Download presentation

Presentation is loading. Please wait.

Published byDandre Bramley Modified over 2 years ago

1
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

2
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

3
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

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

5
5 Uneven Workload In-degree distribution leads to uneven workload

6
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?

7
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]

8
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)

9
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

10
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

11
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

12
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 )

13
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

14
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

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

16
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

17
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

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

Similar presentations

OK

Revisting Random Key Pre-distribution Schemes for Wireless Sensor Network By Joengmin Hwang and Yongdae Kim, Computer Science and Engineering, University.

Revisting Random Key Pre-distribution Schemes for Wireless Sensor Network By Joengmin Hwang and Yongdae Kim, Computer Science and Engineering, University.

© 2017 SlidePlayer.com Inc.

All rights reserved.

Ads by Google

Ppt on cse related topics about psychology Ppt on project financing in india Ppt on induced abortion Ppt on production function Ppt on earth day Ppt on area of parallelogram and triangles geometry File type ppt on cybercrime virus Ppt on endangered species of birds Ppt on linear programming in operations research Ppt on file system in unix grep