Modelling Performance Optimizations for Content-based Publish/Subscribe
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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
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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
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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)
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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
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Link-Group TopologyLow Selectivity
X X
High Selectivity
o
o
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Link-Cluster Topology
. . . . . . . . .
Multi-Cluster-Link Topology
. . .
Cluster TopologyMulti-Link Topology
. . . . . .
Dynamic Programming(not very efficient)
. . . . . .
Arbitrary Topologies
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Cluster Topology
• Dramatic scalability effects of clustering in CPS• Observed trend depends on proportion of commonalities not number of predicates
. . .X
o
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Applications – DoS Resilience
Normal
SubscriptionMigration
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Applications – DoS Resilience
HighCommonality
LowCommonality
HighCommonality
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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
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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
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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
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MIDDLEWARE SYSTEMSRESEARCH GROUP
Extra Slides
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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
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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
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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