This unit introduces cloud computing as the provision of computing resources. Students are exposed to modern systems architectures and software development kits that, together, provide cloud computing frameworks. Students will learn about different aspects of the design, development, provisioning and management of cloud-based applications.
Students will gain a sound understanding of cloud-based computing and the opportunities that it provides for a diverse range of computing applications. Special attention will be made to security of cloud based applications and the different strategies that are available in these deployments.
An overarching goal of the unit is to provide students with an opportunity to undertake problem identification, analysis and solution and to apply these skills to the field of Cloud Computing.
The course is based on the cloud services of Amazon Web Services and a range of open source and other products. It is not possible in a course such as this to cover all of the products that these cloud services provide. The theme has been to concentrate on the most common use cases of [a] using AWS to deploy web applications utilising data sources such as databases and [b] data analytics and machine learning.
Students are able to
- Understand cloud services, there motivation, design and implementation
- Understand the basics of virtualisation of hardware, networks and security
- Understand application architectures and how they meet specific requirements and needs
- Understand how to achieve scalability and security in a cloud-based architecture
- Use DevOps to deploy and manage the creation and updating of software environments
- Use cloud services to carry out specific use cases such as data analytics, machine learning and other artificial intelligence tasks
- Write code in Python using a variety of SDKs to achieve the above where appropriate
The course consists of 12 weeks of lectures and 10 weeks of practical lab classes (2 points each). Assessment is through a midsem exam and a final exam.
- 20% Labs
- 30% MidSem Exam
- 50% Final Exam
For Labs cilck here.
Motivation for cloud computing, introduction to cloud computing
- CloudComputingIntro (pptx)
- Previous Lecture Recording (2019): Video
- WhatIsTheCloud (pptx)
- Week 1: 4 Questions
Cloud Computing and AWS: Introduction to AWS platform and services, awscli command line and python boto programming interfaces
- Lecture Recording 2021 Week 2
- AWSIntro (pptx)
- Boto (pptx)
- Week 2: 2 Questions
Computer virtualisation. Background and different approaches. Containers and Docker
- Lecture Video 2021 - Week 3
- Assignments for week 3 Available
- Virtualisation (pptx)
- Lab 2 Recording 2021- EC2, Boto3, Docker
- Week 3: 2 Questions
AWS storage EBS, S3, DynamoDB
- AWS Storage (pptx)
- Previous Lecture Recordings:
- Lab 3 Recording - S3 and DynamoDB
- Week 4: 4 Questions
AWS security and identity management
- Lecture Video 2021 - Week 5
- IAM (Identity Access Management) (pptx)
- Previous Lecture Recording:
- Lab 4 Recording - Policies and KMS
- Week 5: 3 Questions
Networking, IP addressing, subnets, routing virtual private clouds
- Networking (pptx)
- Previous Lecture Recording (2018):
- Lab 5 Recording - Load balancers
- Week 6: 2 Questions
There will be no scheduled labs or content for lectures and the time can be used for completing the pending labs.
Web architectures using python as a model with RDBMS
- WebArchitecture (pptx)
- Week 8: 2 Questions
Software controlled deployment of services and software using Chef
- DevOps (pptx)
- Week 9: 2 Questions
Machine learning using classification and categorisation services
- Week 10 (final): 1 Question
More AI services including text analysis, image analysis, chatbots and data analysis using Jupyter and SageMaker
- MoreAI (pptx)
- Previous Lecture Recordings:
Mobile application integration and services and IoT integration using cloud services
- MobileIntegration (pptx)
For Lectures cilck here.
This semester all labs will be assessed as "Lab notes". You should follow all steps in each lab and include your own comments. In addition, include screenshots showing the output for every commandline instruction that you execute in the terminal and any other relevant screenshots that demonstrate you followed the steps from the corresponding lab. Please also include any bash or python script that you create and the corresponding output you get when executing it. Please submit a single PDF file. The formatting is up to you but a well organised structure of your notes is appreciated.
- No Labs for Week 1.
- Instructions: Lab1 Intro and setup of environment
- Instructions: Lab2 EC2 and docker
- Instructions: Lab3 S3 and DynamoDB Creating CloudStorage application
- Instructions: Lab4 IAM, KMS and AES encryption
- Previous Recordings (2020): Lab Video
- Instructions: Lab 5 Networks and VPC
- Previous Recordings (2020): Lab Video
- No labs this week to make up for the assignment
- Instructions: Lab 6 Web applications, Django and ELB
- Previous Recordings (2020): Lab Video
- Instructions: Lab 7 DevOps
- Previous Recordings (2020): Lab Video
- New Lab 8 (2021 version - SageMaker)Lab Instructions
- Lab 8 (Old - AWS ML Deprecated): Lab 8 Machine learning using classification and categorisation services
- Previous Recordings (2020): Lab Video
- Instructions: Lab 9 More AI: Text analysis, image analysis, chatbots
- Previous Recordings (2020): Lab Video