Intelligent Cloud Systems and Architectures
Credit points
(Generally, 1 credit = 10 hours of classes and independent study.)
Location
Online Real Time
VU Brisbane
VU Sydney
Prerequisites
NIT1104 - Computer Networks
(Or equivalent to be determined by unit coordinator)
Co-requisites
(Corequisite units must be studied concurrently with this unit)
Overview
This unit introduces you to intelligent cloud systems, where AI-driven tools and platforms enhance cloud architecture, resource management, and application deployment. You will explore how AI-driven optimisation techniques are transforming traditional cloud systems, enabling smarter, more autonomous cloud solutions. You will examine next-generation cloud deployment models and cloud services, focusing on compute, storage, security, and monitoring services enhanced by AI. You will gain hands-on experience with AI-assisted cloud management tools for tasks like predictive scaling, automated resource allocation, real-time performance optimisation, and seamless cloud migrations: preparing you for roles in cloud engineering, DevOps, or infrastructure architecture in AI-enabled environments.
Learning Outcomes
On successful completion of this unit, students will be able to:
- Evaluate the influence of AI-driven tools in cloud system design, optimisation, and resource management.
- Design intelligent cloud architectures with AI-driven tools to automate the deployment, scaling, and resource management.
- Develop reliable, scalable, and secure cloud infrastructure with AI-enhanced tools.
- Apply AI-enhanced tools for real-time monitoring, predictive scaling, and proactive management of cloud systems.
- Assess cloud migration strategies, integrating AI tools to automate migration processes, minimise downtime, and ensure compliance with industry standards and security best practices.
Assessment
For Melbourne campuses
Assessment type: Laboratory Work | Grade: 20%
Hands-on labs
Assessment type: Project | Grade: 40%
AI-Enhanced Cloud Implementation project with demonstration and Q&A verification.
Assessment type: Case Study | Grade: 40%
Problem-based solution design and presentation with verified live interaction.
Project:
Includes structured milestone reviews and in-class progress checks. Individual contributions are logged, validated, and justified through recorded peer review or demonstration to ensure authenticity.
Case Study:
Students apply concepts to unpredictable data science scenarios. Responses are evaluated based on reasoning, method selection, and outcome justification, with an in-class component or oral defence verifying understanding.
Required reading
NA
As part of a course
This unit is not compulsory for any specific course. Depending on the course you study, this unit may be taken as an elective.