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EN.601.419/EN.601.619: Cloud Computing
Assignment 1

Goal. The main goal of this assignment is to get hands-on experience with a few tools for experimental
research in cloud networking and the MapReduce programming paradigm.
1 OpenFlow functionality with Mininet
Experimentation is an important part of networking research. However, large-scale cloud experiments can
sometimes be hard to achieve, e.g., due to lack of machines. In this section, you will learn how to use Mininet1,
a relatively new experimental platform that can scale to hundreds or more emulated “nodes” running
on a single machine. Mininet takes advantage of Linux support for network namespaces2 to virtualize the
network on a single machine, so that dierent processes on the same machine can see their own network
environments (like network interfaces, ARP tables, routing tables, etc.), distinct from other processes.
Combined with the Mininet software, this enables a single machine to emulate a network of switches and
hosts. The emulated processes, however, do see the same real/physical file system.
Mininet is designed with OpenFlow3 in mind. In this exercise, you will gain a basic understanding of
OpenFlow and create a custom OpenFlow controller to control your switches. Quite simply, OpenFlow
allows for “programmable” network devices, e.g., switches. With Mininet, each switch will connect to the
controller specified when the switch is launched. When the switch receives an Ethernet frame, it consults
its forwarding table for what to do with the frame. If it cannot determine what to do with the frame, the
switch sends the frame (and some extra information such as the input switch port) to the controller, which
will then instruct the switch on what to do with the frame. To avoid this extra work on every such frame,
the controller can install a new rule/match in the switch’s forwarding table, so that the switch can forward
future similar frames without having to contact the controller.
1
http://mininet.org/
2
http://lwn.net/Articles/219794/
3
http://www.openflow.org/
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EN.601.419/EN.601.619 Spring 2020
1.1 Prepare the Mininet VM and OpenFlow Controller
1. Install VirtualBox from https://www.virtualbox.org/wiki/Downloads. VMware should also
work; adjust your VM configurations accordingly. (It is possible to install Mininet directly on your
Linux system, but for simplicity we’ll use the virtual machine here.)
2. Download and unzip the VM with Mininet already installed from http://onlab.vicci.org/mininet-vm/
mininet-2.2.1-150420-ubuntu-14.04-server-amd64.zip
You can also download VM images with Minine from https://github.com/mininet/mininet/
releases
3. In VirtualBox, import the “ovf” template just unzipped. For the newly imported machine, go to
“Settings” ! “Network” and make “Adapter 1″ a “NAT” (Network Address Translation). If your VM is
allowed to obtain an IP address from your local network, you can alternatively use “Bridge Adapter.”
For more information on networking with VirtualBox, see http://www.virtualbox.org/manual/
ch06.html
4. Start the VM
5. Log in with mininet for both username and password
6. Make sure eth0 is up:
(a) run the command:
ifconfig eth0
(b) check the inet addr field. If it does not have an IP address, then run the command:
sudo dhclient eth0
and repeat step (a).
7. The downloaded image should have POX preinstalled. POX is a platform that allows you to write your
own OpenFlow controller using Python. Please check home folder and see if there is a folder called
“pox”. If not, please do:
git clone https://github.com/noxrepo/pox
For more information on POX, see https://openflow.stanford.edu/display/ONL/POX+Wiki
8. Install a GUI in the VM:
(a) Install the GUI
sudo apt-get update
sudo apt-get install openbox xinit -y
(b) Start it
startx
(c) To create a new terminal, right-click on the desktop and select “Terminal emulator”
Alternately you may use SSH to log in to the VM remotely, with GUI (X11) forwarding. With
SSH, you will need to enable X-forwarding (e.g., ssh -X on *NIX hosts) when you ssh into the
VM. NOTE: this requires you have an X server running on the host. See a description of how
to do this on various platforms at https://github.com/mininet/openflow-tutorial/wiki/
Installing-Required-Software. Alternative for some versions of Mac OS X: install the Developer
Tools (a free download from the App Store) and open /Applications/Utilities/X11.
1.2 Create a hub in Mininet using POX
In this exercise you will create a Mininet network with 3 hosts connecting via a switch. Using POX, you will
program the switch to behave like a hub, which simply forwards incoming packets to every port except the
one on which it entered.
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EN.601.419/EN.601.619 Spring 2020
First, you can familiarize yourself with Mininet by following http://mininet.org/walkthrough/.
To start Mininet with the topology we want:
• First clean up the network:
sudo mn -c
• Then create a network with the topology we want:
sudo mn –topo single,3 –mac –switch ovsk –controller remote
This will create a network with the following topology:
host h1 ——switch s1 —- controller c0
host h2 ——-/ /
host h3 ——–/
After you create this network, you will be entering the Mininet console. You can type help in the
console to see a list of commands provided by Mininet. We will later use some of these commands.
Now let’s run POX controller. Create another terminal (right-click on the desktop and select “Terminal
emulator”). Go to the directory you installed POX in this new terminal, and then start POX with basic hub
function:
pox/pox.py log.level –DEBUG forwarding.hub
The argument log.level –DEBUG enables verbose logging and forwarding.hub asks POX to start
the hub component. It takes up to 15 seconds for switches to connect to the controller. When a OpenFlow
switch has connected, POX will print something like:
INFO:openflow.of_01:[00-00-00-00-00-01 1] connected
INFO:forwarding.hub:Hubifying 00-00-00-00-00-01
To verify the hub behavior, we use tcpdump, a common packet analyzer that intercepts and prints
packet information. To do this, we first create an xterm (terminal emulator in X Window System) for each
host in Mininet and view packets in each. To start an xterm for each host, type the following command in
the Mininet console:
xterm h1 h2 h3
You may want to arrange xterms properly so that you can see them on the screen at once. You may
need to reduce the terminal height to fit a laptop screen. In the xterms for h1 and h2, run tcpdump to
capture and print all the packets:
tcpdump -XX -n -i h1-eth0
and
tcpdump -XX -n -i h2-eth0
In the xterm for h3, send a ping to h1:
ping -c1 10.0.0.1
The ping packets are going to the controller, which floods the packet out all interfaces but the received
one. Because of this hub behavior, you should see identical ARP and ICMP packets in both xterms running
tcpdump.
• [1 points] A.1.1 What will happen if you ping a non-existent host that doesn’t reply ICMP requests?
For example, do the following command in the xterm for h3:
ping -c1 10.0.0.9
Submit and explain the results.
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EN.601.419/EN.601.619 Spring 2020
Now let’s take a look at the hub code at pox/pox/forwarding/hub.py. Make sure to get familiar
with the code because many POX API functions used here will help you answer the later questions. We
describe several important API functions here, and you can find more information about POX APIs at
https://openflow.stanford.edu/display/ONL/POX+Wiki#POXWiki-POXAPIs.
• connection.send() function sends an OpenFlow message to a switch.
When the connection between a switch and the controller established, the code will invoke _handle_ConnectionUp()
function that implements the hub logic.
• ofp_flow_mod OpenFlow message
This tells a switch to install a flow entry, which matches some fields of incoming packet headers and
executes some actions on matching packets. Important fields include:
– actions: A list of actions that apply to matching packets (e.g., ofp_action_output described
below).
– match: An ofp_match object (described below).
– priority: When a packet matches on more than one non-exact flow entry, only the highest
priority entry will be used. Here, higher values are higher priority.
• ofp_action_output class
This is an action for use with of.ofp_flow_mod. You can use it to assign a switch port that you want
to send the packet out of. It can also take “special” port numbers, e.g., we use OFPP_FLOOD to send
the packet out all ports but the received one.
• ofp_match class (not used in the hub code but is useful in the assignment) This is an object that
specifies packet header fields and input port to match on. All fields here are optional, i.e., if you do
not specify a field, it becomes a “wildcard” field and will match on anything. Some important objects
in this class:
– dl_src: The data link layer (MAC) source address
– dl_dst: The data link layer (MAC) destination address
– in_port: The packet input switch port
Example to match packets with source MAC address 00:00:00:00:00:01 in a OpenFlow message msg:
msg.match.dl_src = EthAddr(“00:00:00:00:00:01”)
1.3 Create a firewall
A firewall is used as a barrier to protect networked computers by blocking the malicious network trac
generated by viruses and worms. In this assignment, you are asked to implement a data link layer firewall
to block certain trac.
To start this, you will find a skeleton class file at http://soudeh.net/teaching/cloud/spring_
2020/files/a1/firewall.py. This skeleton class is currently not blocking any trac and you will need
to modify this skeleton code to add your own logic later. To test the firewall, put the firewall.py in the
pox/pox/misc directory and run the POX controller:
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EN.601.419/EN.601.619 Spring 2020
./pox.py log.level –DEBUG forwarding.hub misc.firewall
Note: You may need to use the layer 2 MAC learning instead of the hub, i.e., you can replace the
command above with:
./pox.py log.level –DEBUG forwarding.l2_learning misc.firewall
After the connection between the controller and the switch is established, we can verify the connectivity
between all pairs of hosts by typing pingall in the Mininet console. Note that when ping cannot get
through a pair of hosts, you need to wait for the timeout, which takes about 10 seconds.
• [1 points] A.1.2 Modify the firewall (firewall.py) to block trac with source MAC address 00:00:00:00:00:02
and destination MAC address 00:00:00:00:00:03. To show the result, you can use the command pingall
and copy the output to your report. (Hint 1: this only takes a few lines of code. Hint 2: if you did not
specify any action in a OpenFlow message, then matching packets will be dropped.)
1.4 What to turn in
Your submission should comprise two parts: 1) a part in your PDF document that answers the aforementioned questions (2 points) and 2) a .zip file that contains your source code (3 points).
Note: To get your files o the VM, you can scp or ftp them to some other machine. Or you can install
the GUI (instructions in PDF) and then “sudo apt-get install firefox” and then launch the GUI and use firefox
to upload/email the files o the machine.
2 Playing with MapReduce
When discussing data analytic systems, we will see MapReduce as a paradigm for large-scale data processing.
This part of the assignment provides practical experience writing MapReduce jobs.
2.1 Overview
The National Do Not Call Registry4 is a national database of telephone numbers of individuals who do not
want to be contacted by telemarketers. Unfortunately, robocalls and spoofing are on the rise, leading to a
record number of complaints in recent years.
In an eort to stop unwanted calls, law enforcement recently seized evidence from a cloud provider that
assists businesses in contacting their customers.5 In the excitement of the raid, however, service metadata
about the records and cloud consumers was damaged, leaving only portions of logs recording phone calls.
Your goal is to uncover which cloud consumers are violating the law!
Fragments of the original call logs have been partially pieced together. Each log entry is a single line
that provides the following information:
1. the date and time of a call,
2. the company responsible for the call,
3. the originating phone number for the call,
4. the recipient’s phone number, and
5. the duration of the call (in seconds).
4
https://www.donotcall.gov/
5
The events that follow are fictitious. Any similarity to real life is purely incidental.
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EN.601.419/EN.601.619 Spring 2020
For example, the log entry indicates that the Acme Corporation used (429) 785-4094 to place a 9-second call
to (429) 826-1640 slightly before 6 p.m. on 9 April 2017. Of course, this information alone is insucient
to determine if this phone call is legitimate: one must know that 1) the first telephone number has been
reported for spam calls or 2) the second telephone number is part of the Do Not Call Registry and reported
this specific call as unwanted.
Law enforcement has identified that the following numbers reported unwanted calls during the time
frame captured in the call log: (216) 684-9356, (404) 934-5110, (589) 371-5037, and (945) 792-0329. You
may assume that all calls that appear in the log and were placed to these numbers are violations of the Do
Not Call Registry. That is, no business contacted these numbers for legitimate reasons such as an order
confirmation.
As part of your forensics investigation, you must answer the following questions:
• A.2.1 On how many occasions did companies violate the Do Not Call Registry?
• A.2.2 How many numbers should be blocked / marked as spam to reduce the number of unwanted
calls?
• A.2.3 Which telephone numbers received the most spam calls?
• A.2.4 Which telephone numbers are responsible for the most spam calls?
• A.2.5 Which hours of the day are spam calls most likely?
Although you technically need not use MapReduce to answer these questions, large-scale data analysis
practically necessitates a parallel processing framework.
2.2 What to turn in
You may implement the MapReduce jobs using any programming language. For simplicity, consider using a
scripting language (e.g., Python) and Hadoop’s streaming to facilitate testing your jobs.
For testing your implementation and answering questions, you can use the data file at http://soudeh.
net/teaching/cloud/spring_2020/files/a1/mr.data.
Your submission should comprise two parts: 1) a part in your PDF document that answers the aforementioned questions and uses the guidance that follows for forming your responses to them (5 points) and
2) a .zip file that contains your source code (10 points). The source code archive should include all code
used to answer each question, with the source code for each question in a separate directory named (01,
02, … ). That is, the root directory of the archive should contain a subdirectory for each question and each
subdirectory could include all source code (i.e., implementation of the MapReduce job) used to answer that
question. This archive must also include a script called “runall.sh” which will run all of your mapping and
reducing jobs.
Please note that answering some questions may require post-processing of the MapReduce results (e.g.,
extracting only the top-3 hours that had the most spam calls). You are not required to submit any code
used for such post-processing, as it is assumed that you can perform this step manually.
Specific guidance for answering each question follows.
A.2.1. On how many occasions did companies violate the Do Not Call Registry? For each company
that violated the Do Not Call Registry list the number of known unwanted calls placed by that company.
That is, how many times did each company contact one of the numbers that reported unwanted calls?
Order your results lexicographically by company (i.e., alphabetically by company name).
Your answer should resemble the following:
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EN.601.419/EN.601.619 Spring 2020
Acme Corporation 4

A.2.2. How many numbers should be blocked / marked as spam to reduce the number of unwanted
calls? What is the total number of telephone numbers that should be blocked for each company? These
telephone numbers should be all numbers used by the companies guilty of violating the Do Not Call Registry.
That is, if the company violated the Do Not Call Registry once, then assume that all its calls should be
marked as spam. Order your results lexicographically by company (i.e., alphabetically by company name).
Your answer should resemble the following:
Acme Corporation 6411

A.2.3. Which telephone numbers received the most spam calls? List the top-3 telephone numbers
receiving spam calls and how many spam calls each received. Order your results in decreasing order of the
telephone number receiving the most calls.
Your answer should resemble the following:
(847) 580-3060 18

A.2.4. Which telephone numbers are responsible for the most spam calls? List the top-3 telephone
numbers placing spam calls and how many calls originated from each. Order your results in decreasing
order of the telephone number responsible for the most spam calls.
Your answer should resemble the following:
(202) 221-4130 77

A.2.5. Which hours of the day are spam calls most likely? List the top-3 hours that had the most
spam calls and how many spam calls were placed in each hour. Order the results in decreasing order of the
number of calls.
Your answer should resemble the following:
11 a.m. 3157

7
A Hadoop
Apache Hadoop1 is an open source software framework for big data processing.
It has several components, but the two most critical to this assignment are its
implementation of MapReduce and the Hadoop Distributed File System (HDFS),
which are based on MapReduce [1] and the Google File System (GFS) [2] respectively.
For simplicity in this assignment, we’ll use Hadoop’s streaming application
programming interface (API) which allows the use of any executable script to
define the map and reduce operations. The streaming API uses the standard input
and output streams to pass information among jobs. More specifically, the map
operation coverts lines of input (text-based and terminated by a line break) into
a series of key-value pairs, one per line of output. After these key-value pairs
are sorted (automatically by Hadoop), the reduce operation aggregates them to
produce a final value for each unique key. By convention, the streaming API uses
the first tab character on a line to delimit the key and value.
An advantage of the streaming API is that you can use command line utilities to
test your map and reduce operations. For example, the following shell command
executes two Python scripts using a (small) local data file:
cat /path/to/data | python map.py | sort | python reduce.py
where cat prints the specified data files on standard out, map.py defines the
map operation, sort sorts the script’s output in ascending order, and reduce.py
defines the reduce operation. Of course, none of these steps are parallelized in this
case, but Hadoop will perform the various operations in parallel when processing
multiple data files.
B MapReduce
Writing a MapReduce job using the streaming API is straightforward. The canonical
MapReduce example is counting words so we’ll use it to illustrate the process.
1
https://hadoop.apache.org/
As previously mentioned, the map operation reads input from standard in
and outputs a series of key-value pairs, one per line. The following Python code
implements this operation for counting words:
1 #!/usr/bin/env python
2
3 import sys
4
5
6 for line in sys.stdin:
7 line = line.strip() # remove leading and trailing whitespace
8
9 # split line using whitespace as delimiters
10 tokens = line.split()
11 # iterate over tokens
12 for token in tokens:
13 print(“{token}\t{count}”.format(token=token, count=1))
That’s it! This Python code outputs a stream of tokens with a ‘1’ to indicate that
each token was encountered once in the line of text. (If the same token appears
multiple times, then it will be listed multiple times.) For example, the input
a man a plan a canal panama
becomes
a i|
1
man i|
1
a i|
1
plan i|
1
a i|
1
canal i|
1
panama i|
1
where i| indicates a tab character (i.e., \t).
The reduce operation reads the key-value pairs and aggregates them to produce a final value for each key. Of course, its input must be sorted to produce the
correct results. The following Python code implements this operation for counting
words:
1 #!/usr/bin/env python
2
3 import sys
4
5
6 def emit(token, count):
7 print(‘{token}\t{count}’.format(token=token, count=count))
8
9
10 previous = None
11
12 for line in sys.stdin:
13 line = line.strip() # remove leading and trailing whitespace
14
15 token, count = line.split(‘\t’, 1) # split key-value pair
16 try:
17 count = int(count)
18 except ValueError:
19 continue
20
21 if previous == token:
22 total = total + count
23 else:
24 if previous:
25 emit(previous, total)
26
27 previous = token
28 total = count
29
30 emit(token, total)
In essence, this script simply checks to see if the prior token is the same as the
current token, incrementing the total count when they match and outputting the
total when they dier.
Try writing these scripts and testing them as follows:
echo “a man a plan a canal panama” | map.py | sort | reduce.py
You should see the following result:
a i|
3
canal i|
1
man i|
1
panama i|
1
plan i|
1
(Note: Both scripts must be executable to invoke them in this fashion.)
References
[1] J. Dean and S. Ghemawat. MapReduce: Simplified Data Processing on Large
Clusters. In Proceedings of the 6th Conference on Symposium on Opearting Systems
Design & Implementation, volume 6 of OSDI ’04, pages 10–10, 2004.
[2] S. Ghemawat, H. Gobio, and S.-T. Leung. The Google File System. In Proceedings
of the Nineteenth ACM Symposium on Operating Systems Principles (SOSP ’03), pages
29–43, 2003.

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