Bandwidth estimation tools

Bandwidth estimation tools

Ravi S. PrasadPh.D. Student

Networking and Telecommunications GroupCollege of Computing

Georgia Tech.

Applications of bandwidth estimationOverlay networks

Optimized routingMultimedia streaming

Adjust encoding rateSLA and QoS verification

Monitor available bandwidth in pathContent distribution networks

Select best serverCongestion control and TCP

Infer Bandwidth-Delay-Product End-to-end admission control

Check for sufficient bandwidth

Outline

Capacity estimationTool: Pathrate

Mean available bandwdith (avail-bw) estimation

Access at both endsTool: Pathload

Single end accessTool: Abget

Avail-bw variability estimationTool: Pathvar

Capacity estimation

Tool: Pathrate

Capacity definition

Maximum possible end-to-end throughput at IP layerIn the absence of any cross trafficAchievable with maximum-sized packets

If Ci is capacity of link i, end-to-end capacity C of H hop path defined as:

Capacity determined by narrow link

iHiCC

,...,1min=

=

Packet pair dispersionPacket Pair technique

Originally, due to Jacobson & Keshav

Send two equal-sized packets back-to-back

Packet size: LPacket trx time at link i: L/Ci

Packet pair dispersion: time interval between last bit of two packetsWithout any cross traffic, the dispersion at receiver is determined by narrow link:

⎟⎟⎠

⎞⎜⎜⎝

⎛Δ=Δ

iinout C

L,max

CL

CL

iHiR =⎟⎟

⎠

⎞⎜⎜⎝

⎛=Δ

= ,...,1max

PathrateUses a combination of packet pairs and packet trains to estimate path capacityCan be obtained from www.pathrate.orgHas been tested for up to gigabit/sec speed

At high speed systems related issues become importantE.g., we identified effects of Interrupt Coalescence (IC) and use heuristics to detect IC and Find capacity in presence of IC

Can be run in Quick estimation modeQuick mode takes < 1 minuteNormal mode may take 15-30minutes

Mean Avail-bw Estimation

Tools: Pathload

Available bandwidth definitionPer-hop average avail-bw:

Ai = Ci (1-ui)ui: average utilizationA.k.a. residual capacity

End-to-end avg avail-bw A:

Determined by tight linkISPs measure per-hop avail-bw passively (router counters, MRTG graphs)

iHiAA

,...,1min=

=

Pathload’s iterative algorithmUses self-loading periodic streams (SLoPS) schemeSource: send nth stream with rate R(n)Receiver:

Measure OWDs of the streamCheck for presence of increasing trend Notify source

Source:If “increasing OWDs”, i.e., R(n)>A,

Rmax = R(n)Else, R(n) < =A,

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

Exit when Rmax - Rmin < ω

PathloadAvailable at www.pathrate.orgTakes about 10-20 seconds per measurementAccurate within 10% as long as path does not include multiple bottlenecksTested up to Gigabit/sec speed

Even in presence of Interrupt Coalescence

Gigabit path

0 200 400 600 800 1000Cross-traffic utilization (Mbps)

0

200

400

600

800

1000

Est

imat

ed b

andw

idth

(M

bps)

PathratePathloadCapacityAvail bw

Replay traces with scaled interarrivals

Accuracy comparison

Actual Available Bandwidth

Measured Available Bandwidth

An independent study in testbed by Shriram et al., PAM 2005

Mean Avail-bw Estimation

Tools: Abget

Abget

abget runs in single-end modeIt uses any TCP-based server as sender

Needs to know the name of a downloadable file at the serverManipulates ack number and spacing to get desired data rate from server

Limit the advertised window to one MSS sizeAcknowledge one MSS with each ACK andGenerate paced ‘fake’ ACKs with period T=MSS/R

Uses RTT for trend detectionIterative algorithm is same as Pathload

0

5

10

15

20

25

30

35

11:20 11:30 11:40 11:50 12:00 12:10 12:20

Ava

ilabl

e B

andw

idth

(Mbp

s)

Local Time - Greece

tight link capacityabget estimation

actual avail-bw

0

5

10

15

20

25

30

35

18:30 18:40 18:50 19:00 19:10 19:20 19:30A

vaila

ble

Ban

dwid

th (M

bps)

Local Time - Greece

tight link capacityabget estimation

actual avail-bw

avail-bw from www.nytimes.com to UoC client avail-bw from UoC web server to Gatech client

Validation

We passively measured the actual avail-bw variation at the tight linkabget central estimates are, for the most part, within the actual variation

Avail-bw Variation Range Estimation

Tool: Pathvar

Avail-bw variabilityPrevious estimation techniques focused on average avail-bwHowever, avail-bw has significant variabilityVariability depends on averaging timescale τ

Larger timescale, lower variance

Variation range:Range between, say, 10th to 90th percentiles

Pathvar:Estimates any given percentile at user-specified timescale τ

0 20 40 60 80 100Time (sec)0

20

40

60

80

100

Avail

-bw

(M

bps)

τ = 20 msecτ = 1 sec

0 20 40 60 80 100Avail-bw (Mbps)

0

0.2

0.4

0.6

0.8

1

CD

F

τ = 20 msecτ = 1 sec

PathvarCan be used for continuous estimate of variation range of avail-bw

For user specified percentiles and timescale

Path from Georgia Tech to University of Ioannina, GreeceAverage avail-bw increases over 2 hour period

Variation range decreases as the average avail-bw increases

SummaryWe are

Part of Networking Group at Georgia Tech.Focus on network measurementsHeaded by Prof. Constantine DovrolisWith collaborator in Univ. of Crete, Greece

Our toolsPathrate: Capacity estimationPathload: Available Bandwidth EstimationAbget: Source-only available bandwidth estimationPathvar: Variation range of available bandwidth

Please use them and let us know how they work!

Thank you!