Flexible Performance Prediction of Data Center Networks using Automatically Generated Simulation Models

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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 SIMUtools2015, Athens, Greece,

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

3 3 Piotr Rygielski Research Gap Solution time Addressed Models Simulation models End-to-end performance analysis not detailed enough Existing network models too coarse or too fine grained Analytical models Level of details Other approaches focus only on selected technologies/protocols Flexibility in modeling is missing Time overhead Black-box models Detailed simulations Accuracy

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

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

6 6 Piotr Rygielski Models and Transformations Input Transformation Descriptive Model Predictive Model User Input (Model Extraction) DNI Model Routing format conversion DNI2QPN DNI2mDNI DNI2OMNeT++ Queueing Petri Net Model (DNI) minidni Model OMNeT++ Model mdni2qpn Queueing Petri Net Model (mdni)

7 minidni Meta-Model When not enough data to build full DNI instance Very coarse-granular modeling Network 0..* Route start end 1..* Node connects 2 Link 0..* destination 1 0..* TrafficSource 1 NodePerf 1 LinkPerf 1 Workload 7 Piotr Rygielski

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

9 9 Piotr Rygielski Transformation mdni-to-qpn QPN model of a network node, e.g., Switch, Server (mdni) Aspects: None, Generator, Receiver, Traversal input forward-traversing-traffic output dummy -trafficsource color-generation dummy generation-delay traffic-source node

10 10 Piotr Rygielski Transformation mdni-to-qpn QPN model of a network link (mdni) Delays from Interfaces and links integrated in queueing place transmission-delay node node transmission-delay link

11 11 Piotr Rygielski Transformation mdni-to-qpn

12 12 Piotr Rygielski Transformations - comparison

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

14 14 Piotr Rygielski Case study SBUS/PIRATES

15 Case study SBUS/PIRATES Network Piotr Rygielski

16 16 Piotr Rygielski Model Calibration simulated network simulation intergeneration time ON OFF generation delay = 0 ON t t network software ON OFF ON t generation delay simulation think time software think time t

17 17 Piotr Rygielski Experiment - Hardware VM 4.1 S4 S2 VM 5.1 S9 S8 S7 S5 S3 S6 VM 6.1 SW1 SW2 SW3 S1

18 Results Prediction Accuracy 18 Piotr Rygielski Motivation & Approach DNI & Transformations Current Focus Planning

19 Results Simulation Time Dumbbell topology Simulation duration [s] OMNeT++ (30s) DNI QPN minidni QPN Traffic intensity (think time) [ms] 19 Piotr Rygielski Motivation & Approach DNI & Transformations Current Focus Planning

20 Results Simulation Time Dumbbell topology Simulation duration [s] OMNeT++ (30s) DNI QPN minidni QPN Number of nodes 20 Piotr Rygielski Motivation & Approach DNI & Transformations Current Focus Planning

21 Conclusions Automatically generated three predictive models Prediction errors up to 18% for DNI (fully automatic process) minidni-qpn: accuracy loss (~4%) with speedup up to 300x Support for network virtualization in DNI (SDN planned) Model calibration is important. Modeling support tools needed 21 Piotr Rygielski Motivation & Approach DNI & Transformations Current Focus Planning

22 Thank You! Code & more info:

Model-Based Throughput Prediction in Data Center Networks

Model-Based Throughput Prediction in Data Center Networks Piotr Rygielski and Samuel Kounev Institute for Program Structures and Data Organization, Karlsruhe Institute of Technology (KIT) 763 Karlsruhe,