Description
In the lectures, you learned about Spanning Trees (see Canvas->Modules->Lesson 1-> “Looping
Problem in Bridges and the Spanning Tree Algorithm), which can be used to prevent forwarding
loops on a layer 2 network (see the Wikipedia entry on Spanning Trees.
In this project, you will
develop a simplified distributed version of the Spanning Tree Protocol that can be run on an
arbitrary layer 2 topology. With this project, we will be simulating the communications between
switches until they converge on a single solution, and then output the final spanning tree to a
file.
This document covers the following: 1. Project Files Layout; 2. Project Outline: TODOs; 3.
Testing and Debugging; 4. Assumptions and Clarifications; 5. Submission; 6. Grading; 7. Honor
Code/Academic Integrity.
Part 1: Setup
Download the project from Canvas to the course VM and unzip. Alternatively, you can do this
project on your host system if it has Python 3.7 or newer installed… just be sure that it runs
properly in the VM, as this is where we run the autograder.
Ensure the files have the correct permissions. (See the Simulating Networks project if you need
a refresher on basic Linux commands.) This project MUST be coded in Python 3.7 or newer –
the VM has Python 3.8, otherwise make sure your host’s Python version is updated to 3.7 or
newer.
Part 2: Files Layout
There are many files in the SpanningTree directory, but you only need to (and should only)
modify Switch.py, which represents a layer 2 switch that implements our simple Spanning Tree
Protocol. You will implement the functionality of the Spanning Tree Protocol to generate a
Spanning Tree for each Switch. (Details follow in the “2. Project Outline – TODOs”.)
The other files in the project skeleton are described below. Do not modify these files, all of your
coding will be in Switch.py. However please study these files to understand the project better
and to make decisions about your code in Switch.py.
• Topology.py – Represents a network topology of layer 2 switches. This class reads in
the specified topology and arranges it into a data structure that your switch code can
access.
• StpSwitch.py – A base class of the derived class you will code in Switch.py. The base
class StpSwitch.py abstracts certain implementation details to simplify your tasks.
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• Message.py – This class represents a simple message format you will use to
communicate between switches, similar to what was described in the course lectures.
Specifically, you will create and send messages in Switch.py by declaring a message as
msg = Message(claimedRoot, distanceToRoot, originID,
destinationID, pathThrough)
and assigning the correct data to each input. Message format may not be changed. See
the comments in Message.py for more information on the data in these variables.
• run_spanning_tree.py – A simple “main” file that loads a topology file (see
XXXTopo.py below), uses that data to create a Topology object containing Switches, and
starts the simulation.
• XXXTopo.py, etc. – These are topology files that you will pass as input to the
run_spanning_tree.py file.
• sample_output.txt – Example of a valid output file for Sample.py as described in
the comments in Switch.py.
Part 3: TODOs
This is an outline of the code to implement in Switch.py with suggestions for
implementation. Your implementation must adhere to the “spirit of the project” and satisfy the
labeled TODO sections in the project code per the pre-existing comments.
A. Decide on the data structure that you will use to keep track of the spanning tree.
1. The collection of active links across all switches is the resultant spanning tree.
2. The data structure may be variable(s) needed to track each switch’s own view of the
tree. A switch only has access to its member variables. A switch may not access its
neighbor’s information directly – to learn information from a neighbor, the
neighbor must send a message.
3. This is a distributed algorithm. The switch can only communicate with its neighbors.
It does not have an overall view of the spanning tree, or of the topology as a whole.
4. An example data structure would include, at a minimum:
a. a variable to store the switch ID that this switch currently sees as the root,
b. a variable to store the distance to the switch’s root,
c. a list or other datatype that stores the “active links” (i.e., the links to
neighbors that should be drawn in the spanning tree).
d. a variable to keep track of which neighbor it goes through to get to the root.
(A switch should only go through one neighbor, if any, to get to the root.)
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5. More variables may be helpful to track data needed to build the spanning tree and
will depend on your specific implementation.
6. It is important to create a data structure in the correct place in Python (and most
object- oriented programming languages). If you create it inside a method, every
time the method is called it will be created as new.
You should create a class object
in the class constructor so that the data stored in the object exists for the life of the
class instance that is created by Topology.py. For example self.mylist = [ ] in the
constructor should create an empty list data structure and act as instance variable.
But if mylist were instantiated in, say, process_messages, then it will be created
every time the method is called. This could be useful in how you track which links
are active to certain neighbors for any given switch.
B. Implement sending initial messages to neighbors of the switch.
1. Your implementation of send_initial_messages() in Switch.py will be called in
Topology.py for each switch in the topology before any other messages are
processed and/or sent.
2. See code comments in Message.py, Topology.py, and StpSwitch.py for details on
message format, message creation, and how to send messages between switches.
a. pathThrough is a Boolean, not an int.
b. In a message, pathThrough is TRUE if the message-sending switch goes
through the message-receiving switch in order to reach claimedRoot.
pathThrough is FALSE if the message-sending switch does not go through
the message-receiving switch in order to reach claimedRoot.
3. Initially, each switch thinks it is the root of the spanning tree.
C. Implement processing a message from an immediate neighbor.
1. For each message a switch receives, the switch will need to:
a. Determine whether an update to the switch’s root information is
necessary and update accordingly.
I. The switch should update the root stored in its data structure if it
receives a message with a lower claimedRoot.
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II. The switch should update the distance stored in its data structure if
a) the switch updates the root, or b) there is a shorter path to the
same root.
b. Determine whether an update to the switch’s active links data structure is
necessary and update accordingly.
The switch should update the
activeLinks stored in the data structure if:
I. The switch finds a new path to the root (through a different
neighbor). In this case, the switch should add the new link to
activeLinks and (potentially) remove the old link from activeLinks
II. The switch receives a message with pathThrough = TRUE but does
not have that originID in its activeLinks list. In this case, the switch
should add originID to its activeLinks list.
III. The switch receives a message with pathThrough = FALSE but the
switch has that originID in its activeLinks. In this case, the switch
may need to remove originID from its activeLinks list.
c. Determine whether the switch should send new messages to its neighbors
and send messages accordingly.
I. This is an important design decision. There are many correct
algorithms that send messages at different times. The distributed
algorithm has converged to the Spanning Tree when no more
messages are sent.
II. The message FIFO queue is maintained in Topology.py. The switch
implementation does not interact with the FIFO queue directly, but
instead uses send_msg to send messages, and receives a message
as an argument in process_message.
iii. When sending messages,
pathThrough should only be TRUE if the destinationID switch is the
neighbor that the originID switch goes through to get to the
claimedRoot. Otherwise, pathThrough should be FALSE.
2. Other variables may be helpful for determining when to update the root information
or the activeLinks data structure and can be added to your data structure and
updated as needed, depending on your implementation.
D. Write a logging function that is specific to your particular data structure.
1. The switch should only output the links that it thinks are in spanning tree.
2. Follow the format. Unsorted/non-standard formatting can result in autograder
penalties. Examples of correct solutions with correct format have been provided to
you.
3. Sorted: Not sorted:
1 – 2, 1 – 3 1 – 3, 1 – 2
2 – 1, 2 – 4 2 – 4, 2 – 1
3 – 1 3 – 1
4 – 2 4 – 2
Part 4: Testing and Debugging
To run your code on a specific topology (SimpleLoopTopo.py in this case) and output the results
to a text file (out.txt in this case), execute the following command:
python run_spanning_tree.py SimpleLoopTopo out.txt
NOTE: “SimpleLoopTopo” is not a typo in the example command – don’t include the .py
extension.
We have included several topologies with correct solutions (and format) for you to test your
code against. You can (and are encouraged to) create more topologies with output files and
share them on Ed Discussion.
You will only be submitting Switch.py – your implementation must be confined to
modifications to that file. We recommend testing your submission against a clean copy of the
rest of the project files prior to submission.
We encourage adding print statements to facilitate debugging during the development process,
if they are removed or commented out prior to submission.
Part 5: Assumptions and Clarifications
You may assume the following:
A. All switch IDs are positive integers, and distinct.
1. These integers do not have to be consecutive.
2. They will not always start at 1.
3. There is no maximum value beyond language (Python) limitations (which your code
does not need to check for).
B. Tie breakers: If there are two paths of equal distance to the same root, the switch
should choose the path through the neighbor with the lowest switch ID.
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1. Example: switch 5 has two paths to root switch 1, through switch 3 and switch 2.
Each path is 2 hops in length. Switch 5 should select switch 2 as the path to the root
and disable forwarding on the link to switch 3.
C. There is a single distinct solution spanning tree for each topology. This is guaranteed
by the first two assumptions.
D. All switches in the network will be connected to at least one other switch, and all
switches are able to reach every other switch. It will always be possible to form a tree
that spans the entire topology.
E. There will be only 1 link between each pair of directly connected switches. You do not
need to consider how STP should behave with redundant links.
F. The topology given at the start will be the final topology. The topology will not change
while your code is running (i.e., adding a switch, severing a connection, etc.)
G. A switch may always communicate with its neighbors. When a switch treats a link as
inactive, the link can still be used during the simulation. “Inactive” simply means that
the port/link will not be used for forwarding normal network traffic.
H. The solution implemented in Switch.py should terminate without intervention.
When there are no more messages in the queue to process, the simulation will log
output and self-terminate.
What to Turn In
To complete this project, submit ONLY your Switch.py file to Canvas as a single file in a zip file
named GTLogin_stp.zip, where GTLogin should be replaced with your ID you use to log into
Canvas (e.g., smith7 in smith7_stp.zip). Do not modify the name of Switch.py or else grading
will be affected, and you may receive a zero.
The directory scheme must be that file at the top
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level when extracted from the GTLogin_stp.zip file is Switch.py. When extracted your output
should contain:
• Switch.py
Before submission:
a. Make sure your logging format is correct. Invalid format will result in autograder
penalties.
b. Remove any print statements from your code before turning it in. Print statements left
in the simulation, especially for inefficient implementations, have drastic effects on
runtime. Your submission should take less than 10 seconds to process a topology used in
grading. If print statements in your code adversely affect the grading process, your work
will not receive full credit.
c. Make sure your Switch.py works in the virtual machine, where we will run the
autograder. When done, submit to Canvas. Then, in the VM, log into Canvas and
download your submission and test it out to make sure it works there. You’ll need to
download the project files as well and drop in your Switch.py.
d. Make sure your Switch.py has the proper Linux-style line endings! Specifically, if you
are editing the files in Windows, make sure the line endings remain Linux-style, with just
a LF at the end of lines. Windows may try to put CRLF at the end of lines.
After submission:
e. Make sure your submission uploaded correctly. Late submissions will result in reduced
or zero points.
What you can and cannot share
Honor Code/Academic Integrity: Do not share code from Switch.py with your fellow students,
on Ed Discussion, or publicly in any form. You may share log files for any topology, and you may
also share any code you write that will not be turned in, such as new topologies or other testing
code.
In past semesters, the most trouble we have had with students not abiding by the honor code
was in the Spanning Tree Project. All work must be your own, and consulting solutions, even in
another programming language or just “for reference”, are considered violations of the honor
code. DO NOT reference solutions on Github! For more information see the Syllabus definition
of Plagiarism.
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Start early, ask questions in Ed Discussion and attend TA chats if helpful. While this project is
challenging, most of our students have succeeded with time and hard work and have a great
sense of personal achievement with this project.
Rubric
15
pts
Correct
Submission
For turning in all the correct files with the correct names, and significant
effort has been made in each file towards completing the project.
60
pts
Provided
Topologies
For correct Spanning Tree results (log files) on the provided topologies.
75
pts
Unannounced
Topologies
For correct Spanning Tree results (log files) on three topologies that you will
not see in advance. They are slightly more complex than the provided ones
and may test for corner cases.
GRADING NOTE: Partial credit is not available for individual topology spanning tree output
files. The output spanning tree must be fully correct to receive credit for that input topology – a
single link’s discrepancy will result in a zero for that topology.
Additionally, we will be using
many topologies to test your project, including but not limited to the topologies we provide,
and checking for corner cases not exhibited in the sample topologies provided.
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
switch level. Each switch only knows its internal state, and the information passed to it via
messages from its direct neighbors – the algorithm must be based on these messages.
The skeleton code we provide you runs a simulation of the larger network topology, and for the
sake of simplicity, the StpSwitch class defines a link to the overall topology.
This means it is
possible using the provided code for one Switch to access another’s internal state. This goes
against the spirit of the project and is not permitted. Additional detail is available in the
comments of the skeleton code.
Therefore … GRADING NOTE: The autograder checks if
submissions attempt to directly access topolink or self.topology. Submissions that attempt this
will 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!)
