www3.informatik.uni-wuerzburg.de Institute of Computer Science Department 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

2 Click to edit author name 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

3 Click to edit author name 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

4 Click to edit author name 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 p min  Definition: set of considered scenarios S

5 Click to edit author name 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

6 Click to edit author name European Nobel Test Network

7 Click to edit author name Network Unavailability for Madrid‘s Aggregates of Madrid‘s Aggregates

8 Click to edit author name Average Network Unavailability for Routers

9 Click to edit author name Network Unavailability for Overall Traffic C

10 Click to edit author name 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 s  S  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

11 Click to edit author name Impact of Probability Limit p max for Failure Scenarios p min =10 -6 p min =10 -8

12 Click to edit author name Which Link is Most at Risk?

13 Click to edit author name Link Rankings  Utilization threshold u c  Utilization percentile q  Appropriate weighted integral based on utilization distribution

14 Click to edit author name Graphical Presentation

15 Click to edit author name 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