performance metrics for resilient networks
DESCRIPTION
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. Outline. Motivation - PowerPoint PPT PresentationTRANSCRIPT
![Page 1: Performance Metrics for Resilient Networks](https://reader036.vdocument.in/reader036/viewer/2022062322/56814eab550346895dbc5625/html5/thumbnails/1.jpg)
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
![Page 2: Performance Metrics for Resilient Networks](https://reader036.vdocument.in/reader036/viewer/2022062322/56814eab550346895dbc5625/html5/thumbnails/2.jpg)
2Click 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
![Page 3: Performance Metrics for Resilient Networks](https://reader036.vdocument.in/reader036/viewer/2022062322/56814eab550346895dbc5625/html5/thumbnails/3.jpg)
3Click 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
![Page 4: Performance Metrics for Resilient Networks](https://reader036.vdocument.in/reader036/viewer/2022062322/56814eab550346895dbc5625/html5/thumbnails/4.jpg)
4Click 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
pmin
Definition: set of considered scenarios S
![Page 5: Performance Metrics for Resilient Networks](https://reader036.vdocument.in/reader036/viewer/2022062322/56814eab550346895dbc5625/html5/thumbnails/5.jpg)
5Click 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
),,(1
, swvedDisconnectspSp
wvpSs
Sdis
![Page 6: Performance Metrics for Resilient Networks](https://reader036.vdocument.in/reader036/viewer/2022062322/56814eab550346895dbc5625/html5/thumbnails/6.jpg)
6Click to edit author name
European Nobel Test Network
![Page 7: Performance Metrics for Resilient Networks](https://reader036.vdocument.in/reader036/viewer/2022062322/56814eab550346895dbc5625/html5/thumbnails/7.jpg)
7Click to edit author name
Network Unavailability for Madrid‘s Aggregates
of Madrid‘s Aggregates
![Page 8: Performance Metrics for Resilient Networks](https://reader036.vdocument.in/reader036/viewer/2022062322/56814eab550346895dbc5625/html5/thumbnails/8.jpg)
8Click to edit author name
Average Network Unavailability for Routers
![Page 9: Performance Metrics for Resilient Networks](https://reader036.vdocument.in/reader036/viewer/2022062322/56814eab550346895dbc5625/html5/thumbnails/9.jpg)
9Click to edit author name
Network Unavailability for Overall Traffic
C
![Page 10: Performance Metrics for Resilient Networks](https://reader036.vdocument.in/reader036/viewer/2022062322/56814eab550346895dbc5625/html5/thumbnails/10.jpg)
10Click 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 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
![Page 11: Performance Metrics for Resilient Networks](https://reader036.vdocument.in/reader036/viewer/2022062322/56814eab550346895dbc5625/html5/thumbnails/11.jpg)
11Click to edit author name
Impact of Probability Limit pmax for Failure Scenarios
pmin=10-6 pmin=10-8
![Page 12: Performance Metrics for Resilient Networks](https://reader036.vdocument.in/reader036/viewer/2022062322/56814eab550346895dbc5625/html5/thumbnails/12.jpg)
12Click to edit author name
Which Link is Most at Risk?
![Page 13: Performance Metrics for Resilient Networks](https://reader036.vdocument.in/reader036/viewer/2022062322/56814eab550346895dbc5625/html5/thumbnails/13.jpg)
13Click to edit author name
Link Rankings
Utilization threshold uc
Utilization percentile q
Appropriate weighted integral based on utilization distribution
![Page 14: Performance Metrics for Resilient Networks](https://reader036.vdocument.in/reader036/viewer/2022062322/56814eab550346895dbc5625/html5/thumbnails/14.jpg)
14Click to edit author name
Graphical Presentation
![Page 15: Performance Metrics for Resilient Networks](https://reader036.vdocument.in/reader036/viewer/2022062322/56814eab550346895dbc5625/html5/thumbnails/15.jpg)
15Click 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