CSC8110/CSC8634 – Cloud Computing
Assessment 1
Assessment Overview
This assessment contributes 50% towards the total mark for this module. It is an individual
exercise: no group work is permitted for the assessment. You are advised to watch all the instructional videos, provided in-line with the individual task specifications, as well as read all
tutorial resources before you start answering these questions. Marks shown are indicative only.
The coursework report must be submitted on NESS by 22:00 pm on December
17, 2021. After completing Tasks 1-4, you will need to provide in-depth details on how you
implemented the solutions (e.g., code, commands) to solve the aforementioned tasks in a final
Report (Task 5). Additionally, you will be required to demonstrate successful executions of Tasks
1 – 4. Before the report submission deadline, you will be provided a 15 mins slot to conduct live
demonstrations. In case of unforeseen disruptions (e.g., further lockdowns), we may also allow
recorded demonstrations.
Final marks for this assessment will be decided by your performance in the live demonstrations and the details provided in the final report. While the final report needs to be submitted
to NESS by 22:00 pm on December 17, 2021, the live demonstration session will be organised on
either December 16 or 17, 2021.
You are required to complete the Tasks 1-4 using both command line interface ([https://docs.docker.com/engine/reference/commandline/cli/]) and one of the
Software Development Toolkits (SDKs) provided by the Docker Engine ([https:
//docs.docker.com/engine/api/sdk/]).
Objectives
This coursework helps you to learn and understand the fundamentals of programming and deploying Docker-based (an emerging cloud virtualisation technology) application hosting environment.
By successfully completing the coursework, you will be able to gain hands on experience on the
following interrelated aspects including:
• Configuring a Docker-based application hosting environment;
• Pushing and pulling images from the Docker Hub (global repository of software components’ images maintained by the developers);
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• Creating and deploying a complex web application stack consisting of multiple software
components (e.g., database, web server, etc.);
• Monitoring ongoing performance of your application stack using the inherent features provided by the Docker environment.
Pre-Requisites
Before attempting this course work, you are advised to go through the training content and videos
given at [https://github.com/ncl-iot-team/csc8110]. Apart from providing basic tutorial,
an instructional video (video name: Pre VirtualBox Tutorial) is available which provides details
on:
• how to download and install VirtualBox in your laptop/PC and
• how run a virtual machine image, which is packaged with all necessary programming environments required for this coursework, in your laptop/PC.
• how to run your experiments on Azure Labs (if you choose to do so instead)
Specification of Tasks
The coursework consists of 5 tasks. Tasks 1-4 need to be done by both command line
and SDK solutions.
Task 1: Deploy a web application component in Docker Environment (7.5 Marks)
Task Objective : Understand and learn how to pull and run Docker image from Docker Hub
using the command line interface.
Hints : Please first watch the video tutorial (Task1 EmergingTechnologies Coursework) given
at [https://github.com/ncl-iot-team/csc8110].
A sample Java web application component image, named “nclcloudcomputing/javabenchmarkapp”, has been uploaded to the Docker Hub. The image contains a ready-to-use implementation of a web application deployed in a Tomcat server (an open source web server). In terms of
computational logic, the web application implements a prime number check on a large number.
By doing so, the application can generate high CPU and memory load.
1. Pull and run the Docker image “nclcloudcomputing/javabenchmarkapp” from Docker Hub
in the virtual machine running on your laptop/PC. Perform these tasks using the command
line interface (CLI).
2. Verify whether the web application is working by pointing your web browser to [http:
//localhost:8080/primecheck]. The page should display the time taken to perform a
prime number checking calculation.
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Task 2: Deploy a complex web application stack in Docker Environment (12.5
Marks)
Task Objective : Understand how to create a complex web application stack consisting of multiple Docker images delivering distinct services including web server, load generation, database
server, monitoring engine, and visualisation. This task will also help you in understanding how
native Docker Compose and Swarm techniques can be leveraged to manage and deploy such
complex web application stacks.
Hints : You are advised to carefully view the relevant video tutorial (Introduction to Docker
swarm), given at [https://github.com/ncl-iot-team/csc8110], before attempting the below
sub-tasks.
1. Pull the following images from the Docker Hub:
(a) load generator Docker image (locustio/locust);
(b) MongoDB Docker image (mongo);
(c) cAdvisor monitoring Docker image (gcr.io/cadvisor/cadvisor);
(d) Docker Swarm Visualizer (dockersamples/visualizer);
2. Define a Docker compose file which contains all necessary configurations and instructions
for deploying and instantiating the above (step 1) Docker images (web server, locust, MongoDB, cAdvisor, Docker Swarm Visualizer);
3. Create a web application topology, as shown in Figure 1, on a single Docker swarm node
by using the above (step 2) Docker compose configurations.
Docker Host
Docker Swarm
Mongo DB
Instance
Web Application
cAdvisor
Instance
Load Generator Docker Swarm
Visualizer
Client
Bridge networks
Figure 1: Deployment Architecture
Task 3: Build your own Docker image and push it to the Docker Hub (10 Marks)
Task Objective : Understand how to build a Docker image from scratch and how to push it
to Docker Hub so that other developers can view, use and extend the same.
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Hints : The video tutorial (Docker Build Tutorial) is provided at [https://github.com/
ncl-iot-team/csc8110].
1. You need to build a Dooker image of a Java program. To download the source code
of the provided program, use the following link [https://github.com/ncl-iot-team/
cadvisor-scraper]. In terms of the application logic, the program implements features
including how to extract the monitoring information from the Docker cAdvisor container
(an running instance of gcr.io/cadvisor/cadvisor image) and how to insert these monitoring information into a MongoDB Docker containter (an running instance of mongo
image). The cAdvisor is able to monitor run-time performance (CPU load, network I/O
and memory usage) of all instances of Docker images running within a Docker Swarm.
2. Edit the provided Java code using the IntelliJ editor (already installed in the provided
virtual image). The instructions in Java code that need editing include containerId, mongodbAddress, and endpoint address.
3. After code modification, pack the program as a standalone Docker image and push it to
the Docker Hub. Name the image as cadvisor-scraper.
Task 4: Fully deploy and run the complex web application stack and undertake
performance benchmarking activities (5 Marks)
Task Objective : To learn and understand how to deploy complex web application stack,
how to benchmark their run-time performance, and how to store the benchmarking results into
a database server.
Hints : The video tutorial (Introduction to cAdvisor) is provided at [https://github.com/
ncl-iot-team/csc8110].
1. Pull cAdvisor-scraper image, created in Task 3, back to the virtual machine on your laptop/PC.
2. Stop your Docker Swarm instantiated in Task 2.
3. Modify the Docker compose file to include the new cAdvisor-scraper image.
4. Re-run the new Docker compose file to launch the image.
5. Send dynamic workload (e.g., http requests) to the Web Application Image Instance (in
other words Web Application Container) by using the Load Generator Instance.
6. Verify that cAdvisor-scraper instance is logging the run-time performance statistics to the
MongoDB instance.
Task 5: Report (15 Marks)
Prepare the Final Report in plain English. The report should consist of:
1. Detailed response to each task and related sub-tasks;
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2. Screenshots of running services in the Docker Environment;
3. Screenshots of Code Snippets and/or Docker console;
4. Plots of Benchmarking results using any of the tools (e.g. R, MATLAB, Excel).
5. Discussion of the results and related conclusions.
Assessment1(1)(4)
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