a set of loopless  routes in accordance with the specified network topology. The article also provides the software code of the algorithm written in the C# language, as well as the results of test solutions of the specified topologies. The algorithm was developed via experimental and theoretical methods, on the bases of the available route search algorithms, such as Floyd's algorithm and Dijkstra's algorithm, as well as mechanisms of static and dynamic routing, such as RIP, OSPF, and EIGRP. The novelty of this work consists in elaboration of search algorithm for loopless routes from transmitter to the recipient in the conditions of the available network topology; and in comparison of the acquired results with other methods of formation phase variables. This algorithm allows generating a list of all loopless routes within the indicated network topology between the pair of interacting nodes.

Providing Quality of Service to Computer Networks through Traffic Modeling

2010 ◽  
pp. 160-181
Author(s):  
Flávio Henrique Teles Vieira ◽  
George E. Bozinis
Keyword(s):  
Communication Networks ◽  
Network Traffic ◽  
Loss Probability ◽  
Estimation Methods ◽  
Probability Estimation ◽  
Effective Bandwidth ◽  
Traffic Modeling ◽  
Traffic Flows ◽  
Data Loss ◽  
Network Traffic Modeling

In this chapter, the authors examine two important network traffic issues: estimation of effective bandwidth and data loss probability in communication networks. They focus on estimation approaches based on network traffic modeling. Initially, they review some concepts related to network traffic modeling such as monofractal and multifractal properties. Further, they address the issue of estimating the effective bandwidth for network traffic flows. Besides effective bandwidth, the knowledge of the loss probability explicitly allows us to guarantee some QoS parameters required by the traffic flows, for example, by discarding flows with intolerable byte loss rate. In this sense, the authors present an overview of loss probability estimation methods including an approach that considers multifractal characteristics of network traffic. That is, given the model parameters, the data loss probability for network traffic can be directly computed. They conclude that both the multifractal based effective bandwidth and loss probability estimation methods can be powerful tools for really providing QoS to network flows.

scholarly journals Attack-Aware IoT Network Traffic Routing Leveraging Ensemble Learning

Sensors ◽  
2021 ◽  
Vol 22 (1) ◽  
pp. 241
Author(s):  
Qasem Abu Al-Haija ◽  
Ahmad Al-Badawi
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Network Traffic ◽  
Kernel Methods ◽  
Ensemble Methods ◽  
Error Rates ◽  
Identification Accuracy ◽  
Traffic Routing ◽  
Network Intrusion ◽  
Network Methods

Network Intrusion Detection Systems (NIDSs) are indispensable defensive tools against various cyberattacks. Lightweight, multipurpose, and anomaly-based detection NIDSs employ several methods to build profiles for normal and malicious behaviors. In this paper, we design, implement, and evaluate the performance of machine-learning-based NIDS in IoT networks. Specifically, we study six supervised learning methods that belong to three different classes: (1) ensemble methods, (2) neural network methods, and (3) kernel methods. To evaluate the developed NIDSs, we use the distilled-Kitsune-2018 and NSL-KDD datasets, both consisting of a contemporary real-world IoT network traffic subjected to different network attacks. Standard performance evaluation metrics from the machine-learning literature are used to evaluate the identification accuracy, error rates, and inference speed. Our empirical analysis indicates that ensemble methods provide better accuracy and lower error rates compared with neural network and kernel methods. On the other hand, neural network methods provide the highest inference speed which proves their suitability for high-bandwidth networks. We also provide a comparison with state-of-the-art solutions and show that our best results are better than any prior art by 1~20%.

Detection and Classification of Anomalies in Network Traffic Using Generalized Entropies and OC-SVM with Mahalanobis Kernel

2014 ◽  
Author(s):  
Jayro Santiago-Paz ◽  
Deni Torres-Roman ◽  
Angel Figueroa-Ypiña
Keyword(s):  
Network Traffic ◽  
Generalized Entropies
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