LCR vs Floodmax in distributed computing (Leader Election)

Question

I am trying to understand the practical difference between LCR and Floodmax within the context of synchronous networks.

I understand that Floodmax has a time complexity of O(N) and in essence works as follows:

• Every process keeps the maximum UID it has seen so far (initially its own).
• At each round, each process sends this maximum value to every outgoing neighbor.
• After diam rounds if the maximum value is the process's UID then it elects itself the leader, otherwise it is a non-leader.

LCR on the other hand:

• Each process sends its UID around the ring. When a process receives a UID, it compares this one to its own.
• If the incoming UID is greater, then it passes this UID to the next process.
• If the incoming UID is smaller, then it discards it.
• If it is equal, then the process declares itself the leader.

It too has a time complexity of O(N). So, in essence, both algorithms pass around UIDs in a token-ring network. Is there any real difference or advantage between the two?

Answer 1

As the name implies, the FloodMax algorithm "floods" the network with messages. Unlike LCR, FloodMax will work even if the network topology is not a ring. A pre-requisite for the FloodMax algorithm is that the network diameter must be known (with LCR this is not the case) and has a time complexity of diameter-rounds. LCR on the other hand does not require the network diameter to be known: because of this it requires additional communication overhead as the leader needs to notify all other processes to terminate once it has elected itself.