Tangaroa, is a Byzantine tolerant Raft implementation in Haskell, developed by Christopher Copeland and Hongxia Zhong for a Distributed Systems class at Stanford. The authors apply many of the approaches in PBFT to Raft, allowing for the Byzantine failure of nodes.  My interest in this work is how can you stop unnecessary leader elections in Raft algorithm using more conservative election approaches, given that election are too common in some environments (see s4.2 in ARC) .

Copeland and Zhong quickly identify this is one of the key barriers to developing a byzantine raft and describe their lazy voters approach. As with the original Raft protocol, a node time out, becomes a candidate, increments its terms and dispatches RequestVotes RPC’s. In addition to the normal restrictions on granting a vote, Tangaroa adds two extra conditions

• new term must be < current_term + H, where H is a statistically unlikely number of split votes.
• the current leader must have failed to dispatch AppendEntries within the heartbeat.

I am not convinced that the first condition will not lead the system to become permanently unavailable. For example, a network partition could separate the nodes into two groups thus one group will have a leader in a low term and the other group constantly trying to re-elect a new leader as it cannot get a majority. When network connectivity is restored, the nodes in will observe a sudden unbounded term increase. Thus nodes will not give out their votes and the system is stalled.

The author’s highlight how the second condition could be implemented. Nodes who are in a position to grant a vote except that the second condition isn’t meet, will record the vote locally and then dispatch the votes only when they observe a leader failure for themselves. This means that a node may only replace the leader when a majority of nodes detect a failure (or the client does) instead of when a single node detects a failure. The paper doesn’t go into much detail about this and how this would impact performance, I suspect that this approach is a little too conservative and many in some cases make it significantly more difficult to replace a fault leader. Various implementation details need to be consider here, for example, if a node times out and dispatches its vote, how long until the node steps up to candidate itself? or after how long does it “forget” its local vote, the election timeout maybe.

Writing a Byzantine Fault Tolerant Raft was always going to be hard, Tangaroa is a good first step in the right direction.