Available Bandwidth Estimation Manish Jain Networking and Telecom Group CoC, Georgia Tech

Available Bandwidth Estimation

Manish JainNetworking and Telecom Group

CoC, Georgia Tech

8803 Class Presentation 2 09/23/2003

Outline

Introduction and definitions Estimation methodologies

Train of Packet Pairs(TOPP)Self Loading Periodic Streams (SLoPS)Packet Train Gap Model

Open Issues


Varies with time ui : utilization of link i in time interval ( 0 <= ui <=

1 ) Available bandwidth in link i:

Available bandwidth in path (Avail-bw):

Tight link: minimum avail-bw link

Definition Available Bandwidth: unutilized capacity

)u-(1C A iii

)u-(1 C min A min A ii0..Hi

i0..Hi


Available Bandwidth:time varying metric

defines sampling/averaging timescale Average avail-bw in

Does not tell how avail-bw varies Variation range gives more information

t

A(t)

T


Why do we care ?

ssthresh in TCP Streaming applications SLA verification Overlay routing End-to-end admission control


Measuring per-hop available bandwidth

Can be measured at each link from interface utilization data using SNMP

MRTG graphs: 5-minute averages

But users do not normally have access to SNMP data And MRTG graphs give only per-hop avail-bandwidth


Measuring path Available Bandwidth

Blast path with UDP packets Intrusive Carter & Crovella: cprobe (Infocom 1996)

Packet train dispersion does not measure available bandwidth (Dovrolis et.al. Infocom’01)

Measure throughput of large TCP transfer TCP throughput depends on network buffer

Ribeiro et.al. : Delphi (ITC’00) Correct estimation when queuing occurs only at single

link Assumes that cross traffic can be modelled by MWM

model

8

A New End-to-end probing and analysis method for estimating bandwidth

bottlenecks

B. Melander et al, In Global Internet Symposium, 2000


Introduction

& C*

&

&

111j

11

1

1111

1111**

1

11

1

11

11

jjjjjj

jj

j

jjjjjjj

jjjjjjjj

j

jj

j

jj

jj

mComCoifmo

omComCoifo

mCo-mCoifCCm

mo

omo

oo

In one hop:

In two hop:

CjCj+1

Oj-1 Oj

Mj-1

Oj+1

Mj

11

1*11

1

jjjj

jjjjjj

jj

mCoifo

-mCoifCmooo

In FCFS queue, output rate is function of input rate and cross-traffic rate

Cj+1-Mj > Cj-Mj-1


Key Idea:TOPP

o :sending rate f: receiving rate

where i is number links with different available bandwidth

For i=1 =1/Ctight

1=1-Atight/Ctight

of

oii

Break points


Algorithm Algorithm:

Send n probe pairs with a minimum rate Record receive rate at receiver Increment rate by fixed and repeat Measure available bandwidth from the relation of o/f

vs o

Avail-bw and capacity of other links can be measured if links in ascending order of avail-bw

In practice, break points may be hard to identify

12

End-to-end Available Bandwidth: Measurement

Methodology, Dynamics and Relation with TCP Throughput

M. Jain and C. Dovrolis, In IEEE/ACM TON, August 2003


Key idea: SLoPS

Examine One-Way Delay (OWD) variations of a fixed rate stream Relate rate to avail-bw

OWD: Di = Tarrive-T = Tarrive - Tsend + Clock_Offset(S,R)

SLoPS uses relative OWDs, Di = Di+1 – Di-1 (independent of clock offset)

With a stationary & fluid model for the cross traffic, and FIFO queues:

If R > min Ai, then Di > 0 for I = 1…N Else Di = 0 for for I = 1…N

sendS

R RR


Illustration of SLoPS Periodic Stream: K packets, size L bytes, rate R = L/T

If R>A, OWDs gradually increase due to self-loading of stream


Trend in real data

For some rate R Increasing trend in OWDs R > Avail-bw No trend in OWDs R < Avail-bw


Iterative algorithm in SLoPS

At sender: Send periodic stream n with rate Rn At receiver: Measure OWDs Di for i=1…K At receiver: Notify sender of trend in OWDs At sender: If trend is :-

increasing (i.e. Rn >A ) repeat with Rn+1 < Rn non-increasing (i.e. Rn <A ) repeat with Rn+1>Rn

Selection of Rn+1 : Rate adjustment algorithm

Terminate if Rn+1 – Rn < : resolution of final estimate


If things were black and white…

Grey region: Rate R not clearly greater or smaller than Avail-bw during the duration of stream Rate R is within variation range of avail-bw


Big Picture

Increasing trend R > variation range of Avail-bw

No trend R < variation range of Avail-bw Grey trend R inside variation range


Grey region

Rate adjustment algorithmIncreasing trend :

Rmax = R(n)R(n+1) = (Gmax + Rmax)/2

Non-increasing trend:Rmin = R(n)R(n+1) = (Gmax +Rmin)/2

Grey region & R(n) > Gmax:

Gmax = R(n) R(n+1) = (Gmax + Rmax )/2

Grey region & R(n) < Gmin:

Gmin = R(n)R(n+1) = (Gmin + Rmin )/2

Terminate if:(Rmax – Gmax) && (Rmin– Gmin) <

Rmax > A

Rmin < A

Gmax

Gmin

Vari

ati

on R

ange


How do we detect an increasing trend?

Infer increasing trend when PCT or PDT trend 1.0

1

)( 12

K

DDIR

jj

pct

K

j

||

)(

1

1

2

2

jj

jj

pdt

DD

DDR

K

j

K

j


Verification approach

2300

minmax

1

iiW

i

RRqR

i

Simulation Multi-hop topology Cross traffic: Exponential and Pareto interarrivals Varying load conditions

Experiment Paths from U-Delaware to Greek universities and U-Oregon MRTG graphs for most heavily used links in path Compare pathload measurements with avail-bw from

MRTG graph of tight link In 5-min interval, pathload runs W times, each for qi secs

5-min average avail-bw R reported by pathload:


Verification: Simulation Effect of tight link load

Pathload range versus avail-bw during simulation (average of 50 runs)

5 Hop, Ctight=10Mbps, utilnon-tight=.6 %

Center of pathload range: good estimate of average of avail-bw


Verification: Experiment Tight link: U-Ioannina to AUTH (C=8.2Mbps), =1Mbps


Avail-bw Variability versus stream length

Relative variation index:

Longer probing stream observe lower variability However, longer streams can be more intrusive


Avail-bw variability versus traffic load

Heavier link utilization leads to higher avail-bw variability

26

Evaluation and Characterization of Available Bandwidth

Techniques

N. Hu et al, JSAC, August 2003


Packet Pair Model: Single Hop

Assumption: Fluid cross traffic In practice, CT is bursty

Packet train will capture average

Input

Case1: Go = Gi – q/C < Gi

Case2: Go=m/C+Gb

Gi

Go

Go

q

m/Ct

t

t

In single hop path Competing traffic may be inserted between packet pair Packet pair gap at receiver is function of cross traffic


Packet Train Model: Single Hop

Assumption: Only increased gap sees CT Packet dispersion not affected by CT at post-tight link

N

ii

K

ii

M

ii

M

iBi

ggg

ggC

111

1

)(*

Where Total numer of probing packets = M+K+N

Gi

Gb Gi+

t

t


IGI and PTR Algorithm

Start by sending out packet train with minimum gap ( gB)

If gap@receiver != gap@senderSend another train with increased gap

Else calculate available bandwidth IGI: Use equationPTR: Available Bandwidth = Rate of last

train measured at receiver


Summary: Single Hop Model

IGI: Need to know the capacity of tight link Assume that tight link is same as narrow link

PTR: Same as TOPP

Relation of amount of cross-traffic and dispersion May not hold in multi-hop path


Open Issues Integrate avail-bw estimation methodology with

application Use data packets in place of probe packets

Implement avail-bw estimation algorithm in network interface card

Allow routers to do avail-bw estimation Can we make some short-term predictions of

avail-bw? High bandwidth paths

Time stamping packets MTU limitations


Pathchirp Uses exponentially spaced packet train Main idea:

Avail-bw > Rk , if qk >= qk+1

Avail-bw < Rk , otherwise Can be used when probe packets are close enough

Identify excursions: consecutive packets show increased queuing delays

Per-packet avail-bw Ek Final estimate: Expected value of Rk

Documents

Available Bandwidth Estimation Manish Jain Networking and Telecom Group CoC, Georgia Tech