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Modelling Performance Optimizations for Content-based Publish/Subscribe

Modelling Performance Optimizations for Content-based Publish/Subscribe. Alex Wun and Hans-Arno Jacobsen Department of Electrical and Computer Engineering Department of Computer Science University of Toronto. Matching Performance Optimizations.

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Modelling Performance Optimizations for Content-based Publish/Subscribe

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  1. Modelling Performance Optimizations for Content-based Publish/Subscribe Alex Wun and Hans-Arno Jacobsen Department of Electrical and Computer Engineering Department of Computer Science University of Toronto

  2. Matching Performance Optimizations • Often based on exploiting similarities between subscriptions • Avoid unnecessary subscription and predicate evaluations • Can we abstract these optimizations? • Formalize content-based Matching Plans (order of predicate evaluations) • Theoretically quantify performance of matching plans • Compare heuristic techniques with optimal matching plans

  3. 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.

  4. Link-Group Topology Depth First Algorithm to determine probabilistically optimal matching plan [Greiner2006] in

  5. Link-Group Topology Low Selectivity X X High Selectivity o o

  6. . . . . . . Dynamic Programming (not very efficient) . . . Link-Cluster Topology Multi-Cluster-Link Topology . . . . . . . . . . . . . . . Cluster Topology Multi-Link Topology Arbitrary Topologies

  7. Cluster Topology o • Dramatic scalability effects of clustering in CPS • Observed trend depends on proportion of commonalities not number of predicates X . . .

  8. Applications – DoS Resilience Normal Subscription Migration

  9. Applications – DoS Resilience High Commonality Low Commonality High Commonality

  10. Related Work • Carzaniga et al. [Carzaniga2001] • Formal notation for covering • Mühl [Mühl2002] • Formal syntax for CPS routing • Li et al. [Li2005] and Campailla et al. [Campailla2001] • BDD based CPS matching algorithms

  11. Conclusion • Probabilistically optimal matching plans are known for some subscription topologies • Scalable CPS matching depends heavily on commonalities • Focus on abstracting commonalities • Future work • Express covering, correlation, … • Arbitrary subscription topologies • Metrics for expressing compression due to existence of commonalities

  12. References • [Greiner2006] • Finding optimal satisficing strategies for And-Or trees, Artificial Intelligence • [Carzaniga2001] • Design and Evaluation of a Wide-Area Event Notification Service, ACM Transactions on Computer Systems • [Mühl2002] • Large-Scale Content-Based Publish/Subscribe Systems, PhD Thesis • [Li2005] • A Unified Approach to Routing, Covering and Merging in Publish/Subscribe Systems based on Modified Binary Decision Diagrams, ICDCS • [Campailla2001] • Efficient filtering in Publish-Subscribe Systems using Binary Decision, International Conference on Software Engineering

  13. Extra Slides

  14. Table-based versus Tree-based Naive Table-based Tree-based

  15. Disjunctive Commonalities Given some publication P • “Shortcut” unnecessary subscription/predicate evaluations • Examples: • Per-Link Matching [Banavar1999,Carzaniga2003] • DNF Subscriptions Computed by matching algorithm

  16. Conjunctive Commonalities Given some publication P • “Shortcut” unnecessary subscription/predicate evaluations • Examples: • Shared predicates • Clustering on subscription classes or attributes • “Pruning” strategies (e.g., number of attributes) Computed by matching algorithm

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