www3.informatik.uni-wuerzburg.de

Institute of Computer ScienceDepartment of Distributed Systems

Prof. Dr.-Ing. P. Tran-Gia

Performance Metrics for Resilient Networks

Michael Menth, Jens Milbrandt, Rüdiger Martin, Frank Lehrieder, Florian Höhn

This work was in cooperation with Infosim GmbH & CoKG and supported by the Bavarian Ministry of Economic Affairs

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Outline

Motivation

Unavailability of the network for end-to-end (e2e) aggregates

Calculation

Illustration of results

Overload probability for links

Calculation

Illustration of results

Summary & outlook

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Motivation

Availability of the network Link failures Node failures

Link overload Redirected traffic due to

failures More traffic due to

increased user activity (hot spots)

More traffic due to interdomain rerouting

Tool for the assessment of network resilience Network availability Overload probability

Why is it useful? Early discovery of risks Support of intentional

overprovisioning Evaluation of potential

upgrade strategies– New routing– More bandwidth, new

links or nodes– New customers or SLAs

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Key Ideas

Network elements can fail Failure probability Independent failures Correlated failures modelled by virtual element

Traffic matrices can vary Example: additional interdomain traffic, hot spots Traffic matrix probability Independent of network failures

Definition: scenario = set of network failures and traffic matrix Scenarios determine unavailability / overload Derive scenario probability Take all scenarios for the analysis with probability larger than

pmin

Definition: set of considered scenarios S

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Calculation of Network (Un)Availability

Problem: multiple failures can compromise connectivity

Loss of connectivity for e2e aggregate between node v and w in special scenario s? Disconnected(v,w,s) {0, 1} Analysis of routing in scenario s

Conditional probability for loss of connectivity

Estimate for unavailability: not all possible scenarios respected in S

Upper and lower bounds available

),,(1

, swvedDisconnectspSp

wvpSs

Sdis

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European Nobel Test Network

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Network Unavailability for Madrid‘s Aggregates

of Madrid‘s Aggregates

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Average Network Unavailability for Routers

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Network Unavailability for Overall Traffic

C

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Calculation of „Link Overload“

Problem: redirected and extra traffic leads to overload

Link utilization ρ(l,s) of link l in special scenario s? Analysis of routing and traffic matrix in special scenario s

Probability to have utilization U(l) larger than x on link l Complementary cumulative distribution function (CCDF) Calculate ρ(l,s) for all considered scenarios sS Sum all probabilities p(s) of scenario with ρ(l,s)>x

Comments Intelligent data structures and efficient algorithms required Only estimate, but upper and lower bounds available

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Impact of Probability Limit pmax for Failure Scenarios

pmin=10-6 pmin=10-8

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Which Link is Most at Risk?

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Link Rankings

Utilization threshold uc

Utilization percentile q

Appropriate weighted integral based on utilization distribution

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Graphical Presentation

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Summary & Conclusion

Tool for assessment of network resilience Network availability „Overload“ probability Useful for planning and operation of networks

Achievements Fast algorithms (Java) Visualization of

– Unavailabilty– „Overload“

Outlook: interdomain resilience