End-to-End Available Bandwidth: Measurement Methodology, Dynamics,

and Relation with TCP Throughput

Manish Jain

Constantinos Dovrolis

SIGCOMM 2002

Presented

by

Jyothi Guntaka

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Definitions

Path capacity C: Maximum possible end-to-end throughput. It is defined as C = mini=0…H {Ci}, where, Ci is capacity of link i.

Available bandwidth (termed as avail-bw): Spare capacity in the path. In other terms, maximum end-to-end throughput given cross traffic load. It is a time-varying metric, defined as average over a certain time interval.

Narrow link: The link with minimum capacity. Tight link: The link with minimum available bandwidth.

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Capacity vs. Avail-bw

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Previous work

Measure throughput of bulk TCP transfer A bulk TCP’s throughput is not avail-bw. TCP saturates path (i.e., intrusive measurements)

Carter & Crovella: dispersion of long packet trains (cprobe)

Ribeiro et al.: estimation technique for single-queue paths (Delphi)

Melander et al.: attempt to estimate capacity & avail-bw of every link in path (TOPP)

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Self-Loading Periodic Streams (SLoPS)

Basic idea: Periodic stream (probing packets) which consists of K

packets of size L at a constant rate R is sent from sender to receiver.

When R>A, the one-way delays of successive packets at the receiver show an increasing trend.

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SLoPS (2)

Periodic stream: K packets, period T, packet size L, rate: R=L/T

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SLoPS with Fluid Cross Traffic

For a path P:

One-way delay (OWD) of packet k

where is the queue size at link i upon k’s arrival

SND RCV

SLoPS Stream

Cross Traffic

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SLoPS with Fluid Cross Traffic (2)

The OWD difference between two successive packets k and k+1 is:

where Proposition 1: if R > A, then for k=1,

…,K-1. Else, if R < A, for k=1,…,K-1

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SLoPS algorithm

Iterative algorithm Sender send a periodic stream n at rate R(n) Receiver determine whether or not R(n) > A Receiver notify sender:

If R(n) > A, R(n+1) < R(n) Else, R(n+1) > R(n)

Specifically: Initially: If R(n) > A, then

The algorithm terminate when :

ARRR 0maxmin ,0

)(),( minmax nRRelsenRR 2/)()1( minmax RRnR

minmax RR

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Check with Proposition 1

A=74Mbps (MRTG), R=96Mbps (K=100packets, T=100s, L=1200B)

R=96 Mbps R = 37 Mbps

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Refinement of SLoPS algorithm

Refinement:• Watching the increasing trend during the entire stream• Accept the possibility of variation of A during a probing stream, no strict ordering between R and A which is called grey-region

R=82 Mbps

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PATHLOAD: Implementation

No timing issue: consider the variation of OWD Parameters:

a stream consists of K packets, each has size L, sent at a constant rate R, inter-spacing time T = L/R,

Stream duration V=KT

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Detection of increasing OWD trend

OWD of a stream, can be

grouped into groups, find median in each group , Pathload analyzes the set

Two metrics to determine the trend Pairwise Comparison Test (PCT) PCT: Measures the fraction of consecutive OWD pairs

that are increasing (between 0 and 1).

kDDD ,...,, 21

Kk

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PCTS

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Detection of increasing OWD trend (2)

Pairwise Difference Test (PDT)

PDT: Quantifies how strong is the start-to-end OWD variation, relative to the OWD absolute variations during the stream (between –1 and 1).

2

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||k

kk DD

DDPDTS

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Fleets of streams

N streams idle time between streams Duration of a fleet

Average rate of a fleet =

)( VNU

VRVN

NKL

11

)(

One StreamV=KT

Interval between streamsmax { RTT, 9V }

N streams in a fleet at a single iterative stepN_default = 12

packets

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Rate-adjustment algorithm

If either metrics shows an increasing trend, the stream is typed as type-I, otherwise type-N.

If a fraction f of the streams in a fleet are type-I, the fleet has a rate > A.

If a fraction f of the streams in a fleet are type-N, the fleet has a rate < A.

If less than Nf streams are type-I, and also less than Nf streams are type-N, then the fleet is in grey-region.

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Grey region

Measurement stream rate can fall into avail-bw variation range.

Pathload reports grey-region boundaries [Gmin, Gmax]. Relative width of grey-region: quantify avail-bw variability.

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Experimental Verification

Simulation scenario:

Path tightness factor: 1t

nt

A

A

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Simulation Results

Pathload produces a range that includes the average avail-bw in the path, in both light and heavy load conditions at the tight link.

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Simulation Results (2)

Pathload estimates a range that includes the actual avail-bw in all cases, independent of the number of non-tight links or of their load.

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Simulation Results (3)

Pathload succeeds in estimating a range that includes the actual avail-bw when there is only one tight link in the path, but it underestimates the avail-bw where there are multiple tight links.

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Dynamics of Available Bandwidth

Relative variation metrics:

To compare the variability of the avail-bw across different operating conditions and paths.

Each experiment has 110 runs, plot the {5,15,…,95} percentiles of .

2/)( minmax

minmax

RR

RR

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Different Load Condition

Variability of the avail-bw increases significantly as the utilization u of the tight link increases (i.e., as the avali-bw A decreases).

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Effect of Stream Length K

Variability of the avail-bw decreases significantly as the stream duration increases.

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Effect of Fleet Length

As the fleet duration increases, the variability in the measured avail-bw increases. Also, as the fleet duration increases, the variation across different pathload runs decreases.

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TCP and intrusiveness

A Bulk Transfer Capacity (BTC) connection using TCP can get more bandwidth than what was previously available in the path, grabbing part of the throughput of other TCP connections.

Pathload is not intrusive.

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TCP and intrusiveness (2)

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TCP and intrusiveness (3)

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Applications

Bandwidth-Delay-Product in TCP Overlay networks and end-system multicast Rate adaptation in streaming applications End-to-end admission control Server selection and anycasting

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Comments

Works well when there is only one tight link. Almost all parameters are empirical.

Could be difficult to tune them under different scenarios. Difficult to draw general conclusions.

Difficult to predict converge time. In their reported experiments, converge time for a single fleet of

streams is [10, 30] seconds. Not intrusive?

Only gives a single experiment. Difficult to justify. How about if lots of users are using pathloads?

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Acknowledgements

Some of the slides are taken from The presentation by Honggang Zhang

(http://gaia.cs.umass.edu/measurement/slides/avbw.ppt) http://lion.cs.uiuc.edu/seminar.ppt

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Questions?