Bringing Paxos Consensus in
Multi-agent Systems
Andrei Mocanu
Costin 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
50% Loss
Paxos Algorithm (3 Proposers, 5 Acceptors, 5 Learners)
30% Loss
10% Loss
18
Rounds until Consensus
16
14
12
10
8
6
4
2
0
1
6
11
16
21
26
31
36
41
46
51
56
Run Number
61
66
71
76
81
86
91
96
Dueling Proposers – Experiment
50% Loss
Paxos Algorithm (10 Proposers, 5 Acceptors, 5 Learners)
30% Loss
10% Loss
30
Rounds until Consensus
25
20
15
10
5
0
1
6
11
16
21
26
31
36
41
46
51
56
Run Number
61
66
71
76
81
86
91
96
Dueling Proposers – Experiment
10% Loss
Paxos Algorithm (3 Proposers, 15 Acceptors, 50 Learners)
30% Loss
50% Loss
20
18
Rounds until Consensus
16
14
12
10
8
6
4
2
0
1
6
11
16
21
26
31
36
41
46
51
56
Run Number
61
66
71
76
81
86
91
96
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
2.2
4.78
3 Proposer, 15 Acceptor, 50 Learner 1.63
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?