flexible performance prediction of data center networks using automatically generated simulation...
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Flexible Performance Prediction of Data Center Networks using Automatically Generated Simulation Models
Piotr Rygielski, Samuel Kounev, Phuoc Tran-Gia
Chair of Software EngineeringUniversity of Würzburg
http://se.informatik.uni-wuerzburg.de/
SIMUtools2015, Athens, Greece, 25.08.2015
Motivation
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(dst_IP>*.*.*.128) ? port1 : port0;
(src_TCP==80 && src_TCP==443) ? port1 : port0;
What if…
What if…
What if…
Current performance known – monitoring.
Goal: predict performance after a change.
Research Gap
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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 models Detailed simulations
Time overhead
Accuracy
Model
Model
Model
Model
Approach
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Real network
Model extraction
Model transformation(s
)
Descriptive model
Performance model(s)
Approach
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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
singlemodel
script
Models and Transformations
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miniDNI Meta-Model
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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)
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Transformation mDNI-to-QPN
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QPN model of a network node, e.g., Switch, Server (mDNI)
Aspects: None, Generator, Receiver, Traversal
Transformation mDNI-to-QPN
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QPN model of a network link (mDNI)
Delays from Interfaces and links integrated in queueing place
Transformation mDNI-to-QPN
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Transformations - comparison
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Traffic Management System
GPS Sensors
Traffic Light
Sensors
http://www.cl.cam.ac.uk/research/time/
Induction Loops
Traffic Cameras
Case study – SBUS/PIRATES
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Case study – SBUS/PIRATES
Event Bus
Bus Sensors
TrafficControl
LicensePlate
RecognitionCamCam
Speeding
Toll
LocationBus
Proximity
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Case study – SBUS/PIRATES
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Model Calibration
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Experiment - Hardware
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Results – Prediction Accuracy
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Results – Simulation Time
19 Piotr Rygielski Motivation & Approach DNI & Transformations Current Focus Planning
Dumbbell topology
Results – Simulation Time
20 Piotr Rygielski Motivation & Approach DNI & Transformations Current Focus Planning
Dumbbell topology
Conclusions
21 Piotr Rygielski Motivation & Approach DNI & Transformations Current Focus Planning
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
Thank You!
http://se.informatik.uni-wuerzburg.de
Code & more info:
http://go.uni-wuerzburg.de/aux