PathChirp STAB Measuring Available Bandwidth and Locating Bottlenecks in Packet Networks Vinay Ribeiro Rolf Riedi, Richard Baraniuk Rice University spin.rice.edu

pathChirp & STAB Measuring Available Bandwidth and

Locating Bottlenecks in Packet Networks

Vinay Ribeiro

Rolf Riedi, Richard Baraniuk

Rice Universityspin.rice.edu

Rice University | SPIN.rice.edu 2

Packet Networks

• Data transmitted as packets

• Routers forward packets until destination

• Routers buffer packets in queues

• Link bandwidth = maximum data transmission rate (bits/sec)

link


Network Expansion

• Grown in size and importance

• Crucial for commerce, government, research, …

ARPANET 1969 NSFNET 1993


Study Network Properties

• Properties– connectivity between routers– bandwidth used on different

links– queuing delays– statistical properties of packet

arrivals

• Improve network performance– Network design– Use bandwidth resources efficiently– Reduce delays– Assist network-aware applications


Obtaining Network Information is Hard

• Different parts of Internet owned by different organizations

• Information sharing difficult– Commerical interests/trade secrets– Privacy

• Direct measurement – Router performance affected with too much measurement– Tapping links, extra infrastructure, expensive

• Sheer volume of information– Cannot measure everything

Difficulties faced by network administrator

Difficulties faced by network user

differentorganizations


Edge-Based Probing

• Inject probe packets into network

• Infer internal properties from packet delay

End-to-end packet delay = speed of light propagation + queuing delay

probe packets


Probing “Uncertainty Principle”

• Large volume of probe packets– Accurate inference of network properties– Inefficient use of precious bandwidth resources

• Small volume of probe packets– Less accurate inference– Efficient use of resources

• Balance tradeoff in accuracy vs. efficiency


Available Bandwidth

• Link available bandwidth = unused bandwidth on a link

• Path available bandwidth = smallest available bandwidth of all links of a path

• Available bandwidth is time-varying

• Goal: end-to-end probing to estimate path available bandwidth

Link bandwidth = 100MbpsBandwidth used to transmit packets = 30MbpsLink available bandwidth = 70Mbps

70Mbps 30Mbps 50Mbps 20Mbps 60Mbps

Link available bandwidths

Example:


Applications• Server selection

• Route selection (e.g. BGP, overlay networks)

• Service verification

• Tuning transport protocols

• UDP-storm attack detection

• Early warning of meltdown


Probing Tool Requirements

• Fast, real-time estimate

• Accurate

• Efficient, introduce light probing load

• No topology assumptions (e.g. link bandwidths)• No topology assumptions (e.g. link bandwidths)


Self-Induced Congestion

• Advantages– No topology information required

• Transition point gives estimate of available bandwidth

Probing bit rate > available bandwidth delay increases (queues start filling up)

Probing bit rate < available bandwidth no delay increase (queues do not fill up)

time

time

probepackets

low probing rate

high probing rate


Chirp Packet Trains

• Exponentially decrease packet spacing within packet train

• Simultaneously probe at wide range of probing rates

• Efficient: few packets

Example: Chirp of 25 packets with =1.2 has probing range 1--100Mbps

(bits/sec)


Available Bandwidth estimation with pathChirp

• Segment delay profile into increasing/decreasing regions

• Apply principle of self-induced congestion to each region

• Average over different regions for per-chirp estimate

• Final estimate: moving-average of per-chirp estimates


Gigabit Testbed Experiment

• CAIDA/CalNGI bandwidth estimation lab

• Vary available bandwidth using cross-traffic generator

• pathChirp tracks available bandwidth well

Mbp

s

time (seconds)


Thin Links

• Thin link – link with less available bandwidth than all preceding links

• Sub-path available bandwidth A[1,m] = smallest available bandwidth among first m links

• Goal: use end-to-end probing to locate thin links in space and track changes in location over time

70Mbps 30Mbps 50Mbps 20Mbps 60Mbps

Link available bandwidths


Applications

• Science: where does congestion occur and why?

• Network aware application– Route around problem spots in Internet

• Network monitoring/troubleshooting– Locating hot spots


Estimating Sub-Path Available Bandwidth A[1,m]

• Replace each packet by two packets: Big packet size P, small packet size p

• Key: Probing rate decreases by p/(p+P) at link m

• Self-induced congestion only up to link m

• Small packets carry timing information to receiver

1 2 m


Tight Link Localization with STAB

• Thin links: links at which A[1,m] decreases

• Last thin link has least available bandwidth among all links

• Implemented in Spatio-Temporal Available Bandwidth estimator (STAB)


Simulation• STAB tracks thin links well

Actual

Estimated

Probability that different links are thin links

topology

t=360 sec

t=180 sec

Link number m

Sub

-pat

h av

aila

ble

Ban

dwid

th A

[1,m

] (M

bps)

time (sec)

time (sec)

Sub

-pat

h av

aila

ble

Ban

dwid

th A

[1,m

] (M

bps)

Link number m


Probability that different links are thin links

• Locate thin links on two paths simultaneously

• Estimated thin link locations are consistent for two paths

Internet Experiment

time tim

eLink number mLink number m

Sub

-pat

h av

aila

ble

Ban

dwid

th A

[1,m

] (M

bps)

Sub

-pat

h av

aila

ble

Ban

dwid

th A

[1,m

] (M

bps)

Router data supports STABresults

UIUCRice

UIUCRice

UWiscRice

UWiscRice


New Research Directions• Spatio-temporal network tomography

• Wireless network probing


Other Projects

• Synthesis of fractal data

• Alpha-Beta analysis of Internet data

• High-speed transport protocols


Synthesis of Fractal Data Bytes/time time series from an Internet link

Classical Models(Markov/Poisson)

Bytes per 600ms

Bytes per 60ms

Bytes per 6ms

• Internet data is fractal --- high variability if we zoom-in or zoom-out• Fast synthesis using multifractal wavelet model

– Useful for simulations– Code available at dsp.rice.edu

• People: Matthew Crouse, Rolf Riedi, R. Baraniuk


Alpha-Beta Analysis of Internet Data

• Connection -- set of all packets with a unique source and destination

• Few connections (alpha) cause most of the “spikes”

• Implications for designing simulation topologies, queuing analysis, congestion control

• People: Shriram Sarvotham, Rolf Riedi, Richard Baraniuk

= +

Time series ofbytes per 500ms

Alpha component“Spiky”

Few connections

Beta componentGaussian

Most connections


High-Speed Transport Protocols• Transport protocols – send at maximum data rate that does

not congest network

• Current protocol (TCP-Reno) cannot utilize all the bandwidth on high-speed Giga-bit networks

• Existing solutions for high-speed networks too aggressive– Negative impact on competing TCP-Reno connections– Cannot deploy such solutions

• Hybrid protocol – Utilizes bandwidth on high-speed networks– Competes fairly with TCP-Reno connections

• People: Ryan King, Rolf Riedi, Richard Baraniuk


Conclusions

• pathChirp – efficient probing tool to estimate path available bandwidth

• STAB – probing tool to locate thin links in space and track changes in location over time

• Code (UNIX) – Available for download at spin.rice.edu

• Other projects – synthesis of fractal data (dsp.rice.edu), alpha-beta analysis, high-speed transport protocols

Documents

PathChirp STAB Measuring Available Bandwidth and Locating Bottlenecks in Packet Networks Vinay Ribeiro Rolf Riedi, Richard Baraniuk Rice University spin.rice.edu