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Novel Function Placement of Congestion Control Building Blocks in the

Internet

Kartikeya Chandrayana

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Outline

• Review• Randomized TCP • Uncooperative Congestion Control• virtual AQM• Conclusions

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Congestion Control

• Internet Meltdown – Need for congestion control.

• Congestion Avoidance and Control– End system based techniques.

• TCP

– Network based solutions• Active Queue Management (AQM) e.g. RED

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TCP

• Transmission Control Protocol– Protocol used to transport data– Source: Send a packet, Receiver: Acknowledge the

packet

• Almost all applications (90%) use TCP• What rate to send ?

– No way of knowing what is the available bandwidth

• Probe for bandwidth– In some time “T” send w packets– If Acks for all w packets are rcvd then

• Send w+1 packets next time

– Else • Send w/2 packets

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End-System Based Solution

• TCP + Drop-Tail Queuing.• TCP’s performance suffers on Drop-

Tail queues.– Synchronization

• Congestion window of different flows increase and decrease simultaneously

– Burst losses– Bias against flows with large RTT– Full Queues– Phase Effects

• Only a section of flows get dropped all the time

– Lockout Effect• Few flows monopolize the buffer space

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Active Queue Management

• Proactively Manage Queues – Drop packet before queue overflows– Small queues

• Probabilistic Dropping – Introduces randomization in network

• Early Congestion Indication• Protect TCP Flows

– CBR flows, selfish flows

• e.g. RED (and variants), REM, AVQ, CHOKe

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Random Early Drop (RED)

MinthMaxth

avg: average queue length (EWMA) if avg < Minth then queue packet

if avg > Maxth then drop packet

else, probabilistically drop/accept packet.

Head

Accept Drop/Mark Probabilistically Accept

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AQM Continued

• Have parameters which require configuration– e.g. Threshold to probabilistically drop packets

• Configuration Parameters are generally a function of link capacity, number of flows etc.– Small operating region– RED can perform worse than Drop-Tail queues

• AQMs are not deployed on the Internet

Internet Works with Drop-Tail QueuesProblems with Drop-Tail Queues Persist

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Review: Possible Solutions

Some Buffer Mgmt. Scheme

Users

End System Based Solution: Use same congestion control

algorithm

Network

Routers

Network Based Solution: Use AQM/Scheduler in the network

Limitation

How do we verify the trust ?

Constrains the choice of congestion control algorithms

AQM Placement Required at every router.

Limitations

May require exchange of control information between all AQMs/Schedulers in the network.

Generally only provides Max-Min Fairness.

Most Solutions do not work with a Drop Tail queue Network

What are the alternate architectural responses ?

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Proposed Solution

Users

Network

Core Routers

Uncooperative Congestion Control

Virtual AQM

Edge Routers Any queue mgmt algorithm

Drop Tail/RED etc.

Minimal Changes/upgrades in the

network

Big, Fast Routers, Millions of Flows, Giga Bytes of Data

First place where network can verify trust

Medium Sized Routers, Manageable number of

flow/data

Randomized TCP

Emulate Many Beneficial Properties of RED

Protect TCP Flows, Manage Queues

De-couple congestion control tasks from their placement

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Outline

• Review• Randomized TCP• Uncooperative Congestion Control• virtual AQM• Conclusions

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Randomized TCP

• Randomize the packet sending times = (1+x) RTT/W– X : Uniform(-1, 1)

• Always observe packet conservation

TCP Randomized TCP

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Benefits of Randomized TCP

• End-System solution for introducing randomization in the network

• Emulates many beneficial properties of RED– Breaks synchronization– Spreads losses over time

• Independent losses

– Removes Phase Effects– Removes Bias against large RTT flows– Reduces burst losses

• Competes fairly with TCP Reno

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Randomized TCP: Bias against large RTT flows

TypeThroughput (pkts/sec)

% Share of the

bottleneckLoss (%)

Long Reno 132.05 28 1.2

Short Reno 333.58 72 0.3

Long Reno 215.20 44 0.3

Short Random 277.86 56 0.3

Long Random 214.89 47 0.5

Short Random 242.03 53 0.5

Long Reno-RED 216.05 46 0.3

Short Reno-RED 256.80 54 0.3

Single Bottleneck, Ideal Share: Long (43%), Short(57%)

60 ms

80 ms

2 Mbps

8 Mbps

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Randomized TCP: Phase Effects

8 Mbps5 ms

8 Mbps5 ms

0.8 Mbps100 ms

Randomized TCP removes phase effects

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• Randomized TCP competes fairly with TCP Reno• Removal of Phase Effects, Bias against large RTT

flows, synchronization– Other Single bottleneck setups– Multi-bottleneck setups

• Even one Randomized TCP flow improves performance

• Randomized TCP reduces burst losses• Randomized TCP improves performance of other

window based rate control schemes– Binomial Congestion Control

Randomized TCP: More Results

Randomized TCP can emulate many beneficial properties of REDWe can decouple management of synchronization, phase effect,

bias against large RTT flows, burst losses from AQM design

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Outline

• Review• Randomized TCP• Uncooperative Congestion

Control• virtual AQM• Conclusions

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New Congestion Control Schemes

• Application needs have changed– TCP not suitable– Different congestion control protocols

• Real-Player, Windows Media, Quake, Half-Life etc.

• Linux, FreeBSD Boxes came along– Make your own TCP.– If receive w acks then put w+5 packets in next RTT

• TCP send w+1 packets in next RTT

– If congestion put 3w/4 packets in next RTT• TCP send w/2 packets in next RTT

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Classification

• Responsive – React to congestion indication by cutting

down its rate– e.g. TCP (and its variants)– Selfish/Mis-Behaving

• Maybe

• Un-responsive – Do not react to congestion indications– e.g. UDP, CBR– Selfish/Mis-Behaving

• Always

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Responsive vs Un-responsive

1 Mbps

UDP Source Sending at 600Kbps

Bandwidth left For TCP

1 MbpsResponsive Selfish

Source

Consistently looks at increasing it’s share

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Selfish Responsive Flows: Impact

Drop Tail Queue20 ms 20 ms0.8 Mbps 0.8 Mbps

8 Mbps5 ms

TCP Flows shut out

Traffic Volume Based Denial-of-Service Attack

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Possible Solutions

• Everyone uses TCP• TCP Friendliness

– Any rate control scheme gets the same throughput as TCP under same operating conditions.

– x 1/sqrt(p) (x: rate, p : packet loss probability)

• Network Based Solutions– Use Active Queue Management (AQM)

• e.g. Random Early Drop (RED)– Minth, Maxth, p, Qavg

• FRED, CHOKe etc.– Require Deployment at ALL routers

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AQM: Effect of Misbehavior

RED

RED Queue20 ms 20 ms0.8 Mbps 0.8 Mbps

8 Mbps5 ms

RED Helps: Though unfair sharing persists

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Other TCP Like Schemes• TCP - Every RTT

• W(t+1) = W(t) + ( = 1) if no loss• W(t+1) = (1-)W(t) ( = 0.5) otherwise

• Time-Invariant Schemes– Control parameters do not change with time

• Utility function does not change with time

– Increase : /f(W) f(W) > 0– Decrease: (1-)*g(W) 0 < g(W) < 1– TCP Friendly Schemes

• f(W)g(W) = W– Binomial Congestion Control Schemes

• Increase: /Wk(t) , Decrease: (1-)Wl(t)• TCP Friendly Schemes given by k+l = 1

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Other TCP Like Schemes• Time Invariant Schemes

– Aggressive Selfish schemes: > 1 > 0.5• f(W)g(W) < W• e.g Increase: , Decrease: W0.5(t)

• Time Variant Schemes– Control Parameters change with time (t) > 0 (t) > 0 – Increase: 1/Wk(t) , Decrease: Wl(t)

• k(t) + l(t) = 0

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Consequences..

Users can choose their rate control scheme Rate Control Scheme rate allocation. Aggressive Rate Control More Rate Incentive for users to misbehave. But majority of users are responsible. Traffic-Volume based denial-of-service attack

Assume (for now) the network’s standard CC scheme is TCP

Any scheme which gets more rate than TCP is uncooperative

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Detour: Congestion Control-Optimization Frameworks

• Utility Functions– Economics– One function can

capture a group of rate control schemes.

– TCP-Friendly schemes imply• U(x) -1/x

x (Rate)

U(x)

10 20 1M

1M + 10

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Detour: Congestion Control-Optimization Frameworks

• Users choose congestion control algorithm Choose a Utility Function TCP : U(x) -1/x CC Scheme Utility function

• Every user maximizes his own utility function• Distributed optimization.

• Network imposes capacity constraints• Total input rate cannot exceed capacity• Communicates to users the price of using link• Price : loss rate, mark (ECN), delay

• Users use this price to update their rate

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Optimization Framework: TCP

• TCP tries to minimize delay• Equilibrium allocation (fairness)

– Minimum Potential Delay Fairness

• Max-Min Fairness– U(x) = –1/xN (N )

• Proportional Fairness (TCP Vegas)– U(x) = log(x)

Max -1/xs

s.t. (xs – Cl) 0, for all l

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Work in the Utility Function Space

Key Design Objectives:• Deployment Ease

• Retain existing link price update rules.

No changes in the core.• Retain existing user’s rate

updation rules. Allows users to chose

rate control protocol.

• Should work with either drop or marking based network.

• Should work on a network of Drop Tail queues.

U1

U2

Us

Conformant

Non Conformant

U1,U2 define the conformance space

U

x (Rate)

Selfish

Map user’s Utility Function to Conformant Space

x (Rate)

U1 = U2 = -1/x

Us

Non ConformantU

TCP Friendliness

Map

Map user’s Utility Function to Conformant Space

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• User s is described by: – xs: Rate, Us: Utility function, q: end-to-end price – xs = Us'-1(q)

– If source was using Uobj then rate would be: xs = Uobj'-1(q)

• Communicate to user the price qnew : qnew = Us' (Uobj'-1(q))

• Now user’s update algorithm looks like xs = Us'-1(qnew)

xs = Uobj'-1(q)

Appears as if user is maximizing Uobj !

Map user’s utility function to some (or range of) objective utility function

Us Uobj , Uobj [U1 , U2 ]

How? By Penalty Function Transformation

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Core Network (No Changes)

Any queue mgmt algorithm Drop Tail/RED etc.

Core Routers

Edge Routers

Edge Based Re-Marking Agent

Maps utility function

Manages Selfish Flows. (Decouple it from AQM design)

Provides Service differentiation (Map users to different utility functions).

Users

Free to choose their congestion control algorithm

Either marking or dropping

Idea: Remap @Edge, Not in every Router

Decouple Management of Selfish Flows from AQM Design

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What do we need to make it work ?

• Estimate utility function– Currently using Least Squares, Recursive LS– Needs only estimates of sending and loss rates

• Estimate loss/mark rate– Currently using EWMA, WALI methods of TFRC

• Need to identify misbehaving flows.– Smart Sampling in Netflow, Sample & Hold etc

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Utility Function Estimation

• Increase: /xk(t) , Decrease xl(t)• Utility function (n = k+l)

– U = - /(Rn (xR)n)– U -1/xn

– U’(x) = p– log(p) = log(nK) – (n+1)log(x)

•Use linear least squares to estimate n

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Results: Single Bottleneck

Drop Tail RED/ ECN Enabled

x Mbps

4x Mbps

20 ms

5 ms

TCP Reno, U=-1/x

Mis-Behaving (U=-1/x0.5)

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Results: Multi-Bottleneck (Drop Tail)

Framework prevents volume based denial of service attack.

Without Re-Mapping

TCP Flows shut out

With Re-Mapping

Drop-Tail Queue20 ms 20 ms0.8 Mbps 0.8 Mbps

8 Mbps5 ms

TCP Reno, U=-1/x

Selfish (U=-1/x0.5) Selfish (U=-1/x0.5)

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Results: Multi-Bottleneck (RED)

Framework improves fair sharing of network

Without Re-Mapping With Re-Mapping

RED Queue20 ms 20 ms0.8 Mbps 0.8 Mbps

8 Mbps5 ms

TCP Reno, U=-1/x

Selfish (U=-1/x0.5) Selfish (U=-1/x0.5)

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Results: Multi-Bottleneck in an ECN Enabled Network

Ideal Case No Re-Mapping

With Re-Mapping

RED Queue20 ms 20 ms0.8 Mbps 0.8 Mbps

8 Mbps5 ms

TCP Reno, U=-1/x

Selfish (U=-1/x0.5) Selfish (U=-1/x0.5)

Congestion Response Conformance

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Utility Function Estimation Results

x Mbps

4x Mbps

20 ms

5 ms

TCP Reno, U=-1/x

Mis-Behaving (U=-1/x0.5)

N = 0.6, (Ideal: N=0.5) N = 0.8, (Ideal: N=1.0)

Can estimate the exponent with a very small sample set

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More Results• Background Traffic

– Web (http) Traffic– Single/Multi Bottleneck scenarios

• Cross Traffic– Reverse path congestion– Especially important with RED– Multi-Bottleneck scenarios

• Comparison with other AQM schemes

• Differentiated Services

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Outline

• Review• Randomized TCP• Uncooperative Congestion Control• virtual AQM• Conclusions

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virtual AQM: Definitions

R1

R2 R3

R4

Stream F

Stream G

Stream H

I1

E1

I1- R1 - R2 - R3 - E1 : Path

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virtual AQM: Definitions

• Path Capacity : Minimum Link Capacity on a Path– Send a pair of back-to-back packets through Priority

Queues

• Path Demand : Demand on a Path• Send a packet train through data queue

a + cCeff = 8*S/c

a

D = C - Ceff

Cross-Traffic

C = 8*S/

S Bytes

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virtual AQM: Algorithm : network utilization ( < 1)• Calculate virtual path capacity as

• Cv = * path Capacity

• Idea : Match Demand to Virtual path capacity at the network edge

• For every path – For every packet

• Drain virtual buffer as (tn-tn-1)* Cv

• Increase count of virtual buffer• If virtual buffer overflows Drop(Mark) packets

< 1 => At Steady State total input rate is less than the network capacity => smaller steady state queue

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virtual AQM: Results

Demand Estimation

vAVQ

Drop Tail AVQ vAVQ

Average Q Size 18.69 13.62 12.22

Throughput (Mbps)

1.6 1.5 1.6

Fairness 0.067 0.03 0.06We can decouple management of bottleneck queue from AQM design

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virtual AQM: Results

Drop Tail Queue20 ms 20 ms0.8 Mbps 0.8 Mbps

8 Mbps5 ms

Demand Estimation

vAVQ

vAVQ

Drop Tail AVQ vAVQ vAVQ*

Average Q Size (First Bottleneck)

18.00 11.46 15.48 14.02

Average Q Size (Second Bottleneck)

17.97 10.86 15.72 14.66

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Conclusions

• Network based congestion avoidance and control solutions are not deployed

• De-couple congestion control task from it’s placement– Deployable architectures– Can get many beneficial properties of network based

solutions

• Randomized TCP– End-System based solution– Can reduce synchronization, phase effects, bias against

large RTT flows, burst losses– Emulate many beneficial properties of RED (AQM).

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Conclusions• Un-Cooperative Congestion Control

– Edge Based Solution– De-couple management of selfish flows from AQM design– Edge-based transformation of price can handle

misbehaving flows

– No changes in the core

– Works with packet drop or packet marking (ECN)

– Independent of buffer management algorithm

• virtual AQM– Edge-based proposal for managing bottleneck queues

– For any path using packet probes find capacity and demand

– Mark (drop) packets to match demand to path capacity

– Results depend on estimation, length of virtual buffer

– Initial Conceptual Prototype Presented

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References

• Kartikeya Chandrayana, Sthanunathan Ramakrishnan, Biplab Sikdar and Shivkumar Kalyanaraman, “On Randomizing the Sending Times in TCP and other Window Based Algorithms”, Conditional Accept for Journal of Computer Networks

• Kartikeya Chandrayana and Shivkumar Kalyanaraman, “Uncooperative Congestion Control”, ACM SIGMETRICS 2004, Also under submission to IEEE Transactions on Networking.

• Kartikeya Chandrayana and Shivkumar Kalyanaraman, “On Impact of Non-Conformant Flows on a Network of DropTail Gateways”, IEEE GLOBECOM 2003

• K. Chandrayana, Y. Xia, B. Sikdar and S. Kalyanaraman, “A Unified Approach to Network Design and Control with Non-Cooperative Users”, RPI Networks Lab Tech Reoprt, ECSE-NET-2002-1, March 2002

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Randomized TCP: Synchronization

Bandwidth TCP RenoRandomized

TCP

3 Mbps 0.4254 0.1721

4 Mbps 0.3152 0.1604

5 Mbps 0.6700 0.0799

x Mbps

4x Mbps

20 ms

5 ms

Randomized TCP reduces/removes synchronization

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virtual AQM: Improvements

Drop Tail vAVQ vAVQ*

Average Q Size 18.69 12.22 10.94

Throughput (Mbps)

1.6 1.6 1.5

Fairness 0.067 0.06 0.05

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Simple Differentiated Services

Multi-Bottleneck Setup: All flows are TCP Flows Objective: Increase the share of long flow by 10%

Differentiated Services: Map users to different utility functions Edge Based

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Users

Network

Routers

Placement

Destination

Mark Packets

MarkAcks

MarkAcks

Drop Packets

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Re-Marker Design

• Implemented it in Network Simulator• Estimation of loss rate• Estimation of throughput• Get utility function estimate• Compute the Re-Marking function• Appropriately Mark/Drop packets.

– Can also Mark Acks

• Different Algorithm for CBR flows.