End-to-End Available Bandwidth:
Measurement Methodology, Dynamics,
and Relation with TCP Throughput
Manish Jain
Constantinos Dovrolis
SIGCOMM 2002
Presented
by
Jyothi Guntaka
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.
2
Capacity vs. Avail-bw
3
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 singlequeue paths (Delphi)
 Melander et al.: attempt to estimate capacity &
avail-bw of every link in path (TOPP)
4
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.

5
SLoPS (2)
 Periodic stream: K packets, period T, packet size L, rate:
R=L/T
6
SLoPS with Fluid Cross Traffic
 For a path P:
SLoPS Stream
SND
RCV
Cross Traffic
 One-way delay (OWD) of packet k
k
H
q
L
L
k
i
D   (  )  (  d ik )
Ci
i 1 Ci
i 1 Ci
H
k
q
where i is the queue size at link i upon k’s arrival
7
SLoPS with Fluid Cross Traffic (2)
 The OWD difference between two successive
packets k and k+1 is:
k
H

q
D k  D k 1  D k   i  d ik
i 1 Ci
i 1
H
qik  qiK 1  qik
where
 Proposition 1: if R > A, then D k  0 for
k=1,…,K-1. Else, if R < A, D k  0 for k=1,…,K1
8
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:
R min  0, R max  R0  A
 Initially:
R max  R(n), else
 If R(n) > A, then
R(n  1)  ( R max

The algorithm terminate when :
R min  R(n)
 R min ) / 2
R max  R min  
9
Check with Proposition 1
 A=74Mbps (MRTG), R=96Mbps (K=100packets,
T=100s, L=1200B)
R=96 Mbps
R = 37 Mbps
10
Refinement of SLoPS algorithm
R=82 Mbps
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
11
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

12
Detection of increasing OWD trend
 OWD of a stream, D1 , D 2 ,..., D k can be
grouped into   K groups, find median in each
group D , Pathload analyzes the set
^ k
^ k
{D , k  1,2..., }
 Two metrics to determine the trend
^
I (D D

k 2

 1

k
Pairwise Comparison Test (PCT) S PCT
 PCT: Measures the fraction of consecutive OWD pairs
that are increasing (between 0 and 1).

13
^
k 1
)
Detection of increasing OWD trend (2)


Pairwise Difference Test (PDT) S PDT 
^

^
1
D D
^
k 2| D

k
D
^
k 1
|
PDT: Quantifies how strong is the start-to-end OWD
variation, relative to the OWD absolute variations
during the stream (between –1 and 1).
14
Fleets of streams
 N streams
 idle time between streams 
 Duration of a fleet U  N (V
 Average rate of a fleet =
 )
NKL
N (V   )
 R 11
V
packets
One
Stream
V=KT
Interval 
between streams
max { RTT, 9V }
N streams in a fleet at a single iterative step
N_default = 12
15
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.
16
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.
17
Experimental Verification
 Simulation scenario:
 Path tightness factor:
Ant

1
At
18
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.
19
Simulation Results (2)
 Pathload estimates a range that includes the actual availbw in all cases, independent of the number of non-tight
links or of their load.
20
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.
21
Dynamics of Available Bandwidth
 Relative variation metrics:
R max  R min
  max
( R  R min ) / 2

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  .
22
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).
23
Effect of Stream Length K
Variability of the avail-bw decreases significantly as the
stream duration increases.
24
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.
25
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.
26
TCP and intrusiveness (2)
27
TCP and intrusiveness (3)
28
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
29
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?
30
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

31
Questions?
32