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12/09/01 1 SCAMP: lightweight membership service for gossip- based protocols Ayalvadi Ganesh, Anne-Marie Kermarrec & Laurent Massoulié Microsoft Research.

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Presentation on theme: "12/09/01 1 SCAMP: lightweight membership service for gossip- based protocols Ayalvadi Ganesh, Anne-Marie Kermarrec & Laurent Massoulié Microsoft Research."— Presentation transcript:

1 12/09/01 1 SCAMP: lightweight membership service for gossip- based protocols Ayalvadi Ganesh, Anne-Marie Kermarrec & Laurent Massoulié Microsoft Research Cambridge, UK

2 12/09/012 Probabilistic multicast Scalable and reliable group communication Gossip-based algorithms Scalable Reliable Probabilistic guarantees Graceful degradation in the presence of failure Non scalable membership protocol

3 12/09/013 Background: gossip-based protocol 0 1 2 5 7 6 3 9 4 8 Fanout = k Probability of reliable delivery : P = exp(-exp(-s)) If k = log n + s

4 12/09/014 Background: gossip-based protocol 0 2 5 7 6 9 8 1 3 4

5 12/09/015 Membership Global knowledge Storage and consistency maintenance Partial knowledge requires coordination Fanout setting to achieve a given probability of reliable delivery Fanout update

6 12/09/016 Scamp: Self-organizing membership protocol Partial knowledge of the membership: local view Fanout automatically set = size of the local view Fanout evolves naturally with the size of the group Size of local views converges towards clog(n)

7 12/09/017 Scamp: Subscription (c=1) View sizes automatically converge to log(n) Gossip protocol: Each node gossips to all the members of its view

8 12/09/018 Subscription algorithm Ser s s s P=1/sizeof view (1-P) P=1/sizeof view (1-P) Subscription forwarded S Subscription to a random member s s s s s s View sizes automatically converge to log(n) Each node gossips to all the members of its view

9 12/09/019 Subscription algorithm 0 1 5 4 6 1 4 5 6 6 6 0 Local view 7 6 2 87 7 2 8 3 6 3 6 7 0 15 6 6 6 6

10 12/09/0110 Lease on subscriptions Lease associated with each subscription Periodically nodes have to re-subscribe Nodes having failed permanently will time out Re-balance the partial views

11 12/09/0111 Unsubscriptions Unsubscription broadcast with local view When a node removes the unsubscribed node, it picks up randomly another node to incorporate in its view

12 12/09/0112 Unsubscriptions 0 1 5 4 1 4 5 Unsub (0), [1,4,5] Local view z x y 8 9 0 7 3 0 6 0 2 8 9 4 x y z 7 3 5 6 0 1

13 12/09/0113 Theoretical Analysis System modelled as random directed graph D(N) = Average out-degree for N-nodes system Subscription adds D(N)+1 directed arcs, so (N+1) D(N+1) = N D(N) + D(N)+1 Solution of this recursion is D(N)=D(1)+1/2+1/3+…+1/N Log(N)

14 12/09/0114 Scamp:Experiments Simulation results Convergence of view size Confirm theoretical analysis Impact of lease Reliability Comparison with traditional gossip-based Attest to the good quality (uniformity) of views

15 12/09/0115 Distribution of view size Log=12.2 Log=13.12

16 12/09/0116 Impact of lease 5000 nodes 0 100 200 300 400 500 600 700 800 147101316192225 View size Number of nodes mean=7.53 mean=8.47

17 12/09/0117 Reliability: 5000 node system 2500 0.9 0.92 0.94 0.96 0.98 1 0500100015002000 Number of failures Reliability SCAMP Global membership knowledge, fanout=8 Global membership knowledge, fanout=9

18 12/09/0118 Conclusion Membership protocol Scalable: local view Self-organizing: truly peer-to-peer Local views naturally converge towards the right value without global directive Theoretical analysis and simulations On going work Integration of locality

19 12/09/0119 Centralized subscriptions

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