correctness of gossip-based membership under message loss
DESCRIPTION
Correctness of Gossip-Based Membership under Message Loss. Maxim Gurevich , Idit Keidar Technion. The Setting. Many nodes – n 10,000s, 100,000s, 1,000,000s, … Come and go Churn Fully connected network Like the Internet Every joining node knows some others (Initial) Connectivity. - PowerPoint PPT PresentationTRANSCRIPT
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Correctness of Gossip-Based Membership under Message Loss
Maxim Gurevich, Idit Keidar
Technion
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The Setting•Many nodes – n▫10,000s, 100,000s, 1,000,000s, …
•Come and go▫Churn
•Fully connected network▫Like the Internet
•Every joining node knows some others▫(Initial) Connectivity
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Membership: Each Node Needs To Know Some Live Nodes•Applications▫Gossip partners ▫Unstructured overlay networks▫Gathering statistics
•Work best with random node samples▫Gossip algorithms converge fast▫Overlay networks are robust, good expanders▫Statistics are accurate
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Membership Protocols•Each node has a view ▫Set of node ids▫Supplied to the application▫Used by membership protocol for maintenance▫Modeled as a directed graph
u v
w
v y w …
y
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Desirable Properties•Randomness…•Holy grail for samples: IID▫Each sample uniformly distributed▫Each sample independent of other samples
Avoid spatial dependencies among view entries Avoid correlations between nodes
▫Good load balance among nodes
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What About Churn? Desirable Properties Cont’d•Views should constantly evolve▫Remove failed nodes, add joining ones
•Views should evolve to IID from any state•Minimize temporal dependencies▫Dependence on the past should decay quickly ▫Useful for application requiring fresh samples
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Do Existing Protocols Measure Up?
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… … w …
Existing Work: Practical Protocols
•Studied only empirically▫Good load balance [Lpbcast, Jelasity et al 07] ▫Fast decay of temporal dependencies [Jelasity et al 07] ▫Induces spatial dependence
… … z …u v
w
v … w …
w
zExample: Push protocol
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v … z …
Existing Work: Analysis
•Analyzed theoretically [Allavena et al 05, Mahlmann et al 06]
▫ Uniformity, load balance, spatial independence ▫ Unrealistic assumptions
Atomic actions with bi-directional communication No message loss
▫ No bounds on decay of temporal dependencies
… … z …… … w …u v
w
v … w …
w
zShuffle protocol
z
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Our Contribution: Bridge This Gap •Formally specify desirable properties outlined
above•A practical protocol▫Tolerates message loss, churn, failures▫No complex bookkeeping for atomic actions
•Formally prove the desirable properties▫Including under message loss
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… …
Send & Forget Membership•The best of push and shuffle•Some view entries may be empty
u v
w
v … w … u w
u w
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S&F: Message Loss•Message loss▫Or no empty entries in v’s view
u v
w
u v
w
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S&F: Compensating for Loss•Edges (view entries) disappear due to loss•Need to prevent views from emptying out•Keep the sent ids when too little ids in view
u v
w
u v
w
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S&F: Advantages over Other Protocols
•No bi-directional communication▫No complex bookkeeping▫Tolerates message loss
•Simple▫Amenable to formal analysis
Easy to implement
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•Proving all desirable properties▫Analytical: degrees distribution w/out loss
Used in setting duplication threshold▫Markov 1: degree distribution with loss▫Markov 2: Markov Chain of reachable global states
IID samples, Temporal Independence
•Hold even under (reasonable) message loss!
Key Contribution: Analysis
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Analytic Degree Distribution
•Similar (better) to that of a random graph•Validated by a more accurate Markov model
0
0.05
0.1
0.15
0.2
0 10 20 30 40Node indegree
BinomialS&F AnalyticalS&F Markov
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•Proving all desirable properties▫Analytical: degrees distribution w/out loss
Used in setting duplication threshold▫Markov 1: degree distribution with loss▫Markov 2: Markov Chain of reachable global states
IID samples, Temporal Independence
•Hold even under (reasonable) message loss!
Key Contribution: Analysis
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…
Node Degree Markov Chain
•Numerically compute the stationary distribution
Transitions without loss
Transitions due to loss
State corresponding to isolated node
outdegree0 2 4 6
inde
gree
0
1
2
3
…
…
…
…
…
…
…
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Results•Outdegree is bounded by
the protocol•Decreases with increasing
loss
• Indegree is not bounded• Low variance even under
loss•Typical overload at most 2x
0
0.05
0.1
0.15
0.2
0.25
0 20 40 60 80Node outdegree
loss=0loss=0.01loss=0.05loss=0.1
0
0.05
0.1
0.15
0.2
0.25
0 10 20 30 40Node indegree
loss=0loss=0.01loss=0.05loss=0.1
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•Proving all desirable properties▫Analytical: degrees distribution w/out loss
Used in setting duplication threshold▫Markov 1: degree distribution with loss▫Markov 2: Markov Chain of reachable global states
IID samples, Temporal Independence
•Hold even under (reasonable) message loss!
Key Contribution: Analysis
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Decay of Spatial Dependencies
• For uniform loss < 15%, dependencies decay faster than they are created
• 1 – 2loss rate fraction of view entries are independent▫ E.g., for loss rate of 3% more than 90% of entries are independent
u v
w
uv
w
u does not delete the sent ids
…
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Temporal Independence•Dependence on past views decays within
O(log n view size) time•Use “expected conductance”•Ids travel fast enough▫Reach random nodes in O(log n) hops▫Due to “sufficiently many” independent ids in views -
previous slide
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Conclusions•Formalized the desired properties of a
membership protocol•Send & Forget protocol▫Simple for both implementation and analysis
•Analysis under message loss▫Load balance▫Uniformity▫Spatial Independence▫Temporal Independence
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Thank You