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Denial of Service in Content-based Publish/Subscribe Systems

Denial of Service in Content-based Publish/Subscribe Systems. M.A.Sc. Candidate: Alex Wun Thesis Supervisor: Hans-Arno Jacobsen Department of Electrical and Computer Engineering Department of Computer Science University of Toronto. v0.4. Background Context of Thesis Work.

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Denial of Service in Content-based Publish/Subscribe Systems

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  1. Denial of Service in Content-based Publish/Subscribe Systems M.A.Sc. Candidate: Alex Wun Thesis Supervisor: Hans-Arno Jacobsen Department of Electrical and Computer Engineering Department of Computer Science University of Toronto 2007 v0.4

  2. Background Context of Thesis Work • PADRES middleware platform • Content-based Publish/Subscribe (CPS) • Originally inspired by distributed dashboard and job scheduling requirements • Increasingly motivated by enterprise application integration • Need to investigate different facets of security for CPS systems • Security amongst top concern in many application scenarios 2007

  3. Contributions of Thesis Work DoS Characteristics Attack Taxonomy Attack Experiments DoS Resilience DoS Prevention Policy Model Commonality Model Matching Algorithm Policy Framework 2007

  4. Content-based Publish/Subscribe Publication (Tuple) P P Publishers [(event,prescription), (patientID,123), (age,63), (drug,X) …] Storing Filters (Functions) Subscrip- tions Broker Network [(event=prescription), (age>50)] [(event=prescription), (drug=Y)] Subscribers S S Subscriptions (Boolean Functions) “Matching” 2007

  5. Matching Performance Optimizations • Often based on exploiting similarities (overlap) between subscriptions • Avoid unnecessary subscription and predicate evaluations • Can we abstract these optimizations? • Formalize content-based Matching Plans (order of subscription and predicate evaluations) • Quantify performance of existing optimizations • Discover future potential optimizations 2007

  6. Commonality Model For a subscription set Disjunctive Commonality Expression • Per-Link Matching • DNF Subscriptions or • Shared predicates • Clustering on subscription classes or attributes • “Pruning” strategies (e.g., number of attributes) Conjunctive Commonality Expression A set of commonality expressions is a subscription topology. 2007

  7. Example: Link-Group Topology Depth First Algorithm to determine probabilistically optimal matching plan [Greiner2006] in 2007

  8. Example: Link-Group Topology Low Selectivity X X High Selectivity o o

  9. Example: Cluster Topology Simulation Experimental (in PADRES) o • Dramatic scalability effects of clustering in CPS • Observed trend depends on proportion of commonalities not number of predicates X . . .

  10. Extended Implication Relationships Between subscriptions Between predicates Between commonalities 2007

  11. Simple Implication Expressions Mixed operatorlists currently not supported 2007

  12. Matching Engine Architecture Shared pred. index (conj. comm.) All predicates index Subscription index … … … … Subscription pool Predicate pool Map Sorted List (Map) Overlay links(disj. comm.) Node elements 2007

  13. Matching Engine Architecture True True False D.C. False Node Elements • Node Element • Subscription • Predicate • Overlay link • (conj. comm.) • (DNF subs) D.C. Implication Lists 2007

  14. Subscription InsertionPredicate Insertion Shared pred. index (conj. comm.) All predicates index Subscription index … … … … Subscription pool Conj. Comm. Predicate pool Unknown predicate priorities default to head of list Overlay links(disj. comm.) 2007

  15. Subscription InsertionImplication List Update a P > 3 4 5 6 7 8 9 P’s True -> True list 3 4 5 6 7 8 9 Xi’s False -> False list 3 4 5 6 7 8 9 P’s False -> False list 2007

  16. Performance Experiments • Generated subscription workloads from ~50 to ~200,000 predicates • {5,10,15,20} Avg. Predicates x {10,100,1000,10000} Subscriptions • 4 Different subscription topologies • Low/High clustering (5/200 classes) • Low/High sharing (subscription overlap) • Randomly generated and matched 100 publications 2007

  17. High Cluster High Sharing Low Sharing Low Cluster

  18. High Cluster High Sharing Low Sharing Low Cluster

  19. Cross-cluster Attributes 2007

  20. Cross-cluster Attributes 2007

  21. High Cluster High Sharing Low Sharing Low Cluster

  22. High Cluster High Sharing Low Sharing Low Cluster

  23. Conclusions • Model captures many existing and potential optimization techniques • Implication list approach significantly reduces number of predicate evaluations in all workloads • Superior for expensivepredicates • Implementation trade-off: Control cascade overhead/usage • Cluster/Index implication lists as well • Optimize iteration over marked nodes • Additional clustering/indexing beyond only event class • Future work • Additional conjunctive/disjunctive commonalities, implication relationships? • Implication relationships relevant to message distribution? • Rule-based implementation of implication/commonality algorithm? Thank You – Questions? 2007

  24. *** Extra Slides *** 2007

  25. High clustering, High sharing

  26. Low clustering, High sharing

  27. Low clustering, Low sharing

  28. High clustering, Low sharing

  29. Publication matchingCommonality Phase Shared pred. index (conj. comm.) All predicates index Subscription index … … … … Subscription pool Predicate pool Iterate and evaluatewhile TC is false Termination Condition: All overlay links have been decided Overlay links(disj. comm.) 2007

  30. Publication MatchingImplication Cascade True True False D.C. True False Cascade and Mark If not alreadydetermined, Evaluate D.C. D.C. False True “Advanced” implications handled with a method call triggered by state change(e.g. Predicate becomes true, calls countTruePredicate() on subscriptions) 2007

  31. Publication MatchingSubscription Phase Shared pred. index (conj. comm.) All predicates index Subscription index … … … … Subscription pool Predicate pool Iterate and evaluatewhile TC is false Overlay links(disj. comm.) + Cascade and Mark + Cascade and Count 2007

  32. Publication MatchingCleanup Phase • There is no cleanup phase • A counter (Vm) is incremented at the start of each publication matching phase • All determined results are versioned (Vd) • A determined result is stale if Vd < Vm • To avoid overflow, reset counter every: • 64bit counter ~= 16x10^18 pubs • @1000 pub/s ~ 16x10^15 s • ~32x10^6 s/year ~ 0.5x10^9 years 2007

  33. Publication MatchingSorted Lists • Commonality/predicate lists sorted by (p+1/N) • p is the predicate selectivity • N is the number of subscriptions sharing the predicate • Subscriptions sorted by (1-p)n • p is average predicate selectivity • n is number of predicates • Predicate hash sorted by predicate value • Commonality/predicate/subscription sorting is meant to be extendable with different priority equations • Include predicate cost, length of implication lists, etc … 2007

  34. High Cluster High Sharing Low Sharing Low Cluster 2007

  35. High Cluster High Sharing Low Sharing Low Cluster 2007

  36. Content-based Publish/Subscribe Databases Query (Boolean Function) Subscriptions (Boolean Functions) Inverse Problems Storing Functions Subscrip- tions Tables Storing Data Scalable Performance Query Plans Matching Plans? Publication (Tuple) DB Rows (Tuples) 2007

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