The document discusses the gossip protocol in computer science, which facilitates peer-to-peer communication by mimicking the way rumors spread in society. It highlights the protocol's advantages, including scalability, fault tolerance, and robustness, and outlines its applications in areas like failure detection, database replication, and cluster membership. Additionally, it reviews key models and properties of gossip protocols, and mentions specific implementations and applications such as the SWIM protocol and associations with systems like Cassandra and AWS.

1. Gossip protocol and applications
Tu Nguyen
Staff Software Engineer - Axon

2. Gossip protocol

4. Gossip in computer science
A peer-to-peer communication protocol●
Inspired by epidemics, human gossip and social networks (spreading rumors)●
epidemic protocol (synonym)■
why ?■
rumors or epidemics in society travel at a great speed and reach to almost every member of the community
without needing a central coordinator.
●
Gossip was founded originally to solve Multicast problem●
Multicast●
we want to communicate a message to all the nodes in the network■
each node sends the message to only a few of the nodes■
Multicast problems ?●
Fault-tolerance: node might crash, packet might be dropped, etc○
Scalability: millions, hundreds of millions of nodes○
Centralized: single sender “multi-cast” TCP/UDP packets to others.○
Tree-based multicast: too much redundancy with ACK/NACK msg.○
Multicast was originally heavily used in network devices (eg. routers); how to leverage it in application layer ?○

5. Gossip basic
A node wants to share some information to the other nodes in the network. Then periodically it
selects randomly a node from the set of nodes and exchanges the information. The node that
receives the information does exactly the same thing.
Cycle●
number of rounds to spread the information■
Fanout●
number of nodes that a node “gossip” within each cycle■

6. Gossip properties
Node selection must be random (or guarantee enough peer diversity)●
Node only stores local information. There is no shared global state.●
Communication is round-based (periodic).●
Transmission and processing capacity per round is limited.●
All nodes run the same protocol.●
Not deterministic (because of randomness peer sampling).●

7. Advantages of Gossip
Scalable●
Fault-tolerance●
Robust●
Decentralized●
Convergent consistency●

8. Gossip modeling
Consider a distributed network where nodes are message-passing to each
other.
State of a node●
Susceptible - node has not received update yet (is not infected).■
Infected - node with an update it is willing to share.■
Removed - node has received the update but is not willing to share.■
Two basic models●
SI (anti-entropy)■
SIR (rumor-mongering)■
When R state happens ?
👉 Many algorithms. One of them are counting for redundant messages.

9. Gossip modeling
Push / Pull / Push-Pull●
Push■
I nodes are the ones sending/infecting S nodes●
efficient when there are a few updates.●
Pull■
all nodes are actively pulling for updates●
efficient when there are many updates.●
Push-Pull■
node pushes when it has updates and also pulls for new updates●
node and selected node are exchanging information ●

10. Gossip modeling

11. https://flopezluis.github.io/gossip-simulator/

12. Gossip Applications

13. Applications
Cluster membership●
Information dissemination●
Failure detection●
Database replication●
Overlay network●
Aggregations●

14. Cluster Membership
Who are my live peers ?
Desired properties
Connectedness●
Balance●
Short path-length●
Reducing redundancy●
Scalability●
Accuracy●
Full Partial

15. Full Partial
👍 Connectedness
👍 Short-path length
👌 Accuracy
👌 Balance
👎 High redundancy
👎 Low scalability
👌 Connectedness
👌 Short-path length
👌 Accuracy
👌 Balance
👍 Low redundancy
👍 High scalability
Cluster Membership
✅

16. SWIM - Cornell University 2002●
SCAMP - Microsoft Research 2003●
CYCLON - Vrije University, The Netherlands, 2005●
HYPARVIEW - University of Lisbon, 2007●
Cluster Membership

17. SWIM - Cornell university (2002)
Scalable Weakly-consistent Infection-style Process Group
Membership
https://www.cs.cornell.edu/projects/Quicksilver/public_pdfs/SWIM.pdf
Properties
Scalable●
Weakly consistent●
Infection-style●
Membership protocol●

18. SWIM
Motivated by traditional heart-beating●
every interval T, notify peers of liveness■
if no update received from peer P after T * limit, mark P as dead.■
heart-beat = membership + failure detection■
Heart-beat is doing good at:●
completeness - yes!■
strong completeness - every crashed node is eventually detected by all correct
nodes.
●
Accuracy - high!■
Heart-beat problems ?●
Network load: N^2■

19. SWIM is trying to ...
Separate two problems and solve them one-by-one●
Failure detection (👉 “live” peers)○
Membership protocol (👉 list of peers)○
Optimization●
Reduce network load○
Failure detection○
decrease processing time●
increase accuracy●

20. Failure Detection properties
One step back...●
The two properties of a distributed system□
Safety - nothing bad ever happens○
Liveness - something good eventually happens.○
Failure Detection properties●
Completeness (L) - failure detector would find the node(s) that finally crashed in the
system.
□
Accuracy (S) - correct decisions that the failure detector has made in a node.□

21. Failure Detection properties
Degree of completeness●
depends on number of crashed nodes is suspected by a failure detector in a certain
period
□
Strong completeness - every faulty node is eventually permanently suspected by every non-
faulty node
○
Weak completeness - every faulty node is eventually permanently suspected by some non-faulty
node
○
Degree of accuracy●
depends on number of mistakes that a failure detector made in certain period□
Strong accuracy - no node is suspected (by any node) before it crashes○
Weak accuracy - some non-faulty node is never suspected○
Eventual strong accuracy - after some time, system becomes strong accuracy.○
Eventual weak accuracy - after some time, system becomes weak accuracy.○

22. SWIM Failure Detection
Each node in set of N node●
Choose a random peer○
Ping - ACK□
Indirect Ping (iff no ACK)○
Choose k random peers□
indirect Ping○
Evaluation:
completeness: every nodes will be pinged!●
accuracy: “high” (🔍)●
speed of detection: 1 * Interval●
network load: (4*k + 2) * N ~ 0(N)●

23. SWIM Membership Protocol
Aware of join / leave nodes●
Motivated by Gossip●
Piggy-back approach■
Infection-style○
ping is sent to random peer□
eventually (weakly) consistent□
updates send peer-to-peer□

24. SWIM - Optimization
Suspicion state - to improve accuracy
Trade-off between failure detection time and false positives.●
Introduce suspicion state.●
A 👉 B: Ping! Suspect C failed■
B 👉 A: ACK!■
A few moment later■
A, B 👉 C: Ping! Are you dead ?□
C 👉 A,B: ACK! (i’m not 😋)□
State FSM

25. SWIM - Optimization
Round-robin probe peer selection
Randomly sort peer set■
Ping in round-robin order■
Evaluation:
Completeness: increase, time-bounded○
State FSM

26. SWIM - Limitations
Node leave vs fail●
Re-joining●
Event ordering●
Message encryption●
Peer metadata●
Custom payload●
Network participants●
More details: https://www.cs.cornell.edu/projects/Quicksilver/public_pdfs/SWIM.pdf

27. SWIM - Implementation
memberlist https://github.com/hashicorp/memberlist●
serf, consul, etcd are relying on swim-based memberlist for failure detection and group
membership.
●

28. Other “announced” applications
Cassandra internal - understand gossip https://www.youtube.com/watch?v=FuP1Fvrv6ZQ●
AWS S3 gossip http://status.aws.amazon.com/s3-20080720.html●
Slicing structured overlay network
T-MAN https://www.researchgate.net/publication/225403352_T-Man_Gossip-
Based_Overlay_Topology_Management
●
https://managementfromscratch.wordpress.com/2016/04/01/introduction-to-gossip●