Page 1

End-to-end asymmetric link capacity estimation?

Ling-Jyh Chen, Tony Sun, Guang Yang, M. Y. Sanadidi, Mario Gerla

Computer Science Department, UCLA, Los Angeles, CA 90095, USA

{cclljj,tonysun,yangg,medy,gerla}@cs.ucla.edu

Abstract. Knowledge of link capacity is important for network design,

management, and utilization. With the increasing popularity of asym-

metric link technologies (such as DSL, 1xRTT, and satellite links), it is

desirable to have a capacity estimation technique, which can simulta-

neously measure forward and backward direction link capacities on an

Internet path. Moreover, this estimation must often be “sender only”,

because of receiver limitations or lack of standards. In this study, we

propose a simple, fast and accurate technique, called AsymProbe, to es-

timate asymmetric link capacities. AsymProbe is a “sender only”, round

trip procedure. It achieves asymmetric link capacity estimation by strate-

gically altering the ratio of probe and acknowledgement packet sizes. Us-

ing simulation and testbed experiments, we validate AsymProbe with a

variety of network configurations. The results show that AsymProbe can

correctly estimate the asymmetric link capacities as long as an appropri-

ate packet size ratio can be employed.

1 Introduction

Knowledge of link capacity is particularly important for network design, manage-

ment and utilization. A simple and accurate scheme for capacity measurement

and monitoring is becoming increasingly desirable, especially for emerging tech-

nologies and applications such as overlay, peer-to-peer (P2P), sensor, grid and

mobile networks. A successful capacity estimation solution will need to encom-

pass speed of execution, simplicity, accuracy, and extendibility beyond the limits

of traditional networks, in particular the increasingly popular asymmetric access

methods to the Internet, e.g. DSL, cable modem and satellite links. It is also of-

ten imperative to carry out the estimation in a round trip, “sender only” fashion.

This is because the receiver is not powerful enough to implement the estimation

algorithm. It must, however, participate in the response to probe packets.

Several capacity estimation methods exist, including CapProbe [4], which is

sender only and fast. However, sender only methods so far have addressed sym-

metric path extimations, ie, the minimum capacity is the same in both directions.

Yet, asymmetric links do exist; moreover, many applications are intrinsically

asymmetric too and thus can benefit from the knowledge of such asymmetries.

?This material is based upon work supported by the National Science Foundation

under Grant No. CNS-0435515.

Page 2

2

For example, in multimedia streaming and file downloading, bulk data is trans-

mitted only in the forward direction, consuming much more bandwidth than the

control traffic in the reverse direction. In this case, the knowledge of one-way

capacity on the forward direction link is mandatory, as it is a much better predic-

tor of the streaming or downloading rate, than the blindly measured round-trip

bottleneck capacity, accounting for the cases when the forward link has larger

capacity than the backward link.

Previous approaches on capacity estimation can be divided into two cat-

egories: one-way probing (e.g. Pathrate [1]) and round-trip probing (e.g. Cap-

Probe [4]). In [4], a thorough comparison of modern capacity estimation methods

was presented, where CapProbe was especially singled out as a fast and accurate

capacity estimation mechanism addressing both wired and wireless links. How-

ever, limited by its round-trip nature, CapProbe only works well on symmetric

links. When operating on an asymmetric link, CapProbe measures the narrower

capacity of the two directions. It cannot distinguish the respective capacities of

the forward and backward links.

Even though capacity estimation for asymmetric links can be achieved by

conducting single direction capacity probing (e.g. Pathrate) for the two direc-

tions separately, this estimation strategy is often considered undesirable, as it

imposes unnecessary computation overhead and complexity on the receiver (eg,

the mobile host). Moreover, it requires compatible software and consistent com-

putation methods in both hosts. To simplify the process of estimating asymmet-

ric link capacities, round trip capacity probing is still the most desirable solution.

Still, existing method like CapProbe, lacked such a capacility, modifications are

needed to add support for accurate capacity estimations of asymmetric links.

To this end, in this study, we propose and evaluate a round trip technique

for estimating asymmetric link capacities called AsymProbe. AsymProbe is engi-

neered based on the well proven CapProbe mechanism. Through careful selection

of probe and acknowledgement packet sizes, AsymProbe can successfully provide

simple, fast, and accurate capacity estimates for asymmetric links.

The rest of the paper is organized as follows. In section 2, we survey and

summarize work related to this study. An in-depth description of AsymProbe

follows in Section 3. In section 4, we evaluate the accuracy of AsymProbe in esti-

mating link capacity through series of NS2 simulations. In section 5, we present

results from our testbed experiments to validate the capability of AsymProbe.

Section 6 concludes the paper.

2 Background and Related Work

Previous research on capacity estimation relied on either delay variations among

probe packets as illustrated in pathchar [3], or dispersion among probe packets

as described in Nettimer [6] and Pathrate [1]. The analysis in [1] clearly revealed

that the dispersions distribution can be multi-modal without multi-channels,

and that the strongest mode in the multimodal distribution of the dispersion

may correspond to either (1) the capacity of the path, or (2) a “compressed”

Page 3

3

Fig.1. (a) under-estimation caused by “expansion” (b) over-estimation caused by

“compression” (c) the ideal case. (T?: Measured dispersion; Tqueue: Queueing delay)

dispersion, resulting in capacity over-estimation, or (3) to the Average Dispersion

Rate (ADR), which is lower than the capacity.

Other tools such as pchar and clink [2] use variations of the same idea as

pathchar. Pchar employs regression techniques to determine the slope of the

minimum RTT versus the probing packet size. However, pathchar-like tools have

limitations with respect to the speed of estimation process as shown in [4].

CapProbe [4] is a recently proposed capacity estimation technique shown to

be both fast and accurate over a large range of scenarios. When a back-to-back

packet pair is launched into a network, it is always dispersed at the bottleneck

link according to the bottleneck capacity. If such dispersion is preserved until

the pair arrives to destination, it identifies the bottleneck capacity (as shown in

Fig. 1-c). Unfortunately, the dispersion can be either expanded or compressed,

where “expansion” of dispersion leads to under-estimation and “compression”

of dispersion leads to over-estimation of the capacity, as shown in Fig. 1-a,b.

To overcome this problem, CapProbe combines the use of dispersion and

end-to-end delay measurements thus filtering out packet pair samples distorted

by cross traffic. Whenever an incorrect value of capacity is estimated, either the

first or the second packet, or both, have been delayed by cross traffic. In this

case, the sum of the delays of the two packets in the packet pair, called the

delay sum, includes some queuing delay. A delay sum that does not include any

queuing delay introduced by cross traffic is referred to as the minimum delay

sum. The dispersion of such a packet pair sample is not distorted by cross traffic

and reflects the actual capacity. A valid sample can easily be identified since its

delay sum is the minimum among delay sums of all packet pair samples. The

capacity is then estimated by the equation:

C =P

T

(1)

Page 4

4

where P is the sampling packet size, and T is the dispersion of the sample packet

pair with the minimum delay sum.

The majority of the existing capacity estimation tools, including the ones

discussed above, are inherently round-trip based. They estimate the narrowest

capacity on the round-trip path. These techniques encounter severe constraints

when measuring link capacities of increasingly popular asymmetric links, such

as DSL, cable modem and satellite links where the forward link capacity is very

different from the backward link capacity. In this study, we propose AsymProbe,

a novel scheme that measures asymmetric link capacities in the round trip fash-

ion. Details of this proposed approach will be presented in the following sections;

evaluation of AsymProbe will be discussed in the simulation and experiments

sections.

3 Proposed Approach: AsymProbe

In this section, we present AsymProbe, a novel capacity measuring technique

that allows to measures the capacity of either the forward or backward narrow

link on the path. The basic idea of AsymProbe stems from the observation that

the measured dispersion in the original CapProbe can be introduced either in

the forward or backward direction of an asymmetric link. When probing and

acknowledgement packets are of same size, the measured dispersion is good for

estimating the round-trip bottleneck capacity, since the narrowest link along the

round-trip path gives the largest dispersion to the (probing or acknowledgement)

packet pairs. One can then easily estimate this capacity by applying Eq. 1.

Fig. 2 depicts the packet pair interactions in an asymmetric link scenario,

with link capacity C1on the forward direction link and capacity C2on the back-

ward direction link. The probe packets are sent back-to-back with packet size

P1on the forward direction link (from A to B); the acknowledgement packets

are sent immediately upon receipt of probe packets with packet size P2on the

backward direction link (from B to A). Suppose T1and T2represent the respec-

tive dispersions of probe packets and acknowledgement packets when they are

sent back-to-back on the link; from the definition of Eq. 1, T1and T2can then

be derived as T1=P1

C1and T2=P2

C2.

Fig.2. Interaction of probe packets in asymmetric link scenarios

Page 5

5

Table 1. Estimate asymmetric link capacity by varying packet sizes (ideal case without

cross traffic and any queuing delays)

Probe (P1) and ACK (P2)

Packet Size

P2

C2>

C1

P2

C2<

C1

P2

C2=

C1

P2= P1

Measured

Dispersion T?

T?→ T2

T?→ T1

T?→ T1= T2

T?→ max(T1,T2) C?

Capacity Estimation

C?

2

C?

C?

C?

1and C?

1< C1;C?

1= C1;C?

1= C1;C?

1= C?

P1

2= C2

P1

2< C2

P1

2= C2

2= min(C1,C2)

The dispersion measured at the end host A, denoted as T?, is the dispersion

between back-to-back acknowledgement packets. Suppose T1> T2, this means

that measured dispersion T?equates to T1. We assume that host B immediately

acknowledges the probe packets without incurring additional queuing delay, else

the min sum condition would be violated and the pair discarded. On the other

hand, suppose T1< T2, then T?reflects T2instead, i.e. the dispersion generated

on the backward direction link prevails. Therefore,

T?= max(T1,T2) (2)

By varying the packet size ratio between the probe and the ACK packets,

and observing the forward link capacity estimate (C?

backward link capacity estimation (C?

the correct capacity estimations for both directions of the link. For instance,

suppose C1> C2and the initial packet size P1= P2, it can be concluded that

T2> T1becauseP2

T?measured equates to T2. Therefore, C?

C1. The estimated capacity is the round-trip bottleneck link capacity on the

asymmetric link (the minimum value of C1 and C2), which is exactly is what

CapProbe estimates as presented in [4].

However, by increasing P1gradually, C?

creases and approaches C1gradually. When P1increased toP2×C1

to C1and C?

(i.e. T1> T2, since

C?

C?

asymmetric link capacities. Table 1 below details this relationship.

Based on the relationship presented in the table, the AsymProbe algorithm

consists of four phases, of which the first three phases are Probing phases, and

the last is the Decision phase. Two packet sizes are used in the probing phases:

Pmaxand Pmin, which are chosen carefully by taking network and system issues

into account. In the first probing phase, P1and P2are both set to Pmax. Thus

we estimate the bottleneck capacity, Clow, of the round trip path. In phase 2

and 3, (P1,P2) are set first to (Pmax,Pmin) and then to (Pmin,Pmax) in order to

1, which is

P1

T?) and the

2, which isP2

T?), the source node can obtain

C2>P1

C1. From the discussion above, the end-to-end dispersion

2=

P2

T? = C2 and C?

1=

P1

T? = C2 <

2remains equivalent to C2, but C?

1in-

C2

, C?

1converges

2converges to C2. Conversely, after P1increased to larger thanP2×C1

P1

C1>P2

T? < C2. This simple relationship between the estimated capacity (C?

2) and the varying packet size can be harvested for the accurate estimation of

C2

C2), T?will reflect T1. As a result, C?

1=P1

T? = C1and

2=

P2

1,

Page 6

6

Fig.3. AsymProbe Algorithm (The Decision Phase)

estimate the forward and backward link capacities respectively. We use C[i][1]

and C[i][2] to denote the estimation results of C?

respectively.

In the fourth phase, namely the Decision phase, a decision algorithm is per-

formed to determine the estimation results of both direction links from all C[i][1]

and C[i][2] as shown in Fig. 3. However, it should also be mentioned that the

capability of AsymProbe in determining the larger capacity is mathematically

bounded by the maximum ratio of packet sizes between probe and acknowledge-

ment packets, i.e. the max of

of all 3 phases are equal, and we know a priori that the link is asymmetric, then,

the packet size ratio is not sufficient large to provide an accurate capacity esti-

mation of the larger link. Therefore, AsymProbe is unable to estimate the actual

capacity in the direction with higher speed, but will indicate that such condi-

tion has occurred and report a “lower-bound” (i.e.Pmax

instead. In this case, one-way capacity estimation tools (e.g. one way version of

CapProbe or Pathrate) can be applied to accurately measure the capacity in

this direction - if this solution is feasible within the scope of the application. In

section 5.4, we discuss another extension of AsymProbe to this problem.

1and C?

2in the i-th phase,

P1

P2and

P2

P1. Specifically, if the capacity estimates

Pmin×Clow) of the capacity

4 Simulation

In this section, we present simulation results that evaluate the accuracy of ca-

pacity estimation of AsymProbe on paths with asymmetric links. AsymProbe is

implemented in the NS-2 simulator [8]. Fig. 4 depicts the simulation topology

that represents a commonly seen scenario nowadays with an asymmetric DSL

link. All links are symmetric 100Mbps Ethernet links except the one between

Page 7

7

Fig.4. Last-hop ADSL scenario. The link capacities are 100Mbps for all links, except

the asymmetric DSL link between D and E (D → E : 128Kbps;E → D : 1.5Mbps)

Table 2. Simulation results of AsymProbe in last-hop DSL scenarios (Unit: Kbps)

Cross Traffic

AsymProbe from A AsymProbe from B CapProbe

A → B

128

B → A

1500

B → A

1500

A → B

128

A ⇔ B

128 none

FTP (B → C)

FTP (C → B)

128 1500 1505 128.057128

128.0621500 1500128 128

Poisson (B → C, rate=300Kbps)

Poisson (B → C, rate=750Kbps)

Poisson (B → C, rate=1500Kbps)

Poisson (C → B, rate=25.6Kbps)

Poisson (C → B, rate=64Kbps)

Poisson (C → B, rate=128Kbps)

12815001500 128128

1281500 1500128 128

1281500 1500128128

1281500 1500128 128

128.00615001500 127.936128

127.988 15001483.143128.039 128

node D and E, which is an asymmetric DSL link with 1.5Mbps downlink capac-

ity (from E to D) and 128Kbps uplink capacity (from D to E). Nodes to the

left of node D (namely A and C) belong to a home networks, while nodes to the

right of node E are on the Internet.

The AsymProbe estimation is performed on the path between node A and B.

In addition to the AsymProbe flow, various types of cross traffic were generated

on the DSL link to test AsymProbe robustness. The cross traffic types used were

FTP and Poisson based UDP traffic of different rates. For the Internet segment,

long range dependent (LRD) traffic is created between node E and F in both

directions. The LRD traffic is composed of 16 Pareto flows with alpha = 1.9 [7],

and the overall rate of LRD traffic is 60Mbps in each direction.

The maximum and minimum AsymProbe packet sizes, Pmaxand Pmin, are

set to 1500 bytes and 100 bytes respectively. For the various cross traffic configu-

rations described in Table. 2, AsymProbe is independently initiated from both A

and B; results obtained from AsymProbe are then compared against CapProbe

as summarized in Table 2.

Page 8

8

Fig.5. Testbed for NIST Net experiments

From the results shown in Table 2, AsymProbe is able to estimate the correct

link capacity in both directions for all test cases; whereas CapProbe can only es-

timate the bottleneck link capacity of the round-trip path. Moreover, simulation

results also show that AsymProbe works when placed on either the end-client

(node A) or the Internet server (node B). The results are consistent in both

cases.

It is also worth mentioning that since the link capacity ratio of the simu-

lated scenario is 1.5Mbps/128Kbps, it is smaller than the packet size ratioPmax

AsymProbe is thus able to measure the correct link capacities. However, if we

decrease the packet size ratio (e.g. increasing Pmin in order to avoid the fine

time resolution problem as described in [5]) and obtain a packet size ratio that

is larger than the link capacity ratio, AsymProbe will only estimate the correct

capacity of the narrower link and output the other direction link as a lower

bound estimation, defined as

estimation tools can be launched.

Pmin,

Pmax

Pmin× Clow. In such case, one-way link capacity

5 Experiments

In this section, we present testbed and Internet experimental results to further

evaluate AsymProbe. We first perform a set of experiments on a “controlled”

testbed to calibrate and verify the correctness of the AsymProbe scheme and its

Linux implementation. We then move to Internet measurements for an evaluation

in the diverse and realistic scenario.

5.1Testbed Experiments

The testbed experiments are performed in the configuration shown in Fig. 5.

The NISTNet emulator [9] is used to set up the asymmetric bottleneck link of

various capacities. A backlogged file transfer session is generated from the FTP

server to the client as cross traffic. This FTP connection shares the bottleneck

link with an AsymProbe connection that traverses from host A to B.

For reasons we will discuss shortly, we choose 1500 bytes and 500 bytes as

the maximum and minimum packet sizes in this set of experiments, respectively.

Thus we have

and Table 4, in which we may see that when the forward/backward (Table 3)

Pmax

Pmin=1500

500= 3. We present the experiment results in Table 3

Page 9

9

Table 3. NIST Net results on High/Low

asymmetric links (Unit: Mbps)

Link

Capacity

AsymProbe

Estimation

CapProbe

Estimation

FBFB

11

1.063 1.0640.981

1.0641.065 0.981

1.063 1.063 0.985

21

2.010 1.0630.979

2.015 1.0620.979

2.0101.064 0.979

31

2.9971.0650.985

3.0121.065 0.983

3.0151.0550.981

41

≥3.611 1.064

≥3.609 1.061

≥3.611 1.062

0.979

0.981

0.979

F: Forward Link; B: Backward Link

Table 4. NIST Net results on Low/High

asymmetric links (Unit: Mbps)

Link

Capacity

AsymProbe

Estimation

CapProbe

Estimation

FBFB

11

1.063 1.0650.981

1.0651.064 0.981

1.0651.065 0.979

12

1.064 2.0150.979

1.0622.0100.975

1.0061.9890.981

13

1.062 2.9970.983

1.063 3.0180.985

1.0562.9970.981

14

1.059

≥3.610 0.981

≥3.610 0.979

≥3.611 0.979

1.065

1.065

F: Forward Link; B: Backward Link

or backward/forward (Table 4) capacity ratio is below 3, AsymProbe measures

both forward and backward capacities very accurately. When the ratio increases

beyond 3, only a lower bound can be obtained in the direction with the larger

capacity.

5.2 Internet Experiments

In addition to the controlled testbed experiments, we also perform a set of In-

ternet measurements to evaluate AsymProbe in a more diverse and realistic

scenario. In this set of experiments, again we have

the asymmetric links we have found, provided by DSL1and Cable2companies,

all have a higher down-link/up-link capacity ratio than 3. As presented in Table

5, AsymProbe captures the up-link capacities accurately, while only obtaining

lower bounds for the down-links.

Pmax

Pmin=1500

500= 3. However,

5.3 Discussion

From the simulation and experiment results above, AsymProbe is capable of

estimating asymmetric link capacities, as long as the capacity ratio of the forward

and backward links is within the range of the packet size ratio of the employed

probe and acknowledgement packets. In order to increase the estimation range

of AsymProbe, the packet size ratio should be as large as possible. However, this

ratio is bound by implementation.

1DSL 1 is provided by Verizon: http://www.verizon.com; DSL 2 is provided by Hinet:

http://www.hinet.net

2Cable Modem is provided by Comcast: http://www.comcast.com

Page 10

10

Table 5. Internet results on asymmetric link

Link

Claimed Capacity Estimated Capacity

DownUp# DownUp

DSL 1 1.5 Mbps 128 Kbps

1 ≥ 379 Kbps 132 Kbps

2 ≥ 382 Kbps 132 Kbps

3 ≥ 380 Kbps 132 Kbps

1 ≥ 1.49 Mbps 565 Kbps

2 ≥ 1.53 Mbps 567 Kbps

3 ≥ 1.51 Mbps 558 Kbps

1 ≥ 721 Kbps 247 Kbps

2 ≥ 730 Kbps 255 Kbps

3 ≥ 723 Kbps 248 Kbps

DSL 2 3 Mbps 512 Kbps

Cable Modem3 Mbps 256 Kbps

Specifically, the maximum size of the employed packets must be bounded

by the Maximum Transmission Unit (MTU), which is the largest size of an IP

datagram allowed to transmit on the path without fragmentation. The size of

MTU may vary greatly in different system configurations. However, practically

it is set to 1500 bytes in most networks. Packets larger than MTU will be seg-

mented into smaller fragments for transmission and then reassembled on the

receiving host. Therefore, using packets larger than MTU is not appropriate for

CapProbe-based capacity estimation techniques, since the dispersion measure-

ment no longer reflects the bottleneck capacity.

On the other hand, the minimum size of AsymProbe packets is also bounded

in accordance with the supported time resolution on the estimating host. This

is due to the fact that a packet pair with a smaller packet size will result in

a smaller inter-packet dispersion, which in turn requires a finer time resolution

to be measured accurately. Assume the capacity of the narrow link is C and

the probing packet size is P, the dispersion time (and also the clock granularity

needed for accurate estimation) that needs to be measured is T = P/C. Table

6 shows the required clock granularities that are needed for different probing

packet sizes and narrow link capacities.

Table 6. Required time resolution for accurate estimation

Packet Size

Narrow Link Capacity

1 Gbps 100 Mbps 10 Mbps 1 Mbps

100 bytes0.0008 ms 0.008 ms 0.08 ms 0.8 ms

500 bytes0.004 ms 0.04 ms0.4 ms 4 ms

1000 bytes 0.008 ms0.08 ms 0.8 ms 8 ms

1500 bytes 0.012 ms0.12 ms1.2 ms 12 ms

Page 11

11

It is clear that the time resolution of an end host relies on the hardware

speed and the operating system. A system with fast processors and I/O inter-

faces can provide a finer time resolution. Additionally, [5] also shows that the

accuracy of CapProbe-based capacity estimation is tightly related to the runtime

execution mode. With kernel mode implementations, the capacity estimation is

faster and more accurate than with user mode implementations. Kernel mode

implementations also provide better time resolutions. Therefore, kernel mode

implementations can use smaller packets for capacity estimation than the user

mode implementations.

In the presented testbed experiments, the employed packet sizes are bounded

with 1500 bytes as the maximum and 500 bytes as the minimum. The value of

1500 bytes is determined by the MTU on the path, whereas the value of 500

bytes is the minimum packet size which can measure the dispersion accurately

with the provided machine time resolution. Thus it is only capable of estimating

an asymmetric link with capacity ratio up to 1500 : 500, namely 3 : 1. For those

links with even higher “asymmetric ratios” (e.g. 1.5Mbps/128Kbps DSL links

or 400Kbps/64Kbps satellite links), it is necessary to increase the packet size

ratio by either increasing the maximum packet size or decreasing the minimum

packet size. In such cases, using a faster machine or switching from user mode

to kernel mode can help.

If all of the above procedures do not work, one can resort to one way capacity

estimation as mentioned in Section 3. This, however, requires full implementa-

tion on the receiver. If the receiver does not cooperate, a possible solution is

to use a packet “train” probing concept as suggested by other researchers [1].

The intent is to replicate the effect of a “long” probing packet without paying

the penalty of reassembly at the host. To illustrate the technique, consider for

example the situation of an asymmetric satellite link with 15Mbps downlink and

128Kpbs uplink capacity. The server in the Internet must determine the down-

link speed to deliver the proper content to a mobile user. The downlink capacity

estimation can be achieved by transmitting a train of 10 consecutive 1500 byte

packets, with a Probe leader and Probe trailer. As before, the two Probe pack-

ets each trigger a 100 byte packet probe response from the receiver. The train

dispersion in the forward link is preserved in the ACK dispersion measured by

the sender after the round trip and provides the desired estimate. Basically, this

scheme is an extension of AsymProbe, where the source experiments with trains

of increasing length until success. As pointed out in [1], the longer the train, the

less dominant the mode corresponding to the forward narrow capacity. Conse-

quently, the less frequent the train samples where no delay/interference occurred

along the path and thus the less accurate the measurements. The measurement

however, provides a conservative (lower bound) estimate of the narrow forward

capacity, which can be progressively improved as more and more samples are

collected. By the way, the “average” capacity measurement (as opposed to min

sum measurement) was shown to converge for large train length N to a value

between the narrow link and the residual link bandwidth. As expected, the lower

the utilization, the faster the min sum measurement convergence [1].

Page 12

12

6 Conclusions

In this paper, we studied asymmetric link capacity estimation and proposed an

extension of CapProbe, namely AsymProbe, to estimate asymmetric link capac-

ities. By strategically altering the ratio of probe and acknowledgement packet

sizes, AsymProbe can simultaneously measure the link capacities of both forward

and backward direction links. Through simulation and testbed experiments, we

validated the accuracy and capabilities of our proposed approach.

The unique advantage of AsymProbe is the ability to measure capacities from

the server using a round trip method that does not require the cooperation of

the receiver (which may have limited processing power or may be altogether

unaware of AsymProbe). Moreover, the technique is extremely fast, thus it is

suitable for mobile receivers that experience rapidly varying, often asymmetric

Internet connectivity. The simplicity, accuracy, and speed of AsymProbe make

it ideal in real deployments where online and timely capacity estimation is re-

quired. Better service can be provided by estimating both forward/backward

direction path capacities. Typical applications feature the efficient transfer of

multimedia files over rapidly varying Internet paths (which may include wire-

less segments). Popular examples are P2P streaming and file sharing, overlay

network structuring, and intelligent vertical handoff decision.

References

1. C. Dovrolis, P. Ramanathan, and D. Moore. What do packet dispersion techniques

measure? In Proc. of IEEE Infocom 2001.

2. A. B. Downey. Using Pathchar to Estimate Internet Link Characteristics. In Proc.

of ACM SIGCOMM 1999.

3. V. Jacobson. Pathchar: A tool to infer characteristics of Internet paths.

ftp://ftp.ee.lbl.gov/pathchar

4. R. Kapoor, L.-J. Chen, L. Lao, M. Gerla, M. Y. Sanadidi. CapProbe: A Simple and

Accurate Capacity Estimation Technique. In Proc. of ACM SIGCOMM 2004.

5. R. Kapoor, L.-J. Chen, M. Y. Sanadidi, M. Gerla. Accuracy of Link Capacity Es-

timates using Passive and Active Approaches with CapProbe. In Proc. of ISCC

2004.

6. K. Lai and M. Baker. Measuring Bandwidth. In Proc. of IEEE INFOCOM 1999.

7. M.S. Taqqu, W. Willinger, R. Sherman. Proof of a fundamental result in self-similar

traffic modeling. SIGCOMM Computer Communications Review, 27: 5-23, 1997.

8. Network Simulator (NS-2). www.mash.cs.berkeley.edu/ns/

9. NIST Net. http://snad.ncsl.nist.gov/itg/nistnet/