1

Ossama Younis and Sonia Fahmy

Department of Computer SciencesPurdue University

For slides, technical report, and implementation, please see:

http://www.cs.purdue.edu/~fahmy/

On Efficient On-line Grouping On Efficient On-line Grouping of Flows with Shared of Flows with Shared

Bottlenecks at Loaded ServersBottlenecks at Loaded Servers

2

Is “On-line” Tomography Useful and at What Time Scale?

What is “tomography”? A method of producing (inferring) an image of the internal structures of a solid object by the observation and recording of the differences in the effects on the passage of waves of energy impinging on those structures.

What is “network tomography”? Internet mapping (routes, per-segment delays, per-segment losses, per-segment bandwidth, shared bottlenecks) via composing end-to-end measurements.

3

Why FlowMate?

Source Receiver

Receiver

Receiver

Receiver

4

Why FlowMate?

• Partitioning flows emerging from the same source (busy server) according to shared bottlenecks is useful for: Customized, more fair and more responsive

coordinated congestion management. Overlay networks (e.g., application-layer

multicast and peer-to-peer applications). Load balancing. Pricing. Traffic engineering and admission control.

5

The Problem

• Input:• A set of flows (micro or macro), F,

originating at the same source, where F = {f1, f2, …, fn}

• Required:• Periodically map each flow fi (1 i n)

to a group gj (1 j m) G = {g1, g2, …, gm}, m n, where all flows f gj G share a common bottleneck

6

FlowMate Features

• Employs passive probing to reduce generation and processing overhead and network load with a large number of flows.

• Employs on-line partitioning based on constantly changing shared bottlenecks.

• Works with or without receiver timestamp support (and no router support).

• Reduces overhead using representatives.• Uses limited history for stability (no

samples).

7

Architecture

Transport layer implementation enables more accurate timestamping

8

Basic Algorithm [O(NG)]

Initialize: Empty group list and flow table.Repeat forever:- Collect delay Information.- Check triggering condition.- If (triggered): partition flows and generate lists.- Delete delay samples and maintain compact history information.Partitioning: - Select delay samples.- Assign a representative flow for each group.- Each flow is tested against each representative, and joins the group with highest correlation.- A flow either joins a group or forms a new one.

9

Shared Bottleneck Test

For two flows f1 and f2 sharing a common bottleneck in sr [Rubenstein00]:The cross correlation measure of multiplexed (f1, f2) packets, spaced apart by time t > 0, is higher than the auto correlation measure of packets of f1 or f2, spaced apart by time T > t.

s

r

10

In-Band Delay Sampling

• One way delay (reasonable clock skew OK).• Extend the time-stamped ACK (RFC 1323) to

include packet reception time.• Select samples according to inter-packet

spacing.Performance of FlowMate vs. RTT usage

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

5 15 25 35 45 55 65 75 85 95

Time (sec)

% c

orr

ectn

ess

FlowMate

Using RTT

time

Samples chosen as probes

11

Triggering Partitioning

Time

d_min d_maxtPartitioningnot invoked

Partitioning may be invoked if

enough samples for all flows

Partitioning must be invoked if not invoked since t

Last timepartitioning was

invoked

• Every flow with at least M samples is considered

12

Our Accuracy Index

• Sources of inaccuracies: false sharing and group splits

• A group split is not as harmful as false sharingLet kj denote the resulting number of splits of a

correct group:

Example: correct: {1,2,3},{4,5,6}, result: {1,2},{3,4,5},{6}, I=0.67

13

Simulation Configuration

Configuration:

• Cross and reverse traffic: CBR sources

• Forward traffic: FTP, Telnet, or HTTP/1.1

• Background traffic: 3 “StarWars” flows (self-similar traffic)

D5

D3

Source

Cross- traffic

generator

Cross- traffic destination(s)

s

D4

D9

D10

D11

D12

D2

D1

D8 D7 D6

11

10

9

8765

43

2

1

3 Mbps

1.5 Mbps

10 Mbps

12 ms

12 ms

19 ms 5 ms 9 ms

22 ms

5 ms11 ms

12 ms

5 ms

3 ms

4 ms

2 ms

2 ms 3 ms2 ms

1 ms

3 ms

4 msbottlenecks

13 ms

14 ms

17 ms

14 ms

3 ms

3 ms

14

Foreground Load

FlowMate accuracy (using a simpler topology)Different loads Staggered start times

Correlation periods: 1, 2, 4, 6, 8, 10 seconds.

15

Background Load

• Load and on/off periods have little impact on average accuracy

16

Bursty Flows

Telnet traffic HTTP/1.1 traffic

Sampling: Flow life-time (P2P FTPs (elephants), HTTP/1.0 vs. 1.1),Packet interleaving patterns, Delayed ACKs

17

Router Buffering

Buffer size vs avg index Drop policy

18

• Naïve coordinated congestion management demonstrates better fairness and responsiveness

Sample Application

19

Related Work

• Two-flow correlation tests based on delay or loss of all Poisson probe samples [Rubenstein et al., SIGMETRICS 2000].

• Semi-active Bayesian probing (using shared packet loss correlations) [Harfoush et al., ICNP 2000].

• Shannon or Renyi entropy-based flow clustering [Katabi et al., TR-2001 and IC3N01].

• Other tomography work, e.g., [AT&T, UMass, BU, Rice, Berkeley].

• Congestion Management schemes, e.g., Congestion Manager (CM) [Balakrishnan et al, SIGCOMM 99], Ensemble, Int, FastStart.

20

Conclusions

• FlowMate is an on-line flow partitioning scheme that does not require active probing. Partitioning is periodically performed at the flow origin for a large set of flows.

• FlowMate appears to be robust under heavy background load and has low overhead.

• High burstiness of flows to be partitioned is the main factor that degrades performance.

• FlowMate can be useful to many applications, such as overlay networks, congestion management, load balancing, and pricing.

21

Ongoing Work

• We are currently integrating FlowMate into Linux v2.4.17 to perform real experiments.

• We are studying various parameters, as well as UDP flow partitioning in more depth.

• More generally, we are studying the compression, composition and real-time use of inferred network properties for adaptation in overlay networks.