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JetStream: Achieving Predictable Gossip Dissemination by Leveraging Social Network Principles Jay A. Patel 1, Indranil Gupta 1, and Noshir Contractor 2 1 Dept. of Computer Science 2 Dept. of Speech Communication University of Illinois at Urbana-Champaign

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2 “Flat” Gossip Network of n nodes A node desires to multicast a message m Each “infected” node gossips to l other randomly selected nodes (i.e., targets) Message reaches all w.h.p if l = log( n ) –[Kermarrec:TPDS:03] c f d a b e g h i h

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3 Random Overlay Selecting l random targets out of n nodes –Membership protocols SCAMP [Ganesh:TOC:03] SWIM [Das:DSN:02] CYCLON [Voulgaris:JNSM:05] Others

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4 Non-uniform In-degree Distribution Constant out-degree: Gaussian distribution for in-degree High Variance

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5 Uneven Workload In-degree distribution leads to uneven workload

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6 Gossip Summary Decentralized process +Resilient: no single point of failure +Balanced: everyone contributes +Fast: parallel transmission Area of improvements –Uneven workload –Cost: total message overhead is n * l –Speed: may be improved?

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7 Social Network Theories Reciprocity –“Mutual Interest” –Reduce messages –Even workload Structural Holes –“Complimentary Interest” –Improve speed Different from previous work -[Marti:IPTPS:03] -[Bernstein:IPTPS:03]

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8 Utilitarian Model Utility is a strictly “local” concept Calculate utility based on current target set x ij is a boolean value –Represents a link from node i to node j Reciprocity Structural Holes Net Utility Maximum utility: l * ( l - 1) 2 Recall: l is the out-degree (or gossip fan out)

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9 JetStream Algorithm: “Global” Start with random overlay Calculate node’s utility De-link random node Iterate through membership list –Replacement candidates improve or maintain utility Once per time period –Gradual “evolution” c f d a b e g h i j Node a’s target set: {d, f, i} Node a’s local utility: 2 Randomly selected de-link node: Node d Iterate through membership list: {b, c, e, g, h, j} Replacement candidate list: {e, g} Node a’s new target set: {e, f, i} Node a’s new local utility: 3

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10 JetStream Overlay “Evolution” Overlay converges after certain time –Converges implies no more target set changes –Emergent behavior Global reciprocity: No variance in in-degree Structural holes satisfied

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11 From Randomized to Deterministic n =100, l =5 Overlay converges –Each node achieves (close to) max utility –“Globally optimal” state through local, greedy decisions –No variance in in- degree –Note: n * l must be even

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12 Localized Implemenation Global doesn’t scale in large networks –O( n * l ) memory and O( n * l 2 ) computational overhead Localized: limited knowledge –Candidate list (replacement candidates): s Superset of target set Complete information As few as s =2* l Timeout mechanism: Candidate list node removed after t out –Network node list: lazy discovery –Overheads -- computation: O(2* l 3 ), memory: O(2* l 2 )

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13 Localized Implementation n =5000, l =10 Overlay stabilizes rapidly –does not “converge” –close to convergence –90+% nodes optimal (i.e., max utility) “Suboptimal” nodes also close to max utility

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14 JetStream: Gossip Workload n =5000, l =10 Fairer Workload –Much smaller range for workload –Node with highest workload JetStream: 16 Random: 35 Chord: 55 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25+ JetStream: Low Variance

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15 JetStream Macro Efficiency JetStream is 25% faster, 40+% fewer total messages

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16 Why “JetStream”? Continuous Gossip Background traffic to maintain target sets –Stable “noise” –Grows logarithmically For n =5000, l =10 –Approx. 0.4 packets per iteration –24 bytes/sec (at 60 bytes/packet) Continuous I thresh amount of gossip –I thresh = 4.8 bytes/sec –Lower net traffic

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17 Conclusion Based on simple social network principles –Social network principles “uniformizes” gossip “Fairer” Workload: net reduction by over 40% Faster: over 25% speedier dissemination Feasible for real systems –Local, greedy approach is sufficient –Churn adaptable, resilient, low overhead

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18 Performance with Churn Overnet Traces –[Bhagwan:IPTPS:03] –Real P2P traces –2 hours Gossip messages reach close to 100% of nodes

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