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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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