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Enabling a “RISC” Approach for Software-Defined Monitoring using Universal Streaming Vyas Sekar Zaoxing Liu, Greg Vorsanger, Vladimir Braverman
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Network Management: Many Monitoring Requirements SDN Controller (OpenDayLight etc.) Traffic Engineering Analyze new user apps Anomaly Detection NetworkForensics Worm Detection Accounting Botnet analysis ……. “Heavy-hitters” “Flow size distribution” “SuperSpreaders” “Entropy”, “Traffic Changes” 1
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Traditional: Packet Sampling 2 161311 1 Flow reports 1 Not good for fine-grained analysis Extensive literature on limitations for many tasks! 11316111131611 12 Sample packets at random, aggregate into flows FlowId Counter Flow = Packets with same pattern Source and Destination Address and Ports Estimate: FSD, Entropy, Heavyhitters, Changes, SuperSpreaders ….
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Application-Specific Sketches Packet Processing Counter Data Structures Application-Level Metric Heavy Hitter EntropySuperspreader Complexity: Need per-metric implementation Recent Example: OpenSketch [NSDI’13] Trend: Many more applications appear! …. Monitoring (on router) Bloom-filter, Count-min Sketch, reversible sketch, etc. 3 Packet Processing Counter Data Structures Application-Level Metric Packet Processing Counter Data Structures Application-Level Metric …. Traffic Computation (off router)
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Packet Processing Counter Data Structures Application-Level Metric Support many applications Holy Grail of Flow Monitoring? Results with high accuracy 4 Traffic
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Our Solution: Universal Monitoring 5 Recent theory advances: Universal Streaming Packet Processing Universal Sketch Traffic App 1 Application-specific Computation App n …... UnivMon Control Plane UnivMon Data Plane One sketch does it ALL
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Theory of Universal Streaming 1. Vladimir Braverman, Rafail Ostrovsky: Zero-one frequency laws. STOC 2010 2. Generalizing the Layering Method of Indyk and Woodruff: Recursive Sketches for Frequency-Based Vectors on Streams. APPROX-RANDOM 2013 13315112465 …... (A stream of length m with n unique items) ‘Universal’ Sketch Estimated G-sum frequency vector is 6
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Universal Sketch Data Structure 13315112465 11511 25 2 L2 Heavy Hitter Algorithms (1,4), (3,2),(5,2) Heavy Hitters (1,4), (5,2),(2,1) …... (2,1) 7 (5,2), (2,1) 0 1 log(n) …... Generate k=log(n) pairwise ind. zero-one hash functions: H 1 …. H k 25 5 Similar to counting bloom filter H 1 (1)=1, H 1 (5)=1, H 1 (2)=1 H 2 (5)=1, H 2 (2)=1 H 3 (2)=1 Levels Heavy Hitter Alg Count Sketch Alg +4+2-2 -4+2 +4-2 +4-2 +4-2+1 -2-4 +1 +1 …... Count-Sketch, Pick-and- drop etc. In Parallel
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Estimating G-sum (1,4), (3,2),(5,2) Counters from Universal Sketch (1,4), (5,2), (2,1) …... (2,1) 8 (5,2),(2,1) Levels 0 1 log(n) …... Apply arbitrary g() (1,g(4)), (3,g(2)),(5,g(2)) (1,g(4)), (5,g(2)), (2,g(1)) (5,g(2)),(2,g(1)) (2,g(1)) Y 3 =g(1) Sum of the g()s Y 2 =g(1)+g(2) Y 1 =g(1)+g(2)+g(4) Y 0 =2g(1)+2g(2)+g(4) Estimated G-sum Recursive Steps: Y i-1 = 2Y i + new counters – repeated counters
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Putting it together: UnivMon Universal Sketch Offline Recursive Computation 9
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Comparison with custom sketches via OpenSketch Preliminary Evaluation 10 N/A
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Distributed universal streaming Multidimensional data Dynamically change monitoring scope Feasibility of hardware implementations? Future Directions 11
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12 Conclusions Network management needs many traffic metrics Today’s solutions offer undesirable extremes Generic but low fidelity (e.g., sampling) High fidelity but high complexity (e.g., specific-sketches) Holy grail: Universal Monitoring Decouple monitoring control and data plane like SDN! This work: Can be viable via Universal Sketches Several open questions e.g. dynamic, multidimensional, distributed, hardware viability
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