MIDDLEWARE SYSTEMSRESEARCH GROUP

Modelling Performance Optimizations for Content-based Publish/SubscribeAlex Wun and Hans-Arno Jacobsen

Department of Electrical and Computer EngineeringDepartment of Computer ScienceUniversity of Toronto

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

Commonality Model

}{ 1 mSS

CSS m 1

For a subscription set

mSSC 1

or

DisjunctiveCommonalityExpression

ConjunctiveCommonalityExpression

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)

Link-Group Topology

LSS m 1

PP

PP

PSPSPL

mmnm

n

m

1

111

1

1

CSS m 1

NNO ln

Depth First Algorithm to determine probabilistically optimal matching plan [Greiner2006] in

Link-Group TopologyLow Selectivity

X X

High Selectivity

o

o

Link-Cluster Topology

. . . . . . . . .

Multi-Cluster-Link Topology

. . .

Cluster TopologyMulti-Link Topology

. . . . . .

Dynamic Programming(not very efficient)

. . . . . .

Arbitrary Topologies

Cluster Topology

• Dramatic scalability effects of clustering in CPS• Observed trend depends on proportion of commonalities not number of predicates

. . .X

o

Applications – DoS Resilience

Normal

SubscriptionMigration

Applications – DoS Resilience

HighCommonality

LowCommonality

HighCommonality

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

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

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

MIDDLEWARE SYSTEMSRESEARCH GROUP

Extra Slides

Table-based versus Tree-based

SNNC SSnSnC

n

n

N

NS

NN

nn

11

1SN

kRc

1

1

1

1

p

pSp

p

pC

Nk

k

N

nRc

Naive Table-based Tree-based

Disjunctive Commonalities

“Shortcut” unnecessary subscription/predicate evaluations

Examples: Per-Link Matching [Banavar1999,Carzaniga2003] DNF Subscriptions

CSS m 1 PCPSi Given some publication P

Computed by matching algorithm

Conjunctive Commonalities

“Shortcut” unnecessary subscription/predicate evaluations

Examples: Shared predicates Clustering on subscription classes or attributes “Pruning” strategies (e.g., number of attributes)

PSPC iGiven some publication P

Computed by matching algorithm

mSSC 1