Adaptive Traffic Congestion Control Of Traffic Network
1
Citation
4
Reference
10
Related Paper
Abstract:
This paper studies the adaptive traffic congestion control of traffic network from the system-theoretic viewpoint. The time-dependent characteristics of traffic congestion length are described by a linear time-varying discrete dynamical system based on the traffic volume at each signalized intersection. An adaptive control system of the traffic congestion length in a traffic network using a decentralized control concept is proposed. The authors also propose two adaptive control algorithms for traffic congestion length control in a traffic network. One is a priority control algorithm, the other is a balance control algorithm.Keywords:
Traffic policing
Network traffic simulation
Traffic wave
This paper discusses modeling and simulation of ATM traffic management. It presents a survey on the requirements needed for such task considering previous work in the area. The survey encloses the following topics: the modeling approach; the requirements for trustworthy performance and QoS analysis; and the choice of the simulation environment where the models will be implemented. The paper also presents a new ATM network model implemented in a communication systems simulation environment called SimNT 2.0. The network model covers SVC routing and management, ATM traffic management functions, packet and cell transport/processing and client behavior. The TM functions taken into account include per-VC queuing, weighted fair-share scheduling, traffic shaping, effective bandwidth connection admission control, selective cell discarding, leaky-bucket traffic policing and traffic contract configuration and negotiation. Numerical results briefly demonstrate the network model.
Traffic policing
Network traffic simulation
Token bucket
Admission control
Cite
Citations (4)
A Set Top Box (STB) for co-operative traffic light systems offers a new solution for a modular and dynamical traffic management. The communication is established between control units of traffic lights and vehicles via manufacturer independent interfaces of the STBs which are connected to the traffic lights control cabinet. Circuit times of the traffic lights and optimized speed suggestions to keep the traffic flow are presented to the vehicle's drivers on a standard PDA. Vehicles transfer information about their own speed, position and intended driving routes to the STBs. The gathered traffic information is used by the STB in order to optimize the circuit times of the traffic lights. This new application offers a new low-cost and adaptive traffic management to reduce traffic congestions.
Traffic wave
Traffic policing
Traffic conflict
Traffic bottleneck
Cite
Citations (0)
For Internet service providers to efficiently use network resources, they need to conduct traffic engineering to dynamically control traffic routes to accommodate traffic with limited network resources. The performance of traffic engineering depends on the accuracy of traffic prediction. However, the volume of network traffic has been changing drastically in recent years due to the growth of various types of network services, making traffic prediction increasingly difficult. Our simple ideas to overcome this challenge are to separate traffic into predictable and unpredictable parts and to apply different control policies to predictable and unpredictable traffic. To promote these ideas, we use software-defined networking technology, particularly Open-Flow, that can control macroflows defined by any combination of L2-L4 packet header information such as 5-tuple. In this paper, we therefore propose the macroflow-generating method for separating traffic into predictable macroflows that have little traffic variation and unpredictable macroflows that have large traffic variation within a limited flow table size. We also propose a macroflow-based traffic engineering scheme that uses different routing policies in accordance with traffic predictability. Simulation evaluation results suggest that our proposed scheme can reduce the maximum link load in a network at the most congested time by 34% and the average link load in a network on average by 11% compared with the current traffic engineering schemes.
Internet traffic engineering
Network traffic simulation
Traffic policing
Predictability
Cite
Citations (6)
Network traffic simulation
Traffic policing
Internet traffic engineering
Cite
Citations (1)
This paper discusses modeling and simulation of ATM traffic management. It presents a survey on the requirements needed for such task considering previous work in the area. The survey encloses the following topics: the modeling approach; the requirements for trustworthy performance and QoS analysis; and the choice of the simulation environment where the models will be implemented. The paper also presents a new ATM network model implemented in a communication systems simulation environment called SimNT 2.0. The network model covers SVC routing and management, ATM traffic management functions, packet and cell transport/processing and client behavior. The TM functions taken into account include per-VC queuing, weighted fair-share scheduling, traffic shaping, effective bandwidth connection admission control, selective cell discarding, leaky-bucket traffic policing and traffic contract configuration and negotiation. Numerical results briefly demonstrate the network model.
Traffic policing
Network traffic simulation
Admission control
Token bucket
Cite
Citations (4)
A challenge in the traffic management of emerging networks is to describe, analyze and control their complex traffic flows in ways that can be applied in practice. Our approach to this problem starts with collection and analysis of high-resolution traffic traces from working networks; models that can accurately and adequately describe these complex flows are devised, which then lead to the development of appropriate and practical traffic management methods. This paper takes a close look at network traffic measurements based on our experiences analyzing a large number of traffic traces collected from a wide range of technologies including Ethernet, ISDN packet, CCS, Internet, frame relay and ATM. We give an overview of lessons learned regarding actual traffic patterns in various packet networks, discussing aggregate network traffic and individual connection traffic, and traffic characteristics found in various protocol layers such as user, link layer, and service; we point out various factors that can impact the observed traffic characteristics. We use real examples to discuss the impacts of equipment and protocol implementations on network traffic patterns.
Network traffic simulation
Internet traffic engineering
Traffic policing
Frame Relay
Cite
Citations (7)
One of the main difficulties, when dimensioning a telecommunication network, is to deal with the random nature of the input traffic. The traffic control goal is to protect both the receiving terminal equipments and the network elements (e.g. multiplexers, switches, etc.) against traffic excess. Once the traffic is controlled, it becomes more predictable for the network and resource utilization can be optimized. We begin the presentation with a brief introduction to B-ISDN networks and to the ATM technique that has been retained as the transfer mode of these networks. It turns out that traffic control mechanisms that are used in existing networks are not adapted to the characteristics of high speed networks. These characteristics are mainly the high bandwidth-propagation delay product and the heterogeneity of the input traffic (voice, video, data, etc.). We present the properties that are required for traffic control mechanisms in order to be well suited to these network characteristics and the general traffic management framework of ATM networks. We finish the talk by presenting some open problems, related to preventive and reactive traffic control, that still required more research work.
Traffic policing
Network traffic simulation
Dimensioning
Cite
Citations (0)
Nowadays, with the increasing deployment of modern packet-switching networks,
traffic classification is playing an important role in network administration. To
identify what kinds of traffic transmitting across networks can improve network
management in various ways, such as traffic shaping, differential services, enhanced
security, etc. By applying different policies to different kinds of traffic, Quality
of Service (QoS) can be achieved and the granularity can be as fine as flow-level.
Since illegal traffic can be identified and filtered, network security can be enhanced
by employing advanced traffic classification.
There are various traditional techniques for traffic classification. However,
some of them cannot handle traffic generated by applications using non-registered
ports or forged ports, some of them cannot deal with encrypted traffic and some
techniques require too much computational resources. The newly proposed technique
by other researchers, which uses statistical methods, gives an alternative
approach. It requires less resources, does not rely on ports and can deal with encrypted
traffic. Nevertheless, the performance of the classification using statistical
methods can be further improved.
In this thesis, we are aiming for optimising network traffic classification based
on the statistical approach. Because of the popularity of the TCP protocol, and
the difficulties for classification introduced by TCP traffic controls, our work is
focusing on classifying network traffic based on TCP protocol. An architecture has
been proposed for improving the classification performance, in terms of accuracy
and response time. Experiments have been taken and results have been evaluated
for proving the improved performance of the proposed optimised classifier.
In our work, network packets are reassembled into TCP flows. Then, the
statistical characteristics of flows are extracted. Finally the classes of input flows
can be determined by comparing them with the profiled samples. Instead of using only one algorithm for classifying all traffic flows, our proposed system employs
a series of binary classifiers, which use optimised algorithms to detect different
traffic classes separately. There is a decision making mechanism for dealing with
controversial results from the binary classifiers. Machining learning algorithms
including k-nearest neighbour, decision trees and artificial neural networks have
been taken into consideration together with a kind of non-parametric statistical
algorithm — Kolmogorov-Smirnov test. Besides algorithms, some parameters are
also optimised locally, such as detection windows, acceptance thresholds. This
hierarchical architecture gives traffic classifier more flexibility, higher accuracy
and less response time.
Traffic classification
Network traffic simulation
Traffic policing
Deep Packet Inspection
Cite
Citations (0)
Traffic flow optimization within traffic networks has been approached through different kinds of methods. One of the methods is to reconfigure the traffic signal timing plan. However, dynamic characteristic of the traffic flow is not able to be resolved by the conventional traffic signal timing plan management. As a result, traffic congestion still remains as an unsolved problem. Thus, in this study, artificial intelligence algorithm has been introduced in the traffic signal timing plan to enable the traffic management systems' learning ability. Q-Learning algorithm acts as the learning mechanism for traffic light intersections to release itself from traffic congestions situation. Adjacent traffic light intersections will work independently and yet cooperate with each other to a common goal of ensuring the fluency of the traffic flows within traffic network. The experimental results show that the Q-Learning algorithm is able to learn from the dynamic traffic flow and optimized the traffic flow.
Traffic wave
SIGNAL (programming language)
Traffic bottleneck
Traffic policing
Signal timing
Cite
Citations (41)
The control of road traffic lights and road traffic signs line set together to form an integral part of road traffic control and management.Traffic signal control has played an important role in the entire road traffic engineering,it is mainly used in traffic volume,traffic flow more conflict and complex intersections,as well as control the direction of traffic flow on certain road sections.For a number of deficiencies in the existing traffic signal control,a number of vehicles in the traffic junctions to guide the factors,the use of VC+ + to write semaphores real-time control program,in order to achieve real-time control of traffic lights.
Traffic wave
Traffic bottleneck
Traffic conflict
Semaphore
Traffic policing
Cite
Citations (0)