Flow control kaist cs644 advanced topics in networking
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Flow Control KAIST CS644 Advanced Topics in Networking. Jeonghoon Mo <[email protected]> School of Engineering Information and Communications University. Acknowledgements. Part of slides is from tutorial of R. Gibbens and P. Key at SIGCOMM 2000 S. Low’s OFC presentation. Overview. Problem

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Flow control kaist cs644 advanced topics in networking

Flow Control KAIST CS644Advanced Topics in Networking

Jeonghoon Mo

<[email protected]>

School of Engineering

Information and Communications University


Acknowledgements

Acknowledgements

  • Part of slides is from

    • tutorial of R. Gibbens and P. Key at SIGCOMM 2000

    • S. Low’s OFC presentation


Overview

Overview

  • Problem

  • Objectives

  • Kelly’s Framework - Wired Data Networks

  • Extensions

    • Quality of Service

    • Wireless Network

    • High Speed Network: Aggregated Flow Control


Problem

Problem

Flows share links:

How to share the links bandwidth?


Problem1

Problem

  • How to control the network to share the bandwidth efficiently and fairly?


Link model

Link Model

  • Set of resources, J; set of routes, R

  • A route r is a subset r J.

  • Let

  • Capacity of resource j is Cj.

A’x  c

x  0


A few system objectives

A Few System Objectives

  • Max Throughput

  • Max-min Fairness (Most Common)

  • Proportional Fairness (Kelly)

  • -Fairness (Mo, Walrand)


Max system throughput

Maximize:

x(1) + x(2) + x(3)

x*= (0,6,6) maximizes the total system throughput.

However, user 1 does not get anything. => unfair

6

6

x1

x3

x2

Max System Throughput

  • Two links with capacity 6

  • Three users: 1,2,3

  • x(i) : bandwidth to user i

  • x(1)+x(2) <= 6

  • x(1)+x(3) <= 6


Max min fairness

Most commonly used definition of fairness.

Maximize Minimum of the x(i), recursively.

x*= (3,3,3) is the max-min allocation.

However, user 1 uses more resources.

6

6

x1

x3

x2

Max-Min Fairness


Proportional fairness

6

6

x1

x3

x2

Proportional Fairness

  • Proposed by Frank Kelly

  • Social Welfare: Sum of Utilities of Users

  • Maximize the Social Welfare

  • x*= (2,4,4) is the Proportional Fair Allocation.

  • Can be generalized into “Utility Fairness”.


Fairness

x(1-)

1-

Max i pi

-Fairness

  • Generalized Fairness Definition

  • System Objective:

  • includes proportional-fair, max-min-fair, max throughput

    •  = 0 : Maximum allocation (p=1)

    •   1 : Proportional fair allocation

    •  = 2 : TCP-fair allocation

    •   : Max-min fair allocation (p=1)


Fairness1

-Fairness

  • Trade-off between Fairness and Efficiency

    • Bigger  favors Fairness

    • Smaller  favors Efficiency

(source: Is Fair Allocation Inefficient, INFOCOM 04)


Fairness and efficiency infocom 04

Fairness and Efficiency (Infocom 04)

  • Counter-Example


Algorithms

Algorithms

How to achieve those system objectives?


Players

Players

  • source

    • controls its rate or window based on (implicit or explicit) network feedback

  • router (link)

    • Generate (implicit) feedback or controls packets


Source algorithm

Source Algorithm

  • TCP Vegas, RENO, ECN

  • XCP


Active queue management aqm

Active Queue Management (AQM)

  • Priority Queue

  • WFQ

  • RED

  • REM

  • XCP Router


Kelly s model and algorithm

Kelly’s Model and Algorithm


User rate and utility

User: rate and utility

  • Each route has a user: if xr is the rate on route r, then the utility to user r is Ur(xr).

  • Ur() --- increasing, strictly concave, continuously differentiable on xr [0 , ) --- elastic traffic

  • Let C=(Cj, j J), x=(xr, r  R) then Ax  C.


System problem

System problem

  • Maximize aggregate utility, subject to capacity constraints


User problem

User problem

  • User r chooses an amount to pay per unit time wr, and receives in return a flow xr = wr/r


Network problem

Network problem

  • As if the network maximizes a logarithmic utility function, but with constants (wr, rR) chosen by the users


Three optimization problems

Three optimization problems

  • SYSTEM(U,A,C)

  • USERr(Ur;r)

  • NETWORK(A,C;w)


Decomposition theorem

Decomposition theorem

  • There exist vectors  , w and x such that

    • wr = rxr for r  R

    • wr solves USERr(Ur; r)

    • x solves NETWORK(A, C; w)

      The vector x then also solves SYSTEM(U, A, C).


Flow control kaist cs644 advanced topics in networking

Thus the system problem may be solved by solving simultaneously the network and user problems


Result

Result

  • A vector x solves NETWORK(A, C; w) if and only if it is proportionally fair per unit charge


Solution of network problem

Solution of network problem

  • Strategy: design algorithms to implement proportional fairness

  • Several algorithms possible: try to mimic design choices made in existing standards


Primal algorithm

Primal algorithm


Interpretation of primal algorithm

Interpretation of primal algorithm

  • Resource j generates feedback signals at rate j(t)

  • signals sent to each user r whose route passes through resource j

  • multiplicative decrease in flow xr at rate proportional to stream of feedback signals received

  • linear increase in flow xr at rate proportional to wr


Related work

Related Work

  • Optimization Flow Control (S. Low)

  • Window based Model (Mo, Walrand)


Optimization flow control

Optimization Flow Control

  • Distributed algorithm to share network resources

  • Link algorithm: what to feed back

    • RED

  • Source algorithm: how to react

    • TCP Tahoe, TCP Reno, TCP Vegas

Source alg

Link alg


Welfare maximization

Welfare maximization

Primal problem:

  • Capacity can be less than real link capacity

  • Primal problem hard to solve & does not adapt


Model

x1

c1

c2

x2

x3

Model

  • Network:Links l each of capacity cl

  • Sources s:(L(s), Us(xs), ms, Ms)

    L(s) - links used by source s

    Us(xs) - utility if source rate = xs


Distributed solution

Distributed Solution

Dual problem:

BW price along path of s

  • Given sources can max own benefit individually

  • indeed primal optimal if is dual optimal

  • Solve dual problem!


Distributed solution cont

Distributed Solution (cont…)

  • Dual problem:

  • Grad projection alg:

  • Update rule:

  • A distributed computation system to solve the dual problem by gradient projection algorithm


Source algorithm1

Source Algorithm

Decentralized: Source s needs only and


Router link algorithm

Router (Link) Algorithm

  • Decentralized

  • Rule of supply and demand

  • Any work-conserving service discipline

  • Simple

aggregate

source rate


Random exponential marking rem

Random Exponential Marking (REM)

  • Source algorithm

    • Identical but does not communicate source rate

  • Link algorithm

    • At update time t, sets price to a fraction of buffer occupancy:

  • Theorem: Synchronous convergence

  • Under same conditions (with possibly smaller ) :

    • Price update maintains descent direction

    • Gradient estimate converge to true gradient

    • Limit point is primal-dual optimal


Flow control kaist cs644 advanced topics in networking

RED

  • Idea: early warning of congestion

  • Algorithm

    Link:Source (Reno):

marking

window

1

queue

time

B


Flow control kaist cs644 advanced topics in networking

rate

fraction

of marks

1

RED

  • Idea: marks for estimation of shadow price

  • Algorithm

    LinkSource

    Global behavior of network of REM: stochastic gradient algorithm to solve dual problem

marking

1

queue

Q


Window based model mo walrand

d1

q11

q21

w1

x1

c2

c1

x3

x2

q23

q12

d2

d3

A’x  c

Q(c - A’x) = 0

w = X(d + qA)

Window-based Model [Mo,Walrand]

Q = diag{qi }; X = diag{xi }.

xi  0, i = 1, 2, 3

qi  0, i = 1, 2,

x1+x2 c1

q1(c1 - x1 - x2) = 0

w1=x1d1 + x1 q1 + x1 q2


Window based algorithm

Window-based Algorithm

Theorem:[Mowlr98]

Let

dwi

si := wi - xi di - pi

di si

= - k

ti := end-to-end delay

dt

ti wi

Then x(t) -> unique weighted -fair point x*

Proof:

2

The function

(si /wi )

i

is a Lyapunov function


Extensions

Extensions

  • Aggregated Flow Control

  • Quality of Service

  • Wireless Network

  • Maxnet and Sumnet


Aggregate flow control

Aggregate Flow Control

  • Motivations:

    • High Capacity of Optical Fiber

  • Idea:

    • player are core routers and access routers.

      • access router: regulates the rate of aggregated flow

      • core router: provide feedbacks to access routers


Quality of service

Quality of Service

  • Only bandwidth is modeled.

  • QoS is affected by

    • loss and delay also

  • How to incorporate other parameters?


Non convex utility function lee04

Considered sigmoidal utility function

Non-convex optimization problem =>duality gap

Non-Convex Utility Function (Lee04)

(source: J. Lee et. al. Non-convexity Issues, INFOCOM 04)


Non convex utility functions

Non-Convex Utility Functions

Dual Algorithm with

Self-Regulating Property

Without Self-Regulation

With Self-Regulation

(source: J. Lee et. al. Non-convexity Issues, INFOCOM 04)


Wireless ad hoc network rad04

Wireless Ad-Hoc Network [RAD04]

  • Physical Model:Rate r is an increasing function of SINR.

  • MAC : Each time slot determines power pn,which determines rate xn

  • Routing matrix R and flow to path matrix F are given.


Random topology results

Random Topology Results

100m x100m grid

12 random node, with 6 pairs of transmissions


In the wireless ad hoc networks

In the wireless Ad-hoc Networks

  • The max-min fair rate allocation of any network has all rates equal to the worst node.

  • The capacity maximization objective leads to starving users.

  • Proportional Fair Allocation give reasonable trade-off between fairness and efficiency.

    • The worst node does not starve.


Maxnet and sumnet

MaxNet and SumNet

  • Source takes max(d1,d2,…, dN) in the maxnet architecture

  • Source takes sum(d1,d2,…,dN) in the sumnet architecture.


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