Data Center Network Throughput Analysis using Queueing Petri Nets

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Bertram Wiggins
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1 Data Center Network Throughput Analysis using Queueing Petri Nets Piotr Rygielski, Samuel Kounev Karlsruhe Institute of Technology, Karlsruhe, Germany University of Würzburg, Würzburg, Germany DCPerf2104, Madrid, Spain,

2 Context Data center networks Performance Run- time 0

3 Motivation What if (dst_ip>*.*.*.128)? port1 : port0; What if (src_tcp==80)? port1 : port0; 1 What if Mo#va#on Approach Meta- model Results Conclusion

4 Motivation perf( (dst_ip>*.*.*.128)? port1 : port0; )= What if perf( (src_tcp==80)? port1 : port0; )=? perf(,,,, )=? 2 Mo#va#on Approach Meta- model Results Conclusion

selected technologies/protocols Flexibility in modeling is missing Time overhead

5 Research Gap End- to- end (software- software) 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 Time overhead Black-box models Detailed simulations Accuracy 3 Mo#va#on Approach Meta- model Results Conclusion

6 Approach Performance model(s) Model transformation(s) Descriptive model Real network Model extraction This paper: Queueing Petri Nets SimQPN 4

7 Approach Real network script Performance models Structure model Traffic model Configuration model to QN to OMNeT++ DNI model of a given network (DNI meta model instance) DNI meta model (the modeling language) single model to QPN to formulas to ns3 other... Model-to-model transformations 5

8 Descartes Network Infrastructures Meta- model DNI consists actually of three parts Structure Configuration Traffic 6

PhysicalNetworkInterface 1..* NetworkInterface 1.

9 The DNI Meta Model Structure NetworkStructure 1 especification 1 Node 1..* forwarding performance PhysicalNode 1 VirtualNode hostedon PhysicalNetworkInterface 1..* NetworkInterface 1..* VirtualNetworkinterface 2 connects 2 connects PhysicalLink 0..* Link VirtualLink LinkPerformanceS 7

10 The DNI Meta Model Traffic Loop 100 times Wait 100 ms Send picture picture 400KB NetworkTraffic originatesfrom 0..* 1..* trafficsources WorkloadDescription 1..* TrafficSource GenericWorkload 0..* AbstractAction 0..* StartAction LoopAction 1 ONOFFFlow StopAction BranchAction 1..* WaitAction SequenceAction 1..* TransmitAction * SoftwareComponent 1..* destination FlowDescription 1 GenericFlow 8

11 The DNI Meta Model Configuration 0..* Route start 1 Hop NetworkConfiguration 1..* ProtocolStack 1..* ProtocolLayer 1..* NetworkProtocol protocol nexthop 0..1 iscarriedby $ traceroute google.pl 1 henry ( ) ( ) ( ) 4 * * * 9

12 Case study SBUS/PIRATES Traffic Management System Induction Loops GPS Sensors Traffic Cameras Traffic Light Sensors 11

13 Case study SBUS/PIRATES Cam Cam Cam... LPR 12

14 Transformation to QPNs Queue Depository Nested QPN EndNode-1 IntermediateNode-1 EndNode-2 Transition Token eth-rx-# Link-1-L Link-1-R EndNode-3 traffic-source-# eth-tx-# o o o o o o o o o Ordinary Place Queueing Place Subnet Place input-trans rx-to-sw sw-to-tx output-trans input output input workloadcontrol workload-stop loop output transmit-flow workload-start wait-action 17

15 Transformation to QPNs - Transition port-#-rx port-#-tx input-trans routingtransition output-trans input output 17

Transformation to QPNs Workload input workloadcontrol workload-stop loop output transmit-flow workload-start wait-action Loop loop-iter-left loop-control loop-iterdone

16 Transformation to QPNs Workload input workloadcontrol workload-stop loop output transmit-flow workload-start wait-action Loop loop-iter-left loop-control loop-iterdone num-loopiter-to-1 output Branch input workloadcontrol output input forwardgeneratedtraffic loopstop subworkload 1-tonum-loop-iter loopstart forkbranch subworkload forwardgenerated-traffic 17

17 Transformation to QPNs Colors Tokens are distinguishable only by colors! One color for every flow, route, and different message size. 17

2 4 6 8 10 Intergeneration time [ms] 500 400 300 200 100 uperf CI

18 Experiment 10ms 10ms 800kB 800kB 800kB H4 S3 H7 H1 H3 H2 H5 H8 S1 S2 Layer 2 Throughput [Mbps] uperf CI SimQPN Intergeneration time [ms] uperf CI SimQPN Intergeneration time [ms] 18

19 Experiment 18

20 Exemplary Results (SimQPN) Only 7 second simulation time (Scenario C) OMNeT++ up to 20 minutes (preliminary estimation) Difficult to mimic TCP- like behavior using QPNs Limited number of token colors Each flow (src- dst pair) consumes at least 1 color Coarse granularity enforcement 19

21 Conclusions Automa6cally generated predic6ve model Acceptable predic6on accuracy despite introduced abstrac6ons Run 6me relevant aspects Support for network virtualiza6on (OpenFlow planned) All traffic sources should be modeled 11 Context Mo6va6on Approach Meta- model Conclusion

22 Thank you Danke für eure Aufmerksamkeit Dziękuję za uwagę Спасибо за ваше внимание Takk for oppmerksomhet

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