Edge-based Traffic Management Building Blocks

Shivkumar KalyanaramanRensselaer Polytechnic Institute

1

Edge-based Traffic Management Building Blocks

David Harrison, Yong Xia, Shiv Kalyanaraman,

Rensselaer Polytechnic Institute

[email protected]

http://www.ecse.rpi.edu/Homepages/shivkuma

I E

I

EI

E

Logical FIFO

B


2

Private Networks vs Public Networks QoS vs Congestion Control: the middle ground ?

Overlay Bandwidth Services: Key: deployment advantages A closed-loop QoS building block

Services: Better best-effort services, Assured services, Quasi-leased lines, App-level QoS…

Overview


3

Motivation: Site-to-Site VPN Over a Multi-Provider Internetwork


4

Virtual ISP: Network-level Overlay Avoid crossing ISP boundaries

Each ISP will provide good service; V-ISP can easily verify it Allocate/buy service across each ISP and compose them Network (IP)-level overlay

ISP 1

ISP 2

ISP 3

Proxy(edge)

GPoP(core)

GPoP(core)

Proxy(edge)


5

Our Model: Edge-based building blocks

New: Closed-loop control !Policy/Bandwidth Broker

I E

I

EI

E

Logical FIFO

B

Model: Inspired by diff-serv; Aim: further interior simplification


6

Closed-loop BB: Bandwidth Sharing

FIFO

B

Loops: differentiate service on an RTT-by-RTT basis using edge-based policy configuration.

B

Priority/WFQ

Scheduler: differentiates service on a packet-by-packet basis


7

Queuing Behavior: Without Closed-loop Control

End system

Bottleneckqueue


8

Queuing: With Closed Loops Bottleneck management issues consolidated at edges

Key: Transparent and lossless loop schemes

Potential: Edge-based QoS services, Edge plays in application-level QoS, active networking..


9

Closed-loop Building Block Reqts

#1. Edge-to-edge overlay operation, #2. Robust stability #3. Bounded-buffer/zero-loss,

#4. Minimal configuration/upgrades + incremental deployment

#5. Rate-based operation: for bandwidth services

Not available in any congestion control scheme… Related work: NETBLT, TCP Vegas, Mo/Walrand, ATM

Rate/Credit approaches


10

Queuing at One Router: Arrival / Service Curves

),(),(

)],(),([

)]()([)]()([

)()(),(

)()()(

)()()(

ttOttI

ttttt

tSttStAttA

tqttqttq

ttSttAttq

tStAtq

ijij

ijij

ijijijij

ijijij

ijijij

ijijij

flow i at router j arrival curve Aij(t)

& service curve Sij(t)

cumulative continuous non-decreasing

if no loss, then

time

Aij(t)

Sij(t)

queue

delaybit

t2t1

b1

b2


11

Accumulation: Series of Routers

J

j

J

jkkiji dtqta

1

1

)()(

11,)()( 1, Jjitdt jijij we have

define accumulation

which is a time-shifted, distributed sum of buffered bits of flow i at all routers 1 through J

1 j j+1 J

μij Λi,j+1

dj

fi

Λiμi

ingress egress


12

Accumulation (Contd)

12

1

1

1

11

1

1

1

1

1

1

),(),(

)],(),([

)],(),([

),(

)()(

)()(),(

J

jj

fi

if

ii

if

ii

J

j

J

jkkij

J

jkkij

J

j

J

jkkij

J

j

J

jkkij

J

j

J

jkkij

iii

ddwhere

ttOtdtI

ttttdt

ttdttdt

tdtq

dtqdttq

tattatta

then

1 j j+1 J

μij Λi,j+1

djfi

Λiμi

ingress egress


13

Accumulation vs Queuing

queue qij(t) -- num of bits of flow i queued in a fifo router j

accumulation ai(t) -- num of bits of flow i queued in a set of fifo routers 1~J

),(),(),(

)(

ttOttIttq

tq

ijijij

ij

fi

if

iii

J

j

J

jkkiji

d

ttOtdtItta

dtqta

),(),(),(

)()(1

1

the collective queuing behavior of a series of fifo routers looks similar to that of one single fifo router

is the forward direction propagation delay.


14

Accumulation: Physical Meaning

1 j j+1 J

μij Λi,j+1

dj

fi

Λiμi

… …

14time

)(1f

ii dtq )(

1

J

jkkij dtq

)(tqiJ

1 j j+1 J

jd 1Jd

),( tdtI fii

)(tai

)( ttai

),( ttOi

fid

t


15

Edge-based Control (EC) policy

1 j j+1 J

μij Λi,j+1

dj

fi

Λiμi

0)( ii ta control objective : keep if , no way to probe increase of available bw;0)( tai

ttttdtttarec

thentaif

thentaif

if

iii

iii

iii

)],(),([),(:

)(

)(

control algorithm :


16

16

EC schemes

monaco accumulation estimation: out-of-band / in-band congestion response: additive inc/additive dec (aiad), etc

vegas accumulation estimation: in-band congestion response: additive inc / additive dec (aiad)

riviera accumulation estimation: in-band congestion response: additive inc / multiplicative dec

using egress rate (aimd-er)


17

Recall: accumulation theory

J

j

J

jkkiji dtqta

1

1

)()(

… …

time

)(1f

ii dtq )(

1

J

jkkij dtq

)(tqiJ

1 j j+1 J

jd 1Jd

)(tai

)( ttai

fid


18

Accumulation vs. Monaco Estimator

1 j j+1 J

μij Λi,j+1

dj

fi

Λiμi

… …

time

)(1f

ii dtq )(

1

J

jkkij dtq

)(tqiJ

1 j j+1 J

jd 1Jd

)(taq im out-of-band

in-band ctrl pkt

),,(1

J

jkkq dtjit


19

19

Accumulation vs. Monaco estimator

1 jf Jf

μij Λi,j+1

djf

fi

Λiμi

Jb jb+1 jb 1djb ctrl

data

jf+1

out-of-bd ctrl

in-band ctrl,data pkt

classifier

ctrl

fifo


20

ec: monaco

20

congestion estimation:out-of-band and in-band control packets

congestion response: (AIAD)if qm < α, cwnd(k+1) = cwnd(k) + 1;

if qm > β, cwnd(k+1) = cwnd(k) – 1;[ 1 = α < β = 3 ]


21

ec: vegas

congestion estimation:define qv = ( cwnd / rttp – cwnd / rtt ) * rttp;

where rttp is round trip propagation delay (basertt)

congestion response:if qv < α, cwnd(k+1) = cwnd(k) + 1;

if qv > β, cwnd(k+1) = cwnd(k) – 1; [ 1 = α < β = 3 ]

Time

cwnd

slow start

congestionavoidance


22

Vegas Accumulation Estimator

22

the physical meaning of qv

rtt = rttp + rttq [ rttq is queuing time ]

qv = ( cwnd / rttp – cwnd / rtt ) * rttp

= ( cwnd / rtt ) * ( rtt – rttp )

= ( cwnd / rtt ) * rttq [ if rtt is typical ]

= sending rate * rttq [ little’s law ]

= packets backlogged [ little’s law again ]

so vegas maintains α ~ β number of packets queued inside the network

it adjusts sending rate additively to achieve this


23

23

Accumulation vs. Vegas estimator

)()(

)(

)(

)()()(

1,

1,

tadta

dtq

ddtq

rttrttttq

bi

bi

fi

J

j

J

jn

bnji

J

j

J

jm

fm

biji

bq

fqiiv

b

b

b

b

b

f

f

f

f

f

Backlogv

1 jf Jf

μij Λi,j+1

djf

fi

Λiμi

Jb jb+1 jb 1djb ack

data

jf+1


24

Vegas vs. Monaco estimators

Vegas accumulation estimatoringress-basedround trip (forward data path and backward ack path)sensitive to ack path queuing delaysensitive to round trip propagation delay measurement

error

Monaco accumulation estimatoregress-basedone way (only forward data path)insensitive to ack path queuing delayno need to explicitly know one way propagation delay


25

Riviera

25

congestion estimation:in-band techniques, similar as vegas

congestion response:

ttttdtttarec

where

kaifkk

kaifkk

if

iii

ii

iiiii

iiiii

)],(),([),(:

10,0

)()()1(

)()()1(


26

Riviera: stability and fairness

lyapunov function

26

iiiii

iiiii

kaifkk

kaifkk

)()()1(

)()()1(

Ll

lliiIi

iiiii

l

dxxcpsswU

0

),(])1(log[)(

i

dxx

xswB ii

iiiii

0

log)(

each flow i maximizes ( utility – penalty )

proportionally fair


27

Linear Network Topology

27

I0

I1

I2

E0

E1

E2

B0 B1 Bn

100Mbps

4ms

I00

E00

I10

En0U

U

U

U U

88

88

8

U U

U

U

U

send rate (Mbps)

All links are 4ms, 100 Mbps.I=ingress, E=egress, U=UDP, B=Bottleneck


28

Stability and Fairness

28


29

Utilization

29


30

Utilization w/ Reverse Path Congestion

30


31

Queue, Utilization w/ Basertt Errors

31


32

Service Differentiation: Loss-based or Accumulation-based ?

32


33

Overlay Edge-to-edge Bandwidth Services

Idea: Use the EC scheme as a closed-loop building block for a range of QoS services

Basic Services: no admission control “Better” best-effort services Denial-of-service attack isolation support Weighted proportional/priority services

Advanced services: edge-based admission control Assured service emulation “Quasi-leased-line” service

Key: no upgrades; only configuration reqts…


34

Without Overlay Scheme With Overlay Scheme

Queue distribution to the edges => can manage more efficiently

CoV vs. No of Flows

FRED at the core vs. FRED at the edges with overlay control between edges

Scalable Best-effort TCP Service


35

Scalable Best-effort TCP Service


36

Edge-based Isolation of Denial of Service/Flooding

TCP starting at 0.0s UDP flood starting at 5.0s


37

Backoff Differentiation Policy:

Backoff little (as) when below assurance (a), Backoff (as) same as best effort when above assurance (a) Backoff differentiation quicker than increase differentiation

Service could be potentially oversubscribed (like frame-relay) Unsatisfied assurances just use heavier weight.

Edge-based Assured Service Emulation

1 > AS >BE >> 0

r =r + min(r, AS aa

if no congestion

if congestion


38

Bandwidth Assurances

Flow 1 with 4 Mbps assured + 3 Mbps best effort

Flow 2 with 3 Mbps best effort


39

Assume admission control and route-pinning (MPLS LSPs). Provide bandwidth guarantee. Key: No delay or jitter guarantees!

Adaptation in O(RTT) timescales Average delay can be managed by limiting total and per-

VL allocations (managed delay) Policy:

Quasi-Leased Line (QLL)

1 > BE >> 0

r =r + if no congestion

if congestionmax(aaa


40

Quasi-Leased Line Example

Background QLL starts with rate 50Mbps

Best-effort VL quickly adapts to new rate.

Best-effort rate limit versus time

Best-effort VL starts at t=0 and fully utilizes 100 Mbps bottleneck.


41

Quasi-Leased Line Example (cont)

Bottleneck queue versus time

Starting QLL incurs backlog.

Unlike TCP, VL traffic trunks backoff without requiring loss and without bottleneck assistance.

Requires more buffers: larger max queue


42

Quasi-Leased Line (cont.)

Worst-case queue vs Fraction of capacity for QLLs

Single bottleneck analysis:

q < b

1-bB/w-delay products

For b=.5, q=1 bw-rtt

Simulated QLL w/edge-to-edge control.


43

Current Work With bottlenecks consolidated at the edge:

What diff-serv PHBs or remote scheduler functionalities can be emulated from the edge ?

What is the impact of congestion control properties and rate of convergence on attainable set of services ?

Areas: Control plane architecture for large-scale overlays Application-level QoS: edge-to-end problem Dynamic (short-term) services Congestion-sensitive pricing: congestion info at the edge

Edge-based contracting/bidding frameworks


44

Summary

Private Networks vs Public Networks QoS vs Congestion Control vs Throwing bandwidth

Edge-based Building Blocks & Overlay services: A closed-loop QoS building block: EC framework Accumulation concept Monaco, Vegas, Riviera Schemes: estimation issues Basic services, advanced services

Edge-based Traffic Management Building Blocks

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