# Distributed Coordination Algorithms

## Summary

Distributed coordination algorithms are algorithms that enable multiple computers or nodes to coordinate with each other to complete tasks. These algorithms are used in distributed systems, where multiple computers are connected to a network and must communicate with each other to complete a task. Examples of distributed coordination algorithms include distributed consensus algorithms, leader election algorithms, distributed mutual exclusion algorithms, distributed scheduling algorithms, and distributed agreement algorithms.

This post documents code examples of some distributed coordination algorithms.

Note that some code samples have been taken from previous projects that I have worked. These can be found in my GitHub repository.

## Algorithms

The most popular distributed coordination algorithms are:

• Primary-backup Algorithm
• Quorum Consensus Algorithm
• Paxos Algorithm
• Raft Algorithm
• Vector Clock Algorithm
• Viewstamped Replication Algorithm
• Clock Synchronization Algorithm
• Token Ring Algorithm
• Distributed Mutual Exclusion Algorithm

## Primary-backup Algorithm

The primary-backup algorithm is a type of fault tolerance algorithm that ensures the reliability and availability of a system. It works by having a primary system (active) and a backup system (passive). The primary system performs the active tasks and the backup system monitors the primary system. If the primary system fails, the backup system takes over the active tasks, while the primary system is repaired or replaced.

In Node.js, a primary-backup in node.js can be implemented using the node-cluster module. The node-cluster module allows for multiple processes to be run in parallel and each process can be designated as either the primary or the backup. The primary process is responsible for handling all requests while the backup process monitors the primary process and takes over if the primary process fails. This ensures that the system remains available and that requests are not lost.

The following pseudo demonstrates an implementation.

``````//Pseudo
//Primary
WHILE (TRUE)
{
WAIT FOR REQUEST;
{
PROCESS REQUEST;
}
}

//Backup
WHILE (TRUE)
{
WAIT FOR REQUEST;
IF (NO RESPONSE FROM PRIMARY)
{
PROCESS REQUEST;
}
}``````

## Quorum Consensus Algorithm

The Quorum Consensus Algorithm is an algorithm used to reach consensus in private or permissioned blockchain networks. It is based on the Raft consensus algorithm and uses a voting system to ensure that only valid transactions are added to the network. Quorum is designed to provide a secure, high performance, and reliable solution for organizations to build applications on the blockchain. It uses advanced cryptography to ensure that the network remains private and secure.

The Quorum Consensus Algorithm is used in a variety of blockchain technologies, including Ethereum, Hyperledger Besu, and Corda.

``````//Microsoft .NET
using System;
using System.Collections.Generic;

public class QuorumConsensusAlgorithm
{
private int _quorumSize;
private int _nodes;

public QuorumConsensusAlgorithm(int quorumSize, int nodes)
{
_quorumSize = quorumSize;
_nodes = nodes;
}

public bool HasReachedConsensus()
{
}

public void RecordVote(int vote)
{
// Record the vote if it hasn't been recorded yet
{
}
}

{
}

{
}
}``````

## Paxos Algorithm

The Paxos Algorithm is a method for achieving consensus in a distributed system. It is a fault-tolerant algorithm that uses a voting process between multiple nodes to determine the next action. The algorithm is used to ensure that all nodes in the system agree on a single outcome or result. It is one of the most popular consensus algorithms in distributed systems.

``````//Microsoft .NET
using System;

public class PaxosAlgorithm
{
private int _n; //number of nodes
private int _k; //number of rounds
private int _i; //node index
private int _v; //value to be decided
private int _proposer; //node that proposed the value

//Constructor
public PaxosAlgorithm(int n, int k, int i, int v, int proposer)
{
_n = n;
_k = k;
_i = i;
_v = v;
_proposer = proposer;
}

//Paxos algorithm
public void RunPaxos()
{
//Round 1 - Propose
if (_i == _proposer)
{
}

//Round 2 - Promise
int maxVoted = 0;
for (int k = 0; k < _k; k++)
{
maxVoted = GetMaxVotedValue();
}

//Round 3 - Accept
if (_i == _proposer)
{
}

//Round 4 - Learn
int acceptedV = 0;
for (int k = 0; k < _k; k++)
{
acceptedV = GetAcceptedValue();
}
}

//Function to get the maximum voted value
private int GetMaxVotedValue()
{
//Code for getting the maximum voted value
//...
return 0;
}

//Function to get the accepted value
private int GetAcceptedValue()
{
//Code for getting the accepted value
//...
return 0;
}

{
//...
}

{
//...
}

{
//...
}

{
//...
}
}``````

## Raft Algorithm

The Raft Algorithm is a consensus algorithm for distributed systems, designed as an alternative to the Paxos algorithm. It is a consensus algorithm used in distributed systems to achieve fault tolerance and is based on an elected leader to manage the replication of log entries across the cluster. The leader coordinates with other nodes in the cluster to ensure that the same log entries are written to the same nodes. The algorithm is designed to be easy to understand and implement, and to provide safety and liveness guarantees.

``````//C Language
#include <stdio.h>
int main()
{
int row, col, i, j;

printf("Enter the number of rows and columns of matrix\n");
scanf("%d%d", &row, &col);

int arr[row][col];

printf("Enter the elements of the matrix\n");

for (i = 0; i < row; i++)
{
for (j = 0; j < col; j++)
{
scanf("%d", &arr[i][j]);
}
}

printf("The matrix is\n");
for (i = 0; i < row; i++)
{
for (j = 0; j < col; j++)
{
printf("%d\t", arr[i][j]);
}
printf("\n");
}

//Raft Algorithm
int raft[row][col];
int k, l;
for (k = 0; k < row; k++)
{
if (k % 2 == 0)
{
for (l = 0; l < col; l++)
{
raft[k][l] = arr[k][l];
}
}
else
{
for (l = col - 1; l >= 0; l--)
{
raft[k][l] = arr[k][col - l - 1];
}
}
}

printf("The Raft matrix is\n");
for (i = 0; i < row; i++)
{
for (j = 0; j < col; j++)
{
printf("%d\t", raft[i][j]);
}
printf("\n");
}

return 0;
}``````

## Vector Clock Algorithm

The Vector Clock Algorithm is a distributed algorithm used to assign a logical clock to each process in a distributed system. The algorithm provides a way to keep track of causality between processes in a distributed system and the order of events between them. The vector clock assigns an integer to each process and is updated each time a message is exchanged. This allows each process to determine the causal ordering of events by comparing the vector clock values of the messages it receives.

``````//JavaScript
//Create a Vector Clock Class
class VectorClock {
constructor(size){
this.size = size;
this.clock = new Array(size).fill(0);
}

//Function to increment clock
increment(index){
if(index < this.size){
this.clock[index] += 1;
}
}

//Function to compare two clocks
compare(other){
let result = 0;
for(let i = 0; i < this.size; i++){
if(this.clock[i] < other.clock[i]){
result = -1;
} else if(this.clock[i] > other.clock[i]) {
result = 1;
}
}
return result;
}

//Function to merge two clocks
merge(other){
for(let i = 0; i < this.size; i++){
if(this.clock[i] < other.clock[i]){
this.clock[i] = other.clock[i];
}
}
}
}``````

## Viewstamped Replication Algorithm

``````//Microsoft .NET
using System;

public class ReplicationAlgorithm
{
public static void Main()
{
// Create a random number generator
Random randomNumberGenerator = new Random();

// Store the number of replicas we need
int numberOfReplicas = 5;

// Create an array to store replica values
double[] replicas = new double[numberOfReplicas];

// Generate random numbers for each replica
for (int i = 0; i < numberOfReplicas; i++)
{
replicas[i] = randomNumberGenerator.NextDouble();
}

// Output the replicas
Console.WriteLine("Generated Replicas:");
for (int i = 0; i < numberOfReplicas; i++)
{
Console.WriteLine(replicas[i]);
}
}
}``````

## Clock Synchronization Algorithm

Clock Synchronization Algorithm is an algorithm used to synchronize the clocks of two or more computers. It is designed to measure the difference in time between two or more clocks and adjust the clocks accordingly. This algorithm is used in distributed systems to ensure the accuracy of data transfer and communication among different computers.

Clock synchronization algorithms are important for network communication systems because they help ensure that the time on the clocks of all the nodes in the network is synchronized. This is essential for proper communication, as time-sensitive messages must be sent and received in order for the system to work properly. Without clock synchronization, the nodes in the network could be sending and receiving messages at different times, leading to confusion and errors. Clock synchronization algorithms help to avoid these problems by ensuring that all the nodes in the network have the same time.

Leader election is an algorithm used to elect a single process as the leader amongst a group of distributed processes. It is used in distributed computing where a group of processes needs to agree on a single leader among them. Each process can communicate with the other processes in the group and sends messages to each other. The algorithm is designed to select one process as the leader and the other processes will follow it. The algorithm can use various methods to decide which process will be the leader. Popular methods include the ring algorithm and the bully algorithm.

``````// Algorithm to implement a leader election (in C#):

// Step 1: Define a class to represent the candidate
public class Candidate
{
// Candidate ID
public int Id { get; set; }

// Candidate name
public string Name { get; set; }

// Flag to indicate if the candidate is the leader
public bool IsLeader { get; set; }

}

// Step 2: Create a list of candidates
List<Candidate> candidates = new List<Candidate>();

// Step 3: Initialize the list of candidates
candidates.Add(new Candidate { Id = 1, Name = "John", IsLeader = false });
candidates.Add(new Candidate { Id = 2, Name = "Mary", IsLeader = false });
candidates.Add(new Candidate { Id = 3, Name = "David", IsLeader = false });

// Step 4: Define a method to conduct the leader election
public void ConductElection()
{
// Step 4.1: Find the candidate with the highest ID
Candidate leaderCandidate = candidates.OrderByDescending(c => c.Id).FirstOrDefault();

// Step 4.2: Set the IsLeader flag to true

// Step 4.3: Print the leader
}``````

## Token Ring Algorithm

The token ring algorithm is a computer networking protocol designed to allow computers in a local area network (LAN) to communicate with each other in a particular order. It is based on the concept of passing a specific token from one node to another in a ring topology. The token is a special type of data packet and it is used to control access to the network. The token ring protocol is designed to ensure that only one node at a time has access to the network, thus preventing data collisions and ensuring that messages are sent and received in the correct order.

``````# Python example
import random

# Initialize the nodes in the ring
nodes = []

# Create some nodes in the ring
for i in range(10):
nodes.append(Node(i))

# Token is passed along the ring
token = None

while True:
# Pick a random node to start from
current_node = random.choice(nodes)

# Pass the token along to the next node
next_node = current_node.next_node(nodes)

# The token is now at the next node
token = next_node.token

# Stop if the token has made a full round
if token == None:
break``````

## Distributed Mutual Exclusion Algorithm

Distributed Mutual Exclusion Algorithm (DMEA) is a distributed algorithm that allows multiple processes to access shared resources in a mutually exclusive manner, meaning only one process can access the resource at any given time. The algorithm guarantees that no two processes will access the same resource simultaneously, preventing race conditions and ensuring system consistency. DMEA is based on the Lamport’s mutual exclusion algorithm, and uses distributed messages, timers, and other techniques to achieve mutual exclusion in a distributed system.

``````# Python example
# Process 0

while (true):
#Request Critical Section
send_request()

#Wait for response

#Enter Critical Section
critical_section()

#Release Critical Section
send_release()

#Noncritical Section
noncritical_section()

# Process 1

while (true):
#Wait for Request

#Check if can enter Critical Section
if (can_enter_critical_section()):
#Send response
send_response()

#Wait for release