YouLighter: An Unsupervised Methodology to Unveil YouTube CDN Changes
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1 YouLighter: An Unsupervised Methodology to Unveil YouTube CDN Changes Danilo Giordano, Stefano Traverso, Luigi Grimaudo, Marco Mellia, Elena Baralis Politecnico di Torino Alok Tongankar, Sabyasachi Sasha Narus Inc.
2 Outline Motivation YouLighter methodology Parameter settings Results Conclusion 2
3 Outline Motivation YouLighter methodology Parameter settings Results Conclusion 3
4 Why monitoring YouTube CDN? A massive, distributed infrastructure Generates 20+% of world wide traffic Several thousands of caches (single servers) Grouped into Hundreds of edge-nodes (groups of caches) However Almost no information about YouTube CDN are available Internet Service Provider (ISP) have no control over YouTube CDN which changes frequently and suddenly sometimes causing QoE degradations possibly causing users complaining 4
5 Why monitoring YouTube CDN? A massive, distributed infrastructure Generates 20+% of world wide traffic Several thousands of caches (single servers) Grouped into Hundreds of edge-nodes (groups of caches) However Almost no information about YouTube CDN are available Internet Service Provider (ISP) have no control over YouTube CDN which changes frequently and suddenly sometimes causing QoE degradations possibly causing users complaining 5
6 Scenario Edge-node 1 Edge-node 2 Edge-node 3 Internet Tstat Tstat PoP1 PoP2 Passive measurements only Probe: Tstat 100+ per-flow statistics RTT, TTL, number of packets sent and received, number of byte sent and received, hostname, etc.. 6
7 Experiments and Validation Three Tstat probes Two probes in ISP1 One probe in ISP2 Consider traffic snapshots sliding window which moves forward each day (ΔT=1d) Trace Period Volume # Unique Videos Caches ISP1-A 01/04/ /02/ TB 2,892,452 8,664 ISP1-B 01/04/ /02/ TB 2,848,625 8,899 ISP1-C 01/04/ /02/ TB 2,711,179 9,028 ISP2 01/03/ /07/ TB 305,802 3,755 7
8 Problem 1: unveil the infrastructure 1. monitoring the single cache is not effective Load distribution changes very frequently The rank of most used caches changes deeply everyday! Idea: monitor edge-nodes, not caches 8
9 Problem 2: Edge-node change over time Get RTT data Generate RTT Distribution for each cache Evaluate behavior of 5 percentiles Points of Distribution
10 Problem 2: Edge-node change over time Get RTT data Generate RTT Distribution for each cache Evaluate behavior of 5 percentiles Points of Distribution X (1) 10
11 Problem 2: Edge-node change over time How can we measure edge-node change over time? X (1) Edge-Nodes 10 X (2) 11
12 Our Solution: YouLighter Edge-node 1 Edge-node 2 Edge-node 3 Internet Tstat YouLighter Tstat PoP1 PoP2 1. Find edge-nodes: Design an unsupervised methodology to group single caches 2. Observe changes: Define a distance to highlight changes in edge-nodes definition and usage over time 12
13 Validation Can we exploit an oracle to validate the results? Yes! YouTube use IATA codes to name edge-nodes and caches rx--abcxxtxx.c.youtube.com Where ABC corresponds to the cache s closest airport e.g., r7--fra07t16.c.youtube.com Frankfurt But, turns out to be not reliable ping r7--fra07t16.c.youtube.com ~= 50ms, 60TTL ping r7--fra09t21.c.youtube.com ~= 110ms, 55TTL Some caches sharing same IATA behave differently! [more complicated than this see the paper] 13
14 Outline Motivation YouLighter methodology Results Conclusion 14
15 Solution to Problem 1: Group caches 1. Get YouTube traffic logs from Tstat probes 2. Measurement consolidation and filtering 3. Feature selection extract RTT, TTL compute stats (percentiles, mean, variance, etc ) Standardize the features (stats) to a unitary space 4. Multi-dimensional clustering using DBSCAN Input: list of (cache_name, <feature1, feature2,, featuren>) tuples Output: A set of clusters (possibly edge-nodes) + Cluster of outlier Parameters: Min Points (MP): min number of points to create a cluster Epsilon (ε): maximum distance between any given point in a cluster and its MP th closest neighbor in the same cluster (critical) 15
16 DBSCAN Performance and Parameter Sensitivity Tuning of DBSCAN Which features? Different statistics Percentiles (20 th, 35 th, 50 th, 65 th, 80 th ), mean+variance, etc.. Which ε choice? Performance indices for clustering evaluation: True Positive Rate: ratio between # of correctly labeled caches and total # of caches Fragmentation: ratio between # of clusters and the # of observed labels Pureness: ratio between # of assigned labels and total # of labels in the ground truth Best result when all indices are equal to 1 16
17 DBSCAN Performance and Parameter Sensitivity (cont d) 1 st week of November 2013 in ISP1 stable situation controlled by manual verification Percentiles vs Mean+Standard Deviation using different ε Percentiles are more robust Fix ε =
18 Now we can find Edge-nodes. And now how can we analyze Edge-Node evolution over time? 18
19 Solution to Problem 2: Constellation distance Surprisingly, no methodology in literature 1. Summarize each cluster in a single point called star Ĉ (n) 2. Astral Distance For each star in Ĉ (n) compute all distances to stars in Ĉ (n+1) and take min Repeat in the opposite direction 3. Constellation Distance Sum of all Astral Distances AD ( ˆx ) 1,Ĉ(2) AD ( ŷ ) 4,Ĉ(1) X (1) Ĉ(1) X (2) Ĉ (2) 19
20 Outline Motivation YouLighter methodology Results Conclusion 20
21 Change observed from ISP1-A Constellation Distance is effective at detecting changes Big changes during May July 2013 Highlighted also in ISP1-B dataset 21
22 What happened in May 2013 Before change During change 22
23 What happened in May 2013 During change After change 23
24 What happened in May 2013 Before change During After change 24
25 Impact on QoE Frank splits in Bad Frank: caches with large std(rtt) Good Frank : caches with small std(rtt) Download throughput distribution of Frank vs Milan 25
26 Changes observed from ISP2 Changes detected in March 2014 No QoE impairment 26
27 Outline Motivation YouLighter methodology Results Conclusion 27
28 Conclusion YouLighter is effective at detecting changes in YouTube s CDN infrastructure Effective clustering to unveil YouTube infrastructure Definition of a novel Constellation Distance to identify changes between different clustering Help ISPs, network admins, etc. to react to changes To limit QoE impairments To improve traffic engineering Patent requested in 2014 Accepted in Italy Pending in USA 28
29 Thanks for you Attention 29
YouLighter: An Unsupervised Methodology to Unveil YouTube CDN Changes
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