Experimental Design for Experimental Design for Practical Network Diagnosis Practical Network Diagnosis Yin Zhang Yin Zhang University of Texas at Austin University of Texas at Austin [email protected][email protected]Joint work with Han Hee Song and Joint work with Han Hee Song and Lili Qiu Lili Qiu MSR EdgeNet Summit MSR EdgeNet Summit June 2, 2006 June 2, 2006
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Experimental Design for Practical Network Diagnosis
Experimental Design for Practical Network Diagnosis. Yin Zhang University of Texas at Austin [email protected] Joint work with Han Hee Song and Lili Qiu MSR EdgeNet Summit June 2, 2006. Practical Network Diagnosis. Ideal - PowerPoint PPT Presentation
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Experimental Design for Experimental Design for Practical Network DiagnosisPractical Network Diagnosis
Yin ZhangYin ZhangUniversity of Texas at AustinUniversity of Texas at Austin
Comparison of DOE Algorithms: Comparison of DOE Algorithms: Estimating Network-Wide Mean RTTEstimating Network-Wide Mean RTT
A-optimal yields the lowest error.
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Comparison of DOE Algorithms: Comparison of DOE Algorithms: Estimating Per-Path RTTEstimating Per-Path RTT
A-optimal yields the lowest error.
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Differentiated Design: Differentiated Design: Inference Error on Preferred PathsInference Error on Preferred Paths
Lower error on the paths with higher weights.
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Differentiated Design: Differentiated Design: Inference Error on the Remaining PathsInference Error on the Remaining Paths
Error on the remaining paths increases slightly.
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Augmented DesignAugmented Design
A-optimal is most effective in augmenting an existing design.
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Multi-user DesignMulti-user Design
A-optimal yields the lowest error.
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SummarySummaryOur contributions
– Bring Bayesian experimental design to network measurement and diagnosis
– Develop a flexible framework to accommodate different design requirements
– Experimentally show its effectiveness
Future work– Making measurement design fault tolerant– Applying our technique to other diagnosis problems– Extend our framework to incorporate additional