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 SIMUtools2015, Athens, Greece,

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.

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

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

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

Models and Transformations 6 Piotr Rygielski

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

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

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

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

Transformation mDNI-to-QPN 11 Piotr Rygielski

Transformations - comparison 12 Piotr Rygielski

Traffic Management System GPS Sensors Traffic Light Sensors Induction Loops Traffic Cameras Case study – SBUS/PIRATES 13 Piotr Rygielski

Case study – SBUS/PIRATES 14 Piotr Rygielski

Case study – SBUS/PIRATES 15 Piotr Rygielski

Model Calibration 16 Piotr Rygielski

Experiment - Hardware 17 Piotr Rygielski

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

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

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

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

Thank You! Code & more info: