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Message broadcast

Last updated May 5, 2022

# Ordering

# Total order broadcast

Globally consistent broadcast, agreement from all nodes (hard but can be done with consensus algorithms like Raft!)

In state machine replication, total order broadcast assumes the state update function is deterministic. That is, whenever two replicas are in the same state, giving them the same input, they will transition to the same next state. The main limitation is that total order broadcast cannot update state immediately, have to wait for delivery through broadcast

# Causal Broadcast

Obeys happens-before (causal) relationships.

In state machine replication, assumed state update function is deterministic and concurrent updates are commutative. Replicas can process updates in different orders and still end up in the same state

# FIFO (reliable) Broadcast

Messages sent by the same node must be delivered in the order they were sent

Assumes state update function is deterministic + all updates are commutative.

# Best-effort

No ordering guarantees.

Assumes state update function is deterministic + commutative + idempotent + tolerates message loss

# Reliability

Nodes can die mid-transmission!

Two strategies for mitigating node-death:

  1. Eager reliable broadcast: first time a node receives a message, re-broadcast to each other node (reliable but expensive! $O(n^2)$ messages for $n$ nodes)
  2. Gossip: first time a node receives a message, forward it to $k$ other nodes, chosen randomly (reliable with high probability)

# Retry semantics

Interactive Graph