End-to-end Bandwidth EstimationEnd-to-end Bandwidth Estimationin the Wide Internetin the Wide Internet

Daniele CrocePhD dissertation, April 16, 2010

• “Breakfast Can Wait. The Day’s First Stop Is Online.” [NYTimes‘09]

but is our connection performing well?

Internet is wonderful

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• Inter-connected networks– Different technologies, many operators– No global view

The Internet

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Net1

Net2Net3

Net4

Objective: characterize the E2E performance

• Simple metrics– Packet loss– Delay (One-Way, RTT), jitter– (TCP) throughput

• Advanced metrics– End-to-end capacity

C=min(Ci)

– End-to-end available bandwidth (AB)• i.e., the unused capacityA=min(Ai)

Performance metrics

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• On a generic link i :

Available Bandwidth

5T

TBCTA i

ii

),0(),0(

• An example:

Narrow link and tight link

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Narrow Link Tight Link

100 Mbps

90 Mbps

1000 Mbps

400 Mbps

155 Mbps

20 Mbps

Available bandwidth

C =

AB =

Capacity

• Tools require access to both end hosts– Impossible between different organizations!

Three single-ended tools

Contribution 1

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Net1

Net2Net3

Net4

• Large-scale deployments of active AB tools– Routing, P2P optimization, improve TCP

Performance evaluation of AB techniquesin large-scale measurement systems

Contribution 2

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Net1

Net2Net3

Net4

• Three AB measurement paradigms exist:– PRM (Probe Rate Model)

• “Is rate higher than the AB?”

– PGM (Probe Gap Model)• “Has the Inter-Packet Gap increased?”

– PDM (Probe Delay Model)• “Has the packet queued?”• Only analytical or simulative studies• Better than PRM or PGM?

Real implementation and comparison with other classic PRM and PGM tools

Contribution 3

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NEW!!!

SINGLE-ENDED TECHNIQUES

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Non-cooperative estimation

• RTT = OWDf

DSLAM

ACK probes

TCP RSTsSender Receiver

Can we separate the effects of the two paths?

Sender Receiver

ACKs

Sender

RSTs

+ OWDr

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Where is the tight link?

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Sender

ACKs

Sender

RSTs

• RSTs are always 40 Bytes

• No matter the size of the ACK probes

• By varying the ACK size We can load the two paths equally (SACK = SRST)

We can load the downlink more than the uplink (SACK > SRST)

We can NOT load the uplink more than the downlink (SACK < SRST)

ABw-Probe (ABP)

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• Measuring the downlink (no uplink traffic)

• Impact of “cross”-traffic on the uplink

cooperative

non-coop.

Uplink cross-traffic

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Filtering uplink cross-traffic

• Cross-traffic is not just MTU packets– Use DT to remove

large packets– Then use RR for

refining15

FAB-probe (large-scale)

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Do we really need a 40 kbps precision?

Real-world experience• Tested on 1244 ADSL hosts, 10 different ISPs

– Participating in Kademlia DHT (eMule)• Used KAD crawler (ACM IMC 2007)• Selected ADSL using Maxmind

1. Capacity of the ADSL link

2. A snapshot of the available bandwidth

3. Average AB on over 10 days– 82 hosts online for over one month– Static IP address– Measured every 5 minutes

• On average 6 seconds per measurement17

Capacity estimation• Comparison of 2 large ISPs

The policy used by Free is quite uncommon (see IMC07)

0.7Mbps

2.5Mbps0.3Mbps

1Mbps

Downlink

Uplink

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Available bandwidth (I)• Snapshot of 1244 (eMule) hosts

Hosts are divided in congested or idle

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Available bandwidth (II)• 82 hosts, 10 days average

– Each point is an average of one user over 10 days

• 30% congested, 30-40% frequently idle20

ANALYSIS OF LARGE-SCALEAB MEASUREMENT SYSTEMS

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Motivation• We have a dream: measure AB everywhere

– Route selection, server selection – Overlay performance optimization– Improve TCP– ...

• Naïve approach:– pick one of the existing techniques!

• BUT what if we all do the same simultaneously?

Interference between measurements 22

In brief• Existing techniques

– Pathload, Spruce, pathChirp• Experimental testbed

– All tools suffer from mutual interference• But not in the same way!!!

– High intrusiveness and overhead• Analytical models

– Probability of interference– Measurement bias

• What can we do?23

Pathload – Packet Trains• Probing strategy:

– Iteratively send N trains at different rates– Binary search to converge to the AB

• Inference:– Detect One-Way Delay increase (rate > AB)

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Spruce – Packet Pairs• Probing strategy:

– Two packets with specific inter-packet gap

• Inference:– Measure dispersion (gap increase) of the pair

– Accuracy is debated, out of our scope

∆in ∆in

Bottleneck

∆out

∆out

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Interference in Spruce• One pair interfering…

• What is the probability that this happens?– Hint: similar to ALOHA protocol

0

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∆in ∆out

100% error!

pathChirp – Packet “chirps”

• Probing strategy:– One train with exponentially increasing rate

• Inference:– Detect One-Way Delay increase

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ABw

Limit

Testbed results• 62 hosts running linux

– Half are senders, half receivers

• Single bottleneck (10 Mbps), CBR traffic– Ideal conditions for ABw tools– Errors are due to mutual interference only

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Pathload

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Spruce

True?? How much OVERHEAD?

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Results are biased

pathChirpResults seem better

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True?? High OVERHEAD!

Intrusiveness

x10 x100

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Possible Solutions• Mutual interference

– Direct probing more promising• Simple, Spruce-like algorithms. No binary search

– Identify interference (and correct it)??

• Overhead– “In-band” measurements (piggy-backing)

• Best, no overhead at all• Complex! (SIGCOMM09) + delay constraints

– “Out-of-band” measurements• At least, make the overhead scale with the ABw!

• Lets help each other! Network Tomography33

Conclusions• Non-cooperative estimation

– Three highly optimized tools

– No need to install software or buy new equipment• An Italian ISP already interested!

• Analysis of large-scale AB measurements– Tools can not be used off-the-shelf

• Mutual interference, Intrusiveness, Overhead

– Interference can be predicted and modeled

– Discussed possible solutions

• Future work includes– Technologies different from ADSL (cable, FTTH)

– New, lightweight techniques (passive?), tomography 34

BACKUP

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Collision with ON-OFF meas.

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Few hosts cause > 10%

collisions!

Non-cooperative estimation

• Who is answering to what (Monarch, IMC’06)

Measurement bias: Spruce

• Measurement error in Spruce– Depends on the # of interfering pairs n :

• The average number of interf. pairs is

• This explains why Spruce bias is proportional to

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Pathload interference, two trains

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PathChirp, two chirps

With only two trains, errors up to

80%!!!

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Measurement Overhead• Spruce

– Overhead = min(240kbps, 5% of Bneck Capacity)– Few hosts can consume a LOT of Bw!

• Pathload– Overhead ≈ ABw– Cons: measurements consume all the ABw– Pro: overhead “scales” with the ABw

• pathChirp– Overhead = 300kbps (tunable parameter)– What if 10 hosts are measuring? If 100?

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With traffic load• 20 hosts running, ABw=6 Mbps

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All tools together• 9 hosts per type (27 senders)

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Delay-based tools• Consider a single server queue

– The utilization can be computed as

• 0 is the probability of the queue being empty

– Probe-Delay-Model (PDM) tools estimate 0

• PDM tools– Make no assumptions on cross-traffic– Inject very little overhead

• no need for high probing rates44

Forecaster Model

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The AB is estimated by “projecting” the

utilization

Threshold problem

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-100 0 100 200 300 400 500 600 700 800 9000

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

One Way Delay(usec)

CD

F

CDF of OWD with 20% cross traffic

delay in this area is considered not suffered from queueing

Time Threshold

In our experiments, must allow ~100us for inaccuracies!