Bringing Paxos Consensus in Multi-agent Systems

Andrei MocanuCostin Bădică

University of Craiova

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

Why is consensus important?

Ensuring processes agree - fundamental problem in Distributed Computing

Applications in other problems: leader election state machine replication atomic broadcast

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!

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

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

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

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

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

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

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)

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

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)

Paxos Example

Paxos Role Distribution

Collapsing Paxos Roles

Discovery - JADE Yellow Pages

Class Structure & Dependencies

Dueling Proposers Scenario

Dueling Proposers – Experiment

Dueling Proposers – Experiment

Dueling Proposers – Experiment

Experimental Results Summary

Configuration/Message Loss 10% 30% 50%

3 Proposer, 5 Acceptor, 5 Learner

1.42 2.18 3.83

10 Proposer, 5 Acceptor, 5 Learner

1.47 2.48 6.36

3 Proposer, 15 Acceptor, 50 Learner

1.63 2.2 4.78

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

Questions?