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Simulation of Adversarial Scenariosin OMNeT++
Putting Adversarial Queueing Theory from ItsHead to Feet
Daniel Berger, Martin Karsten, and Jens Schmitt
OMNeT++ Workshop 2013 in Cannes, French RivieraMarch 5th, 2013
1. Introduction
Introduction
1 Introduction
2 Adversarial Queueing
3 Simulation Results
4 Challenges and Future Work
Daniel Berger, Martin Karsten, and Jens Schmitt – Simulation of Adversarial Scenarios in OMNeT++ S. 1/18
1. Introduction
Overview
Adversarial Queueing Theory (AQT) studies hypotheticalworst-case queueing scenarios.
What we did
Why simulation
Results
→ simulation of AQT scenarios
→ study, new insights, and communicate
→ e.g. on bound tightness, robustness
Goal of this talk
1 raise interest for AQT in the simulation community
2 establish simulation as complementary method to analysis
Daniel Berger, Martin Karsten, and Jens Schmitt – Simulation of Adversarial Scenarios in OMNeT++ S. 2/18
1. Introduction
Scenario
imagine a network with the following requirement:guaranteed delay ≤ 20ms
worst-case delay? ⇒ two steps:1 determine: is a delay guarantee possible2 if yes: find tight bound (employ network calculus/. . . )
rough definition
a network is stable ⇔ a finite bound on delay exists
Daniel Berger, Martin Karsten, and Jens Schmitt – Simulation of Adversarial Scenarios in OMNeT++ S. 3/18
1. Introduction
Scenario
imagine a network with the following requirement:guaranteed delay ≤ 20ms
worst-case delay? ⇒ two steps:1 determine: is a delay guarantee possible2 if yes: find tight bound (employ network calculus/. . . )
rough definition
a network is stable ⇔ a finite bound on delay exists
Daniel Berger, Martin Karsten, and Jens Schmitt – Simulation of Adversarial Scenarios in OMNeT++ S. 3/18
1. Introduction
Contribution
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framework to capture adversarial traffic description
� in short: packet trajectories, and adversarial strategies
implementation of some classical AQT scenarios
interpretation of some of the results
� modeling assumptions, bound tightness, parameter interaction
An open simulator for the Adversarial Queueing frameworkhttp://disco.informatik.uni-kl.de/content/Aqtmodel
Daniel Berger, Martin Karsten, and Jens Schmitt – Simulation of Adversarial Scenarios in OMNeT++ S. 4/18
2. Adversarial Queueing
Adversarial Queueing
1 Introduction
2 Adversarial Queueing
3 Simulation Results
4 Challenges and Future Work
Daniel Berger, Martin Karsten, and Jens Schmitt – Simulation of Adversarial Scenarios in OMNeT++ S. 5/18
2. Adversarial Queueing
Network Instability
network instability is based on inductive constructions
� recall: mentioned cyclic graphs
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hypothetical adversary (cf. online algorithms)
� can inject anywhere and can choose packet trajectory� adversary’s goal: maximize stress on network
injections are subject to a leaky-bucket constraint (e.g.):for any edge: sum of injections in [t1, t2] ≤ r(t2 − t1) + b
r < edge capacity
⇒ e.g. this topology is stable:
Daniel Berger, Martin Karsten, and Jens Schmitt – Simulation of Adversarial Scenarios in OMNeT++ S. 6/18
2. Adversarial Queueing
Example of Inductive Injection Scheme
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1. assume a set of red packets queued at e1
Daniel Berger, Martin Karsten, and Jens Schmitt – Simulation of Adversarial Scenarios in OMNeT++ S. 7/18
2. Adversarial Queueing
Example of Inductive Injection Scheme
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1. assume a set of red packets queued at e12. inject blue packets into e1 with path (e1, e2, e4)
Daniel Berger, Martin Karsten, and Jens Schmitt – Simulation of Adversarial Scenarios in OMNeT++ S. 7/18
2. Adversarial Queueing
Example of Inductive Injection Scheme
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1. assume a set of red packets queued at e12. inject blue packets into e1 with path (e1, e2, e4)3.a inject green packets into e1 with path (e1, e3, e4).
3.b inject packets into e2⇒ slow down blue packets
Daniel Berger, Martin Karsten, and Jens Schmitt – Simulation of Adversarial Scenarios in OMNeT++ S. 7/18
2. Adversarial Queueing
Example of Inductive Injection Scheme
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e1
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1. assume a set of red packets queued at e12. inject blue packets into e1 with path (e1, e2, e4)3.a inject green packets into e1 with path (e1, e3, e4).
3.b inject packets into e2⇒ slow down blue packets
4. fill queue at e4,more injections,transfer to e1,repeat . . .
Daniel Berger, Martin Karsten, and Jens Schmitt – Simulation of Adversarial Scenarios in OMNeT++ S. 7/18
2. Adversarial Queueing
Effect of Adversarial Injections
queue of e4 (and others) experiences repeated bursts
burst size grows without bound over time
no upper bound on queue length or delay
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theory seeks exact conditions of stability
as an engineer: open questions may be different!?
Daniel Berger, Martin Karsten, and Jens Schmitt – Simulation of Adversarial Scenarios in OMNeT++ S. 8/18
2. Adversarial Queueing
Open Questions
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in general:
how real is the threat?
so what does it mean?
more specific:
how long does it take to induce some particular delay?
countermeasures?
how realistic are modeling assumptions?
� perfect time synchronization� infinite buffers� errors in discretization, rough calculations, bounds, . . .
assumption of initial network state (so-called initial sets)
Daniel Berger, Martin Karsten, and Jens Schmitt – Simulation of Adversarial Scenarios in OMNeT++ S. 9/18
3. Simulation Results
Simulation Results
1 Introduction
2 Adversarial Queueing
3 Simulation Results
4 Challenges and Future Work
Daniel Berger, Martin Karsten, and Jens Schmitt – Simulation of Adversarial Scenarios in OMNeT++ S. 10/18
3. Simulation Results
Simulation Results
Case study with four examples
Baseball (BB), due to Andrews et al., 2001
Diaz et al., 2001
graph minor A+, due to Weinard, 2006
gadget chains, Lotker et al., 2004
Disclaimer
Only exemplary study - may not be universally valid.
yet, we consider the selection of scenarios representative
Daniel Berger, Martin Karsten, and Jens Schmitt – Simulation of Adversarial Scenarios in OMNeT++ S. 11/18
3. Simulation Results
Analytical Prediction vs. Simulative ResultQ
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[Pac
kets
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MethodAnalysisSimulation
Daniel Berger, Martin Karsten, and Jens Schmitt – Simulation of Adversarial Scenarios in OMNeT++ S. 12/18
3. Simulation Results
Bound TightnessGadget Chains, Lotker et al.
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MethodSimulationAnalysis: lower bound
Daniel Berger, Martin Karsten, and Jens Schmitt – Simulation of Adversarial Scenarios in OMNeT++ S. 13/18
3. Simulation Results
Rerandomization
adversary relies on strictly deterministic synchronization
� events take place on distinct nodes with exact timing� no noise: fixed channel capacity
we introduce “rerandomization”
� channel with variable delay� every traversal delay is sampled from a Normal distribution
channel mean delay = deterministic delay
delay standard deviation e.g. of 5% or 30%
Daniel Berger, Martin Karsten, and Jens Schmitt – Simulation of Adversarial Scenarios in OMNeT++ S. 14/18
3. Simulation Results
Rerandomization
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Deviation
Daniel Berger, Martin Karsten, and Jens Schmitt – Simulation of Adversarial Scenarios in OMNeT++ S. 15/18
4. Challenges and Future Work
Challenges and Future Work
1 Introduction
2 Adversarial Queueing
3 Simulation Results
4 Challenges and Future Work
Daniel Berger, Martin Karsten, and Jens Schmitt – Simulation of Adversarial Scenarios in OMNeT++ S. 16/18
4. Challenges and Future Work
Challenges and Future Work
high number of nodes, events
long execution time
simplifying abstraction,modeling error
→ approximation
→ parallelism
→ step-wise convergencetowards realism
classical challenges in simulation
we have great interest to learn from simulation community
future work
� utilize as part of our own theoretic work in AQT� develop towards more realistic scenarios
e.g. assess the role of cross traffic in the adversary’s operation� enhance visualization
Daniel Berger, Martin Karsten, and Jens Schmitt – Simulation of Adversarial Scenarios in OMNeT++ S. 17/18
Simulation of Adversarial Scenariosin OMNeT++
Putting Adversarial Queueing Theory from ItsHead to Feet
Daniel Berger, Martin Karsten, and Jens Schmitt
Slides will be available:http://disco.informatik.uni-kl.de/software/AQTmodel/SlidesOMNeT2013.pdf
4. Challenges and Future Work
Rerandomization Detailed Operation
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Deviation0%30%
Daniel Berger, Martin Karsten, and Jens Schmitt – Simulation of Adversarial Scenarios in OMNeT++ S. 19/18
4. Challenges and Future Work
Interaction of Initial Sets with Injection Rate
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Initial Set Size
Daniel Berger, Martin Karsten, and Jens Schmitt – Simulation of Adversarial Scenarios in OMNeT++ S. 20/18