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INTERMONAdvanced architecture for INTER-domain quality of service

MONitoring, modelling and visualisation

http://www.ist-intermon.org

U. Hofmann*, I.Miloucheva, T.Pfeiffenberger, F. StrohmeierSalzburg Research,*Univ. of Applied Sciences, Salzburg Austria

{ulrich.hofmann,ilka.miloucheva,thomas.pfeiffenberger,felix.strohmeier}

@salzburgresearch.at

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1. INTERMON Overview

2. CM Toolset active QoS monitoring and analysis

toolkit

- Motivation

- Components

- Scenario

- Results

3. Future Work

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Motivation : ISP wants to enhance the inter-domain Quality of Service (QoS) analysis in large-scale, multi-domain to offer stabile inter-domain services

D iffe re n tS o u rc e A S

IS P S 1

S o u rc ee n dsy ste m

T ra n s it A S

P e e rin gp o ss ib il ity

IS P T 2 D e st in a tio ne n dsy ste m

IS P T 1

IS P T 3IS P S 2

IS P D 1

IS P D 2

IS P D 3

IS P D 4

D iffe re n tD e s tin a tio n A S

IN T E R M O N to o lk itw ith in t e g ra t e d d a t ab a se

T ra ff ic s tu d y a tb o rd e r ro u te r A lt e r n a t iv e

to p o lo g ie s

E n d - to -e n dQ o S

E n d -to -e n dQ o S

• ISP S1/2 are competitors

• ISP* offers SourceEndSystem

• the aggregate QoS

• best_of {ISP T1/T2}

•...ISP D*

1. Overview

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=> scalable inter-domain QoS monitoring and analysis

effect of inter-domain routing and BGP-4 protocol behaviour

monitoring : IPFIX

modelling load <=> QoS : simulation ( fluid, hybrid

analytical M/G/*

visual data mining architecture

measurement based simulation technologies IPFIX => Simulation

pattern detection, outlier elimination

pattern compression PLA

using

common QoS database with

policy-controlled interworking of components

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Q oS measurement and modelling

„w hat if“analys is

A nalys is o fT raffic &T opo logyImpact onE nd-to -end Q oS

S im ulationtoo lk it-flu id-tim e series-hybrid-analyt ica l

Q oS Patternanalyser-outliers- linearapproxim at ion

IN T E RM O N D B:- QoS param. M eas.(delay, lo ss),-SN M P data per router-IPFIX traffic-BG P-4 protoco l data-T raceroute topo logy

BG P-4Protoco lA nalyser- H eurist icsfo r BG P-4Patterns

Act ive Q oSm easurem entand traffic flowemulat ion(CM T oo lset)

T rafficflow s(IPFIX )

T rafficm atrix

M RCollecto r

Act ivetopo logy(traceroute)A nalys is o fCM T oo lset

Passivedelaym easurem ent too l

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QoS is different for different paths : s->A->B->d , s->A->C->d

QoS is different for different SLS : VoIP (160 Byte), FTP (1500 Byte)

ISP_A

ISP_C

ISP_B

source destination

QoS offer S->D

Salzburg-Madrid

t=20ms

2. CM Toolset active QoS monitoring and analysis toolkit

Motivation

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IP Network

Measurement Flows(TCP/UDP)

MeasurementClient Station

MeasurementManagement Station

GUI(WWW-Browser)

WebServer

CMCaller

CMDaemon

GPSEquipment

MDB

MeasurementClient Station

CMDaemon

GPSEquipment

Components

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CM Toolset Scenario for complex QoS analysis and data mining

-capacity planning-patterns dependent on topology change-application emulation and QoS study - VoIP

Spatio temporal QoS Analyser -Pattern detection (generic patterns for data mining rules)-Outlier-Similarity detection

Spatio temporal QoS Analyser -Pattern detection (generic patterns for data mining rules)-Outlier-Similarity detection

QoS measurement for secified scenario

QoS measurement for secified scenario QoS monitoring

data base

Pattern data base

detect router anomaly

• define pattern owd>2s & singular

• specify measurements rate, p_size, duration, source, destination,..

• measurements

• analysis if(owd>2s & singular) then anomaly else path_change

Scenario:

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Results (1): Active Topology discovery using traceroute data base

Objective: study of topology properties of the connection number hops, availability of the routers long term reporting : per hour, day BGP issues ( security,...)

Separation of measurement results per path

Salzburg-Madridred: router not respondingbrightness: RTTblue: some RTT > e2e_RTT

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Results (2) Piecewise linear approximation

• data as sequences of straight lines,

• optimise the tradeoff between

• high level of aggregation of measurement results• „pieces“: set of consecutive measurement points

with „similar“ gradient d_measurement(t)/dt• plain : gradient =0

• increasing : gradient >0

• decreasing: gradient <0

• „similarity“ : tolerance parameter d

•Pattern Description Language (PDL)

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Salzburg-Sao Paulo: router anomaly ?

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• improved outlier analysis e.g. prediction optimised (ARIMA)

• symbolic representation for pattern detection {R} -> {a,b,c,..}

• evaluation tradeoff : information loss, compression

• InterDomain Forum Cluster

• audio QoS pattern analysis ( ITU E.855, perceptual QoS )

• prototype for global controller ( GEANT ?)

• MOME cluster

• anomaly detection for network security

• monitoring and modelling for network planning

3. Future Work