COMP08164 2023 Cloud Computing

General Details

Full Title
Cloud Computing
Transcript Title
Cloud Computing
N/A %
Subject Area
COMP - 0613 Computer Science
COEL - Computing & Electronic Eng
08 - NFQ Level 8
05 - 05 Credits
Start Term
2023 - Full Academic Year 2023-24
End Term
9999 - The End of Time
Colm Davey
Programme Membership
SG_KCMPU_L08 202300 Higher Diploma in Science in Computing

The module cloud computing will introduce the learner to the domain of cloud computing, architecting for a cloud environment and evaluating appropriate cloud services. 

This module may also allow the learner the opportunity to undertake certification with a public cloud provider.

Learning Outcomes

On completion of this module the learner will/should be able to;


Describe the evolution of cloud computing and the principles of good architectural design


Describe the various storage options, compute services, networking components and security services in a public cloud environment


Configure and deploy appropriate cloud services for web-scale use cases


Describe best practices for building and deploying an optimised cloud environment

Teaching and Learning Strategies

Short videos and appropriate reading materials will be made available to learners at the start of each week. Learners will be given small weekly tasks and encouraged to avail of Moodle to ask and answer each other's questions.

Individual feedback will be provided by the lecturer.

Module Assessment Strategies

One assessment (research-based) will take place in week 5 (25%). In week 9, the learner will undertake an on-site or remotely proctored exam (30%). From week 10 until the end of the semester, the learner will undertake a substantial project (45%). 

Repeat Assessments

One repeat project covering all learning outcomes will be provided. The learner may be brought on-site for an interview

Indicative Syllabus

Describe the evolution of cloud computing and the principles of good architectural design

- The evolution of cloud computing
- The essential characteristics of the cloud computing model (on-demand self-service, broad network access, resource pooling, rapid elasticity, measured service etc.)
- Cloud computing providers
- Designing for HA, load balancing, failover policies
- Cloud deployment models (private, community, public, hybrid etc.)

Describe the various storage options, compute services, networking components and security services in a public cloud environment

Server and database options
- Identity services (users, groups, roles)
- Virtual networks, subnets, access via SGs and NACLs
- Storage (elastic block store, simple storage service)
- Implement data security

Configure and deploy appropriate cloud services for web-scale use cases

- Service models available (PaaS, SaaS, IaaS, BaaS etc.)
- Storage (block, object, file)
- Load balancing, failover policies, lifecycle policies
- Routing, scaling out and in via a scaling group
- Deploying services across regions or AZs
- Data storage, archiving and analysis

Describe best practices for building and deploying an optimised cloud environment

- Operational excellence
- Security

- Reliability
- Performance efficiency
- Cost optimisation

Coursework & Assessment Breakdown

Coursework & Continuous Assessment
100 %

Coursework Assessment

Title Type Form Percent Week Learning Outcomes Assessed
1 Review a core cloud service and implement in order to facilitate HA and scaling Coursework Assessment Assignment 25 % Week 5 1,2
2 Provide an architected solution (with a cloud deployment component) for a given use case Coursework Assessment Closed Book Exam 30 % Week 9 2,3
3 Architect, deploy and secure multiple cloud services for an SME Project Project 45 % OnGoing 2,3,4

Full Time Mode Workload

Type Location Description Hours Frequency Avg Workload
Lecture Lecture Theatre Lecture 1 Weekly 1.00
Practical / Laboratory Computer Laboratory Lab Class 3 Weekly 3.00
Independent Learning Not Specified Self-directed learning 3 Weekly 3.00
Total Full Time Average Weekly Learner Contact Time 4.00 Hours

Online Learning Mode Workload

Type Location Description Hours Frequency Avg Workload
Lecture Online Online Lecture 1 Weekly 1.00
Independent Learning Online Self-directed learning 3 Weekly 3.00
Practical / Laboratory Online Online lab class 2 Weekly 2.00
Total Online Learning Average Weekly Learner Contact Time 3.00 Hours

Required & Recommended Book List

Recommended Reading
2016-10-07 AWS Certified Solutions Architect Official Study Guide: Associate Exam (Aws Certified Solutions Architect Official: Associate Exam) Sybex
ISBN 1119138558 ISBN-13 9781119138556
Recommended Reading
2015-09-25 DynamoDB Cookbook Packt Publishing
ISBN 1784393754 ISBN-13 9781784393755

Over 90 hands-on recipes to design Internet scalable web and mobile applications with Amazon DynamoDB About This Book * Construct top-notch mobile and web applications with the Internet scalable NoSQL database and host it on cloud * Integrate your applications with other AWS services like AWS EMR, AWS S3, AWS Redshift, and AWS CloudSearch etc. in order to achieve a one-stop application stack * Step-by-step implementation guide that provides real-world use with hands-on recipes Who This Book Is For This book is intended for those who have a basic understanding of AWS services and want to take their knowledge to the next level by getting their hands dirty with coding recipes in DynamoDB. What You Will Learn * Design DynamoDB tables to achieve high read and write throughput * Discover best practices like caching, exponential back-offs and auto-retries, storing large items in AWS S3, storing compressed data etc. * Effectively use DynamoDB Local in order to make your development smooth and cost effective * Implement cost effective best practices to reduce the burden of DynamoDB charges * Create and maintain secondary indexes to support improved data access * Integrate various other AWS services like AWS EMR, AWS CloudSearch, AWS Pipeline etc. with DynamoDB In Detail AWS DynamoDB is an excellent example of a production-ready NoSQL database. In recent years, DynamoDB has been able to attract many customers because of its features like high-availability, reliability and infinite scalability. DynamoDB can be easily integrated with massive data crunching tools like Hadoop /EMR, which is an essential part of this data-driven world and hence it is widely accepted. The cost and time-efficient design makes DynamoDB stand out amongst its peers. The design of DynamoDB is so neat and clean that it has inspired many NoSQL databases to simply follow it. This book will get your hands on some engineering best practices DynamoDB engineers use, which can be used in your day-to-day

Recommended Reading
2016-01-05 Big Data Fundamentals: Concepts, Drivers, and Techniques (Prentice Hall Service Technology Series from Thomas Erl) Prentice Hall
ISBN 0134291077 ISBN-13 9780134291079

"This text should be required reading for everyone in contemporary business." --Peter Woodhull, CEO, Modus21 "The one book that clearly describes and links Big Data concepts to business utility." --Dr. Christopher Starr, PhD "Simply, this is the best Big Data book on the market!" --Sam Rostam, Cascadian IT Group " of the most contemporary approaches I've seen to Big Data fundamentals..." --Joshua M. Davis, PhD The Definitive Plain-English Guide to Big Data for Business and Technology Professionals Big Data Fundamentals provides a pragmatic, no-nonsense introduction to Big Data. Best-selling IT author Thomas Erl and his team clearly explain key Big Data concepts, theory and terminology, as well as fundamental technologies and techniques. All coverage is supported with case study examples and numerous simple diagrams. The authors begin by explaining how Big Data can propel an organization forward by solving a spectrum of previously intractable business problems. Next, they demystify key analysis techniques and technologies and show how a Big Data solution environment can be built and integrated to offer competitive advantages. * Discovering Big Data's fundamental concepts and what makes it different from previous forms of data analysis and data science* Understanding the business motivations and drivers behind Big Data adoption, from operational improvements through innovation* Planning strategic, business-driven Big Data initiatives* Addressing considerations such as data management, governance, and security* Recognizing the 5 "V" characteristics of datasets in Big Data environments: volume, velocity, variety, veracity, and value* Clarifying Big Data's relationships with OLTP, OLAP, ETL, data warehouses, and data marts* Working with Big Data in structured, unstructured, semi-structured, and metadata formats* Increasing value by integrating Big Data resources with corporate performance monitoring* Understanding how Big Data leverages distributed and para

Recommended Reading
2016 Getting Started with AWS Amazon Media, ASIN: B007X6SMD6

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