In the lectures, you learned about Distance Vector (DV) routing protocols, one of the two
classes of routing protocols. DV protocols, such as RIP, use a fully distributed algorithm that
finds shortest paths by solving the Bellman-Ford equation at each node. In this project, you will
develop a distributed Bellman-Ford algorithm and use it to calculate routing paths in a network.
This project is similar to the Spanning Tree project, except that we are solving a routing
problem, not a switching problem.
In “pure” distance vector routing protocols, the hop count (the number of links to be traversed)
determines the distance between nodes. However, some distance vector routing protocols that
operate at higher levels (like BGP) must make routing decisions based on business relationships
in addition to a hop count. These protocols are sometimes referred to as Path Vector protocols.
We will explore this by using weighted links (including negatively weighted links) in our network
We can think of Nodes in this simulation as individual Autonomous Systems (ASes), and the
weights on the links as a reflection of the business relationships between ASes. Links are
directed, originating at one Node, and terminating at another.
Part 0: Getting Started
You should review some materials on Bellman-Ford. Some resources include:
• Wikipedia (https://en.wikipedia.org/wiki/Bellman%E2%80%93Ford_algorithm)
• “Computer Networking: A Top-Down Approach” by Kurose and Ross
th edition discusses the algorithm on pages 384-385 in Chapter 5 (“The Network
Layer: Control Plane”)
Download and unzip the Project Files for Distance Vector from Canvas in the Assignments
Part 1: Files Layout
The DistanceVector directory contains the following files:
• DistanceVector.py – This is the only file you will modify. It is a specialization
(subclass) of the Node class that represents a network node (i.e., router) running the
Distance Vector algorithm, which you will implement.
• Node.py – Represents a network node, i.e., a router.
• Topology.py – Represents a network topology. It is a container class for a collection
of DistanceVector Nodes and the network links between them.
• run_topo.py – A simple “driver” that loads a topology file (see *Topo.txt below), uses
that data to create a Topology object containing the network Nodes, and starts the
• helpers.py – This contains logging functions that implement that majority of the
logging code for you.
• *Topo.txt – These are valid topology files that you will pass as input to the run.sh
script (see below).
• BadTopo.txt – This is an invalid topology file, provided as an example of what not to
do, and so you can see what the program says if you pass it a bad topology.
• output_validator.py – This script can be run on the log output from the
simulation to verify that the output file is formatted correctly. It does not verify that the
contents are correct, only the format.
• run.sh – Helper script that launches the simulation on a specified topology and
automatically runs the output validator on the log output when the simulation finishes;
basically a convenient wrapper for run_topo.py and output_validator.py .
Part 2: TODOs
There are a few TODOs in DistanceVector.py:
A. Review the methods already implemented in Node.py.
a. Because DistanceVector subclasses Node, consider how you might use these
existing methods to complete the rest of the TODOs in this list.
b. Do NOT modify Node.py.
B. Decide on how each node will represent its distance vector.
a. Consider what might be the simplest data structure to keep track of path weights
(i.e., the distance vector).
b. The distance vector variable should be local to the Node, i.e., defined in the
init function as a variable accessible via the self object (i.e. self.mylist).
C. Implement the Bellman-Ford algorithm.
a. Each Node will:
i. send out an initial message to its neighbors
ii. process messages received from other nodes
iii. send updates to other nodes as needed
b. Initially, a node only knows of:
i. itself and that it is reachable at cost 0,
ii. its neighbors and the weights on its links to its neighbors
c. NOTE: a node’s links are unidirectional.
d. NOTE: The Bellman-Ford algorithm implementation should terminate naturally
without external intervention.
D. Write a logging function that is specific to your distance vector structure.
a. You can use the logging helper files to take care of the bulk of the logging.
b. You should assume that the logging function only knows itself.
i. Do NOT access the topology for logging; logging should happen at the
Part 3: Testing and Debugging
To run your algorithm on a specific topology, execute the run.sh bash script:
Substitute the correct, desired filename for *Topo. Don’t use the .txt suffix on the command
line. This will execute your implementation of the algorithm in DistanceVector.py on the
topology defined in *Topo.txt and log the results (per your logging function) to
NOTE: You should not include the full filename of the topology when executing the run.sh
script. For example, to run the algorithm on topo1.txt you should only specify topo1 as the
argument to run.sh.
For this project, you may create as many topologies as you wish and share them on Ed
Discussion. We encourage sharing new topologies with log outputs. Topologies with format
errors will get an error back when you try to run them.
We’ve included four good topologies for you to use in testing and one bad topology to
demonstrate invalid topology. The provided topologies do not cover all the edge cases; your
code will be graded against more complex topologies.
Part 4: Assumptions and Clarifications
A. Node behavior:
a. The direction of a link indicates how traffic will flow; two nodes connected with
a link may pass messages regardless of traffic direction.
i. Example: Node B has an incoming link from Node A, but has no outgoing
link to Node A, Node B will send its distance vector to node A to
“advertise” other nodes it can reach (Nodes C and D).
b. A Node’s distance vector is comprised of the nodes it can reach via its outgoing
links (including to itself at distance = 0).
i. A Node will never advertise a negative distance to itself. (Important for
c. A Node advertises its distance vector to its upstream neighbors.
d. Nodes do not implement poison-reverse.
B. Edge and Path weights:
a. Edge weight values may be between -50 and 50, inclusive.
b. The edge weight value type is an integer.
c. There is no upper limit for path weights.
d. The lower limit for path weights is “-99”, which is equivalent to “negative
infinity” for this project.
C. Negative cycles:
a. A Node can forward traffic through a negative cycle.
b. Negative cycles are a series of directed links that originate and terminate at a
single node, where the sum of the link weights is less than 0.
i. This can lead to a negative “count-to-infinity” problem. Therefore, your
implementation must be able to detect negative cycles in order to be
able to terminate on its own.
ii. Any node that can reach a destination node and infinitely traverse a
negative cycle en route will set the distance to that node to -99.
1. Your implementation only needs to detect and record these
traversals appropriately; it does not need to mitigate them.
2. Extra resource: Professor Vigoda explains Negative Weight Cycles
and how to detect them, which is Lecture 4, Parts 2-7 of GATech’s
“Introduction to Graduate Algorithms” course on Udacity.1
6 & 7 are Bellman-Ford specific.)
iii. A Node can advertise a negative distance for other nodes (but not for
iv. A Node that receives an advertisement with a distance of -99 from a
downstream neighbor should also assume that it can reach the same
destination at infinitely low cost (-99).
v. Example: Traffic from Node F to Node D can route through A->B->C->A
indefinitely to reach an extremely low (very negative) value.
c. A Node will not forward traffic destined to itself.
i. Example: The below topology will not result in a count-to-infinity
problem, as there are no possible pairs of source and destination nodes
where traffic could indefinitely traverse a negative cycle. Node A will not
forward traffic for Node A, and similarly for Nodes B and C.
D. Topologies used in grading:
a. We will be using many topologies to test your project. This includes but is not
o topologies with and without cycles (loops), including odd length cycles
o topologies of varying sizes, including topologies with more than 26 nodes
o topologies with nodes with names longer than one character
o topologies with multiple paths to different nodes
o topologies that include any combination of positive weights, zero weight, and
o topologies with negative cycles, meaning a node may reach another at
infinitely low cost
o topologies with Nodes that do not have incoming or outgoing links
All nodes will be connected but:
• some may have both incoming and outgoing links
• some may only have incoming links
• some may only have outgoing links
b. We will NOT test your submission against the following topologies (which means
your algorithm does not need to account for them):
o topologies with more than one link from the same origin to the same
o topologies with portions of the network disconnected from each other
o topologies that do not require intermediate steps (such as a topology with a
o topologies with a valid path between two indirectly linked nodes with no
cycle with an actual total cost of ≤ -99 (topologies will respect that -99 is
“negative infinity” for this project)
Part 5: Correct Logs for Provided Topologies
Below are the correct final logs for the provided topologies. We are providing them to help you
identify correct behavior with respect to negative cycles and the assumptions in the
instructions. We are only providing the final round; each topology should produce at least 2
rounds of output.
Part 6: Spirit of the Project
The goal of this project is to implement a simplified version of a network protocol using a
distributed algorithm. This means that your algorithm should be implemented at the network
node level. Each network node only knows its internal state, and the information passed to it by
its direct neighbors.
Declaring global variables will be a violation of the spirit of the project.
The skeleton code we provide you runs a simulation of the larger network topology. For
simplicity, the Node class defines a link to the overall topology. This means it is possible using
the provided code for one Node to access another Node’s internal state. This goes against the
spirit of the project and is not permitted.
When we grade your code, we will use a special version of Node.py that will have a randomly
generated variable name for the topology object, and if you access it directly to generate your
distance vectors in DistanceVector.py, your code will throw a runtime error, and receive no
credit. If you have questions about whether your code is accessing data it should not, please
ask on Ed Discussion or during office hours!
Part 7: FAQs
Q: May I import a python module into DistanceVector.py? For example, may I use import
A: Your DistanceVector.py submission must run on the provided course VM without requiring
the installation of any additional packages or software. All submissions will be tested using the
commands and files described in this document, as well as with several additional unannounced
topology files. This means that any modules included in the standard Python installation of the
VM are fine for import. However, most students complete this project without doing so.
Q: What is the best way to format and process node messages?
A: There is no right or wrong way to format messages. For best results keep things simple.
Q: Is it required that the distance vectors displayed in my log files be alphabetized?
A: Take a look at the code in helpers.py. Note how the DVs are alphabetized each round, and
this is reflected in the provided correct output logs. The nodes within individual vectors are not
required to be sorted.
Q: Should my solution include an implementation of split horizon?
A: That is not a requirement for this project.
Q: What if there really is a valid path between two indirectly linked nodes with no cycle and
the total cost is -99 or less?
A. We will not test your submission against a topology that does this. However, from the
“Assumptions and Clarifications”, note: “a Node seeing an advertised vector of -99 from a
downstream neighbor can assume this means it can reach that same destination at infinitely
low cost (-99).”
What to Turn In
Submit ONLY your DistanceVector.py in a .zip file named as follows: GTLogin_dv.zip where
GTLogin should be replaced with your ID you use to log into Canvas (e.g., smith7_dv.zip ).
zip gtlogin_dv.zip DistanceVector.py
There are some very important guidelines for this file you must follow:
A. Ensure that your submission self-terminates. If your submission runs indefinitely (i.e.,
contains an infinite loop) or throws an error at runtime, it will not receive full credit.
Manually killing your submission via console commands or interrupts is NOT an
acceptable means of termination.
B. Remove any print statements from your code before turning it in. Print statements left
in the simulation, particularly for inefficient but logically sound implementations, have
drastic effects on run-time. Your submission should take less than 10 seconds to process
a topology. If your leave print statements in your code and they adversely affect the
grading process, your work will not receive full credit. (Feel free to use print statements
during the project and during debugging but remove them before you submit to
C. Ensure your logs are formatted properly. Logging is the only way that we can verify that
your algorithm is running correctly. The output validator will catch most formatting
mistakes, but you should inspect your output manually to make sure it matches the
requested format. (See the TODO comment for logging located in DistanceVector.py for
a. Incorrectly formatted logs will fail the auto grader and will receive no credit. We
will not be manually inspecting incorrectly named/formatted/etc. logs due to
the number of students in the class.
D. Ensure your solution generates completely correct output. Partial credit for individual
topologies will not be awarded, even if the distance vector logs are “mostly correct.”
E. Check your submission after uploading. As usual, we do not accept resubmissions past
the stated deadlines.
What you can and cannot share
Do not share the content of your DistanceVector.py file with your fellow students, on Ed
Discussion, or elsewhere publicly. You may share any log files for any topology, and you may
also share new topologies. Additionally, code that you write that is not required for turn-in, like
testing suites may be shared. It may be a good idea to share a “correct” log for a particular
topology, if you have one, when you share the code for that topology.
When sharing log files, leave alphabetization on so that your classmates can use the diff tool
to see if you are getting the same log outputs as they are.
For turning in the correct file, with the correct name, and significant
effort has been made towards completing the project.
For correct Distance Vector results (log file) on the provided topologies.
For correct Distance Vector results (log file) on topologies that you will
not see in advance. They are slightly more complex than the provided
ones and test some edge cases.
GRADING NOTE: There is no partial credit for individual topologies; each topology is either
“passed” or “failed”.
As with previous projects in this course, due to the size of the class, we will not accept
resubmissions, modifications to old submissions past the deadline, etc.