CS 5204 – Operating Systems1 Paxos Student Presentation by Jeremy Trimble

Paxos CS 5204 – Operating Systems2 Overview Distributed State Machine Distributed Consensus Fault-tolerance Requirements/Assumptions Paxos Algorithm Concepts The Algorithm Itself Examples Multi-Paxos Variations of Paxos

Paxos CS 5204 – Operating Systems3 Distributed State Machine Fault-tolerance through replication. Need to ensure that replicas remain consistent. Replicas must process requests in the same order.

Paxos CS 5204 – Operating Systems4 The Distributed Consensus Problem In a distributed system, how can we: Select a single action among many options?  How can this be done in a fault-tolerant way? Simple solution: A single node acts as the “decider.”  But this is not fault tolerant. (What if the decider fails?) A better solution: Paxos

Paxos CS 5204 – Operating Systems5 Fault-Tolerant Consensus Requirements Safety  Only a value that has been proposed may be chosen.  Only a single value is chosen.  A process never learns that a value has been chosen until it actually has been. Goals Liveness  FLP Impossibility Proof (1985)

Paxos CS 5204 – Operating Systems6 Assumptions Failures “Fail Stop” assumption  When a node fails, it ceases to function entirely.  May resume normal operation when restarted. Messages  May be lost.  May be duplicated.  May be delayed (and thus reordered).  May not be corrupt. Stable Storage

Paxos CS 5204 – Operating Systems7 Proposal An alternative proposed by a proposer. Consists of a unique number and a proposed value. ( 42, B ) We say a value is chosen when consensus is reached on that value. Paxos Terms Proposer Suggests values for consideration by Acceptors. Advocates for a client. Acceptor Considers the values proposed by proposers. Renders an accept/reject decision. Learner Learns the chosen value. In practice, each node will usually play all three roles. A1 P1

Paxos CS 5204 – Operating Systems8 Strong Majority “Strong Majority” / “Quorum” A set of acceptors consisting of more than half of all acceptors. Any two quorums have a nonempty intersection. Helps avoid “split-brain” problem. Acceptors decisions are not in agreement. Common node acts as “tie-breaker.” In a system with 2F+1 acceptors, F acceptors can fail and we'll be OK. Quorums in a system with seven acceptors. A1A6 A4 A5 A7 A3 A2

Paxos CS 5204 – Operating Systems9 Consensus time (N 5, V 3 ) (N 3, V 3 ) (N 2, V 2 ) (N 4, V 1 ) (N 1, V 1 ) (N 6, V 3 ) consensus reached, V 3 chosen (N 7, V 3 ) Values proposed by proposers are constrained so that once consensus has been reached, all future proposals will carry the chosen value. P2c. For any v and n, if a proposal with value v and number n is issued, then there is a set S consisting of a majority of acceptors such that either: (a) no acceptor in S has accepted any proposal numbered less than n, or (b) v is the value of the highest-numbered proposal among all proposals numbered less than n accepted by the acceptors in S. (N 2, V 2 ) A1 A2 A3 A4 A5

Paxos CS 5204 – Operating Systems10 Phase 1a:“Prepare” Select proposal number* N and send a prepare(N) request to a quorum of acceptors. Phase 1b:“Promise” If N > number of any previous promises or acceptances, * promise to never accept any future proposal less than N, - send a promise(N, U) response (where U is the highest-numbered proposal accepted so far (if any)) Phase 2a:“Accept!” If proposer received promise responses from a quorum, - send an accept(N, W) request to those acceptors (where W is the value of the highest-numbered proposal among the promise responses, or any value if no promise contained a proposal) Phase 2b:“Accepted” If N >= number of any previous promise, * accept the proposal - send an accepted notification to the learner Acceptor Proposer Basic Paxos Algorithm * = record to stable storage

Paxos CS 5204 – Operating Systems11 A1 P1 A2A3 time start promise(1) promise(1, -) prepare(1) accept(1, A) prepare(1) accepted(1, A)

Paxos CS 5204 – Operating Systems12 A1 P1 A2A3 time P2 prepare(1) accepted(1, A) prepare(1) prepare(2) promise(2, -) promise(2, (1,A)) prepare(1) accept(2, A) accepted(2, I) accepted(2, A) continued...

Paxos CS 5204 – Operating Systems13 A1 P1 A2A3 time promise(1) prepare(1) prepare(2) accepted(1, C) P2 prepare(1) accept(1, C) prepare(1) promise(2, -) prepare(1) accept(2, D) accepted(1, I) accepted(2, D) start

Paxos CS 5204 – Operating Systems14 A1 P1 A2A3 time promise(1) prepare(1) prepare(2) accepted(1, F) P2 prepare(1) accept(1, F) prepare(1) promise(2, -) prepare(1) prepare(3)... prepare(1) prepare(4) prepare(1) prepare(2)... start

Paxos CS 5204 – Operating Systems15 Other Considerations Liveness Can't be guaranteed in general. Distinguished Proposer  All proposals are funneled through one node. Can re-elect on failure. Learning the Chosen Value Acceptors notify some set of learners upon acceptance. Distinguished Learner A node may play the role of both distinguished proposer and distinguished learner – we call such a node the master.

Paxos CS 5204 – Operating Systems16 Multi-Paxos A single instance of Paxos yields a single chosen value. To form a sequence of chosen values, simply apply Paxos iteratively. To distinguish, include an instance number in messages. Facilitates replication of a state machine. “time” instance chosen valueSP???O

Paxos CS 5204 – Operating Systems17 Paxos Variations Cheap Paxos Reconfiguration  Eject failed acceptors.  Fault-tolerant with only F+1 nodes (vs 2F+1).  Failures must not happen too quickly. Fast Paxos Clients send accept messages to acceptors. Master is responsible for breaking ties. Reduces message traffic. Byzantine Paxos Arbitrary failures – lying, collusion, fabricated messages, selective non-participation. Adds an extra “verify” phase to the algorithm.

Paxos CS 5204 – Operating Systems18 Conclusion State-Machine Replication Distributed Consensus Basic Paxos Examples Optimizations Variations

Paxos CS 5204 – Operating Systems19 Questions?

Paxos CS 5204 – Operating Systems20 References Paxos Made Simple Paxos Made Live Wikipedia – Paxos Algorithm The Byzantine Generals Problem Impossibility of distributed consensus with one faulty process