1. MIDDLEWARE SYSTEMS RESEARCH GROUP 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 or Disjunctive Commonality Expression Conjunctive Commonality Expression A set of commonality expressions is a subscription topology. Per-Link Matching DNF Subscriptions Shared predicates Clustering on subscription classes or attributes “Pruning” strategies (e.g., number of attributes)

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

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

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. MIDDLEWARE SYSTEMS RESEARCH GROUP Extra Slides

14. Table-based versus Tree-based NaiveTable-basedTree-based

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

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