distributed traffic management framework

Distributed Traffic Management Framework

Saurabh NambiarSuryaPrabha C P

Smruthi KShijil

Department of Computer Science andEngineering

Govt. College of Engineering, Kannur

Under the Guidance ofProf. Najeeb K

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Problem Statement

Distributed traffic management framework in which routers aredeployed with intelligent data rate controllers to tackle traffic mass inhigh speed networks.

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Project Outline

• Outline

• Introduction

• Background Information

• Literature Survey

• Requirement and Specification

• Proposed Work

• Design

• Implementation

• Snapshots

• Conclusion

• References

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IntroductionMotivation

• To tackle congestion in high speed traffic.

• To implement fuzzy logic in network.

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IntroductionPurpose and Goal

1. Using fuzzy logic theory to design an explicit rate-based trafficmanagement scheme.

2. The application of fuzzy logic controller using less performanceparameters while providing better performances than the existing.

3. The design of a Fuzzy Smoother mechanism that generaterelatively smooth flow throughput.

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Background Information

• Congestion control

• Fuzzy logic control

• Quality of service

• Max-Min fairness

• Robustness

• Traffic management

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Literature Survey

• TCP feature - source adjusts its window size based on packet losssignal.

• TCP encounters various performance problems ,when the InternetBDP continues to increase.

1. Utilization2. Fairness3. Stability

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Literature Survey

• Explicit Congestion Controls Protocols:

1. XCP2. RCP3. JetMax4. MaxNet

• Inaccurate estimation resulting in performance degradation.

• Queue size not stable due to oscillations- affects the stability oftheir sending rates.

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HARDWARE REQUIREMENTS

• Processor - Pentium IV

• Speed - 1.1 Ghz

• RAM - 256 MB(min)

• Hard Disk - 20 GB

• Key Board - Standard Windows Keyboard

• Mouse - Two or Three Button Mouse

• Monitor - SVGA

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SOFTWARE REQUIREMENTS

• Operating System : Windows XP

• Programming Language : JAVA.

• Java Version : JDK 1.6 & above.

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Proposed work

• Using fuzzy logic theory to design an explicit rate-based trafficmanagement scheme (called the IntelRate controller) for thehigh-speed IP networks.

• The application of such a fuzzy logic controller using lessperformance parameters while providing better performances thanthe existing explicit traffic control protocols.

• The design of a Fuzzy Smoother mechanism that can generaterelatively smooth flow throughput.

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DesignNetwork Model

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Network Model

• Congestion occurs when IQSize exceeds buffer capacity.

• Distributed traffic controller implemented inside each router.

• Req rate : Stores the sending rate of source

• Routers role:◦ Calculates source sending rate according to IQSize.◦ Compares it with Req rate.◦ Modifies this field to the lowest of two values.

• Modified value is sent to source using Acknowledgementpacket(ACK).

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Network Model

• For a particular source-destination pair,RTPD, τpi = τfi1 + τfi2 + τbi

RTT, τi = Propagation delay + queueing delay + processing delay

• Let q(t) be the router queue size(IQSize)if q(t) > 0, q(t) = y(t) + v(t) − c(t)if q(t) = 0, q(t) = [y(t) + v(t) − c(t)]+ where [x ]+ = max(0, x)

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IntelRate Controller Design

• TISO(Two Input Single Output)

• Queue Deviation(error), e(t) = q0 - q(t)

• To remove steady state error, g(e(t)) =∫e(t).dt

• Aggeregate output, y(t) = Σui (t − τi )

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IntelRate Controller DesignFuzzy Logic Control

• FLC is non linear mapping of inputs to outputs.

• Four parts:

1. Rule base building2. Fuzzification3. Inference4. Defuzzification

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Fuzzy logic controlRule Base building

• Rule base is set of linguistic values used to map inputs to outputsusing ”If...Then” format.

Figure : Rule base of IntelRate Controller

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Fuzzy Logic ControlFuzzification

• Transforming crisp values into grades of membership of fuzzy set.

• Membership functions(MF) are used for this transformation.

• Fuzzifier: triangular or trapezoidal.

• For any two inputs P1andP2, certainty of a rule is given byZadeh’s AND Logic:µP1

m(p1)⋂µP2

m(p2) = min(µP1m(p1), µP2

m(p2)) : p[i]εPi

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Fuzzy Logic ControlInference with Fuzzy Smoother

• Fuzzy smoother:

1. realize a smaller TBO.2. reduce queueing delay upon heavy traffic.

• Upper and lower limits are set for both inputs.

1. −mq0 ≤ g(e(t)) ≤ mq0

2. q0 − B ≤ e(t) ≤ q0

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Fuzzy Logic ControlDe-Fuzzification

• Membership degree of fuzzy set is transformed to real valued result.

• IntelRate controller uses COG(Centre of Gravity) method.u(t) = (ΣcjSj/(ΣSj )), j = 1, 2..k

where k = No. of rulescj is bottom centroid of triangular MFSj is the area of triangle

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ImplementationSimulation Model

Figure : Simulated Network

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MODULES

• Sender

• Receiver

• Router Queuing Scheme

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SENDER

• Main module

• Divides the file into packets.

• Requests sending rate it desires by depositing a value into adedicated field Req rate inside the packet header.

• Message log to store all requests and references made.

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RECEIVER

• The receiver then sends this value back to the source via an ACKpacket.

• Source modifies the sending rate accordingly.

• Feedback by receiver helps in congestion control.

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ROUTER QUEUING SCHEME

• In this module,router computes transmission rate based on IQ Size

• Compare it with the rate already recorded in Req rate field.

• Chooses lowest value among them.

• Network analysis is done to enhance performance parameters.

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SnapshotsSource Node

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SnapshotsRouter

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SnapshotsRouter Analysis

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SnapshotsRouter Analysis

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SnapshotsRouter Analysis

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SnapshotsReceiver Node

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Conclusion

• IntelRate controller manages the Internet congestion in order toassure the quality of service for different service applications.

• The controller is designed to improve upon the disadvantages ofearlier used congestion protocols.

• Use of Fuzzy logic provides the intelligence equivalent to humansfor decision making.

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Future Work

• Enhancements are possible for a faster design , that is, fasterqueue size calculation using sophisticated techniques.

• In low speed networks, this framework can be enhanced forefficient utilization of bandwidth.

• Better congestion protocols can be implemented using thisframework so that it is compatible with TCP(widely used currentand future transport layer protocol).

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References

• IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT, VOL. 10, NO. 2, JUNE 2013–UsingFuzzy Logic Control to Provide Intelligent Traffic Management Service for High-Speed Networks

• 11th International Conference on Telecommunications - ConTEL 2011 ISBN: 978-953-184-152-8, June 15-17,2011, Graz, Austria–Fuzzy CAC based Traffic Management

• International Journal of Advances in Engineering & Technology, Nov 2011–DESIGN AND SIMULATION OF ANINTELLIGENT TRAFFIC CONTROL SYSTEM

• J. Liu and O. Yang, Stability analysis and evaluation of the IntelRate controller for high-speed heterogeneousnetworks, in Proc. 2011 IEEE ICC, pp. 1-5

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Thank You ! ! !

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