Bringing Paxos Consensus in Multi-agent Systems

1. Bringing Paxos Consensus in Multi-agent Systems Andrei Mocanu CostinBădică University of Craiova

2. What is consensus? • Agreement • No two processes decide differently • Termination • Every correct process eventually decides • Validity • The value that is decided must be among the values proposed by the processes

3. Why is consensus important? • Ensuring processes agree - fundamental problem in Distributed Computing • Applications in other problems: • leader election • state machine replication • atomic broadcast

4. Why is consensus hard? • Fischer-Lynch-Paterson Result • impossible to solve consensus in an asynchronous system in the presence of even a single failure. • No deterministic fault-tolerant consensus protocol can guarantee progress in an asynchronous network!

5. Paxos Algorithm • Developed in the 1980’s by Leslie Lamport, but paper rejected • Eventually published in 1998 and simplified in 2001 • Used in practice: • Google Chubby and Megastore • Microsoft Autopilot • Apache Hadoop Zookeeper

6. Paxos Algorithm • Safety Properties • value chosen is from proposed values • only one value is chosen • value is learned only if chosen • Paxos will eventually succeed if • a majority of participants is reachable • processes know how to generate values

7. Paxos Roles • Client • process that makes the request • Acceptor • represent the fault tolerant “memory” • organized in groups called Quorums • any message sent to an Acceptor must be sent to a Quorum of Acceptors • any message received from an Acceptor is ignored unless sent from each Acceptor in a Quorum

8. Paxos Roles • Proposer • acts on behalf of the Client • tries to assemble majority of Acceptors • Leader • a distinguished Proposer • many processes may believe themselves Leaders, but progress is made when only one of them is chosen

9. Paxos Roles • Learner • assure replication • once the decision has been received from the Acceptors, they take action and send the response to the Client • more may be added to increase availability

10. Paxos Phases • Phase 1a: Prepare • a Proposer (the leader) makes a proposal numbered n, greater than any proposal number used by that Proposer in the past • the Proposer sends a Prepare message containing n to a Quorum of Acceptors

11. Paxos Phases • Phase 1b: Promise • if n is higher than proposal numbers received so far by Acceptors, then it promises to ignore future proposals <n • if Acceptor had accepted proposal m<n, it will include proposal number m and value u • otherwise ignore proposal (or send NACK)

12. Paxos Phases • Phase 2a: Accept Request • if Proposer receives enough promises from the Quorum, it then chooses the maximum value received from Acceptors or a value it generates if none received • the Proposer sends that value to the Quorum in an Accept Request message

13. Paxos Phases • Phase 2b: Accepted • if Acceptor receives Accept Request message for proposal n • then it accepts proposal if it had not promised to accept only greater numbered proposals; it registers the value of the proposal and sends an Accepted message to the Proposer and Learners • Otherwise it ignores the Accept Request message (or NACK)

14. Paxos Example

15. Paxos Role Distribution

16. Collapsing Paxos Roles

17. Discovery - JADE Yellow Pages

18. Class Structure & Dependencies

19. Dueling Proposers Scenario

20. Dueling Proposers – Experiment

21. Dueling Proposers – Experiment

22. Dueling Proposers – Experiment

23. Experimental Results Summary

24. Conclusions • We demonstrate an implementation of Paxos using Multi-agent Systems • Leverage existing agents in the system to create fault-tolerant layer • Experimental analysis of an interesting Paxos edge case in JADE

25. Questions?