1. Flexible Performance Prediction of Data Center Networks using Automatically Generated Simulation Models Piotr Rygielski, Samuel Kounev, Phuoc Tran-Gia Chair of Software Engineering University of Würzburg http://se.informatik.uni-wuerzburg.de/ SIMUtools2015, Athens, Greece, 25.08.2015

2. Motivation 2 Piotr Rygielski (dst_IP>*.*.*.128) ? port1 : port0; (src_TCP==80 && src_TCP==443) ? port1 : port0; What if… Current performance known – monitoring. Goal: predict performance after a change.

3. Research Gap 3 Piotr Rygielski  End-to-end performance analysis not detailed enough  Existing network models too coarse or too fine grained  Other approaches focus only on selected technologies/protocols  Flexibility in modeling is missing Black-box modelsDetailed simulations Time overhead Accuracy Model

4. Approach 4 Piotr Rygielski Real network Model extraction Model transformation(s) Descriptive model Performance model(s)

5. Approach 5 Piotr Rygielski Real network DNI meta model (modeling language) Structure model Traffic model Configuration model Model-to-model transformations to QN to OMNeT++ to QPN to ns3 to formulas other... Performance models single model script

6. Models and Transformations 6 Piotr Rygielski

7. miniDNI Meta-Model 7 Piotr Rygielski  When not enough data to build full DNI instance  Very coarse-granular modeling

8. DNI Meta-Model Structure model Traffic model Configuration model SoftwareComponent NetworkInterface Link PerformanceDescriptions Node TrafficSource Workload Flow Start Stop Wait Transmit Loop Sequence Route ProtocolStack NetworkProtocol DNI Meta-Model (short) 8 Piotr Rygielski

9. Transformation mDNI-to-QPN 9 Piotr Rygielski  QPN model of a network node, e.g., Switch, Server (mDNI)  Aspects: None, Generator, Receiver, Traversal

10. Transformation mDNI-to-QPN 10 Piotr Rygielski  QPN model of a network link (mDNI)  Delays from Interfaces and links integrated in queueing place

11. Transformation mDNI-to-QPN 11 Piotr Rygielski

12. Transformations - comparison 12 Piotr Rygielski

13. Traffic Management System GPS Sensors Traffic Light Sensors http://www.cl.cam.ac.uk/research/time/ Induction Loops Traffic Cameras Case study – SBUS/PIRATES 13 Piotr Rygielski

14. Case study – SBUS/PIRATES 14 Piotr Rygielski

15. Case study – SBUS/PIRATES 15 Piotr Rygielski

16. Model Calibration 16 Piotr Rygielski

17. Experiment - Hardware 17 Piotr Rygielski

18. Results – Prediction Accuracy 18 Piotr Rygielski Motivation & ApproachDNI & TransformationsCurrent FocusPlanning

19. Results – Simulation Time 19 Piotr Rygielski Motivation & ApproachDNI & TransformationsCurrent FocusPlanning  Dumbbell topology

20. Results – Simulation Time 20 Piotr Rygielski Motivation & ApproachDNI & TransformationsCurrent FocusPlanning  Dumbbell topology

21. Conclusions 21 Piotr Rygielski Motivation & ApproachDNI & TransformationsCurrent FocusPlanning Automatically generated three predictive modelsPrediction errors up to 18% for DNI (fully automatic process)miniDNI-QPN: accuracy loss (~4%) with speedup up to 300xSupport for network virtualization in DNI (SDN planned)Model calibration is important. Modeling support tools needed

22. Thank You! piotr.rygielski@uni-wuerzburg.de http://se.informatik.uni-wuerzburg.de Code & more info: http://go.uni-wuerzburg.de/aux