1 Measurement based traffic engineering Poul Heegaard, Telenor R&D / NTNU Dept. Telematics

2 Open architecturePerformance guaranteeService differentiation Measurements Performance optimisationResource allocation Test labsProduction networks Gilb’s Law: “Anything can be measured in a way that is superior to not measuring it at all.”

3 Open architecture Performance guarantee Service differentiations Measurements Test labsProduction networks Resource allocationPerformance optimisation Foster rich set applications Worldwide commercial interests New actors protect investments New cost models QoS guarantees New QoS requirements New applications, New markets New QoS requirements

4 Performance optimisation Resource control Open architecturePerformance guaranteeService differentiations Measurements Test labs Production networks (core/AS, access/SPE/CPE) Controllable (Protect, Priority, Guarantee) Proactive (planning, designing) Reactive (monitoring, reconfig)

5 Measurements - domain (inter, intra, access, private) - level (physical, network, transport, application) - approach (active, passive) Open architecture (service innovation, fairness) Performance guaranteeService differentiation Performance optimisationResource allocation Test labsProduction network – SLA fulfilled? – Mechanisms effective? – Effect of new applications? – New applications appeared? – Performance bottlenecks? – Connectivity? – Routing stability? – “Clever” users? – “Malicious” users? – Charging?

6 Open architecturePerformance guaranteeService differentiations Measurements Performance optimisationResource allocation Test labs Production networks - redesign, configuration - connectivity - performance assurance - traffic trends - security - input to traffic modelling Essential: multipurpose measurement architecture

7 Multiple measurement objectives Measurement Application IP Network QoS & Performance management MEASUREMENT FUNCTIONS Prognosis Trends Traffic matrix Security Fraud Dependability Reliability Overload (observation and control) AccountingSLA validation Planning / Long term operation Medium / Short term operation Multipurpose measurement probe

8 Many options ADSL 1- Peering network Our own network Service provider POP DoS attack? traffic volume? DoS attack? Traffic volume (per customer)? service usage? End-to-end delay, delay variation (“jitter”), packet loss ratio Asymmetric traffic? DoS attack? traffic trends? resource (e.g. link) utilisation? Volume per customer? DoS attack? Paradigm shift in networking: same platform for all services Should also apply to monitoring: same platform for all measurement needs less than 5% overhead

9 Open architecturePerformance guaranteeService differentiations Measurements Performance optimisationResource allocation Test labs Production networks - coarse grained data collection - imprecise active tests - performance demanding - excessive measurement data - increasing measurement needs - measurements by 3 rd party - measurement architecture hard Essential: configurable, precise, up-to-date, available data

10 Ex: Delay estimation two-way by single point (e.g. in tcp, rtp flows) one-way by dual point (e.g. inexpensive probes) upper bounds on network layer delay protocol effects (e.g. delayed ack) congestion no congestion => perfect match compare single and dual point estimations

11 Ex: flow-sampling Data reduction by sampling

12 Ex: corrected active tests customer view provider view Measure quality any time Measure quality when service is in use

13 Test labs Open architecturePerformance guaranteeService differentiations Measurements Performance optimisationResource allocation Production networks - equipment - mechanisms (e.g. QoS) - configurations - applications - user behaviour Essential: realistic traffic generator => e.g. GenSyn

14 GenSyn - objectives New network mechanisms Controllable Scalable Re-producible Realistic traffic User behaviour model Internet protocols User behaviour model Internet protocols New services

15 GenSyn – in short Java-based, portable traffic generator Flexible and scalable Stochastic state models of user behaviour Link to protocol stack for real packet generation

16 Conflicting and interdependent interests Gordian Knot simplisity openess resource control QoS guarantee differentiation security monitoring segmentation protection revenue real-time

17 The solution?