Implementation of the Pastry (paper) and Chord (paper) DHT Protocols, as part of assignment for course COL819 - Spring 2020 at IIT Delhi.
.
├── chord_node.py
├── chord.py
├── LICENSE
├── links.dat
├── modules
│ ├── __ init__.py
│ └── network.py
├── pastry_node.py
├── pastry.py
└── README.md
For further details, check the docstring in each of the python files.
>>> python pastry.py <num-nodes-in-network> <whether-to-read-network-configuration-from-file (0/1)>The default parameters are as follows:
l = 6 # Length of hash code
b = 4 # Parameter to set Leaf Set and Neighborhood Set
num_points = 10000 # Number of data points in Pastry
num_queries = 1000000 # Number of queries made
To change the parameters, go to pastry.py
>>> python chord.py <num-nodes-in-network> <whether-to-read-network-configuration-from-file (0/1)>The default parameters are as follows:
l = 6 # Hashing scheme for Nodes (Not required for Chord DHT, but to verify correctness)
m = 24 # Number of entries in Finger Table
num_points = 10000 # Number of data points to store in Chord
num_queries = 1000000 # Number of queries made on the DHT
To change the parameters, go to chord.py
Both of the services, Pastry and Chord are implemented, using an underlying network simulation, with a definite measure of geographical distance (or proximity metric) between the nodes. The Network has been simulated by keeping a graph of interconnected vertices. Each vertex may logically correspond to a Pastry/Chord Node. The physical distance between two Pastry/Chord Nodes is hence kept as the distance between the corresponding vertices in the network graph.
At each run, a new Network is initiated and the links (or edges) are stored in a file links.dat. The network configuration can be read from this file in a subsequent run, by specifying the respective argument. See the section How to run.
The Network class implemention and code can be found in the modules.network script. The Network Class utilizes the Node class definition, which is the base class for the PastryNode and ChordNode classes, used in Pastry and Chord respectively.
The Network instance has been used for three major functions:
def get_node(self, node_id):
"""Get the node at node id on the nodes array
Arguments:
node_id {Integer} -- Hash of the node
Returns:
Node -- Associated Node
"""
return self.nodes[node_id]
def is_alive(self, n):
"""Checks if a given node n is alive in the network
Arguments:
n {Integer} -- Node Id of the node
Returns:
Boolean -- True if node is alive else False
"""
if n in self.nodes:
return True
return False
def proximity(self, n1, n2):
"""Define the proximity metric between two Node instances on the network
Currently using the modulo additive inverse for the metric
Arguments:
n1 {Integer} -- Hash of node n1
n2 {Integer} -- Hash of node n2
Returns:
Integer -- the proximity metric (Returns -1 if node not alive)
"""
try:
# ... Code to get the distance between the associated logical vertices in the graph
return distance
except:
return -1Feel free to fork, make your changes and submit a pull request on this repo.
This project is licensed under the MIT License - see the LICENSE.md file for details