Cloud Systems

This microcredential provides a postgraduate-level comprehensive introduction to cloud systems with an emphasis on in-demand and advanced topics such as big data computing, cloud resource virtualization and scheduling, and cloud management systems. It is designed in five courses with four focusing on key topics. Each course includes four topics corresponding to the main theme. The first course focuses on the concepts, architecture, infrastructure, technological foundations, and technology challenges of cloud systems. The second course targets big data processing and analytics in cloud centres. The third course concentrates on virtualisation technology in cloud systems.The fourth course covers the techniques on resource provisioning and task scheduling in cloud. The final course introduces the mechanisms and practical tools on cloud system management and operations. From the module’s outcomes, the student will have a deep understanding of the architecture and the core techniques used in the current cloud systems. Moreover, they will be able to develop their own ideas on innovation applications using cloud and/or new approaches on optimising cloud systems.

1. To summarise the main concepts of cloud systems including deployment models, pricing strategies, cloud economics, cloud management and operations, cloud network function virtualization, load balancing and scheduling.

2. To compare and contrast the core technologies used in cloud systems including parallel data processing technologies, batch computation and in-memory computation approaches.

3. To demonstrate an understanding of the technical challenges and the potential security and privacy issues for cloud adoption and growth.

4. To implement massively parallel data processing using high-level programming primitives.

5. To discuss the role and advantages of virtualisation in cloud computing.

6. To understand the principles and techniques of hypersors and their features.

7. To explain the differences between Docker containers and virtual machines.

8. To apply detailed knowledge and practices of Docker and KVM in cloud virtualisation.

9. To attempt generating new ideas and innovations on scheduling techniques for cloud systems.

10. To design and launch a distributed Hadoop Cluster on a cloud platform.

11. To discuss case studies and understand the rationale behind cloud related choices.

12. To understand synergies and complementarities among schedulers, VM and containers technologies

A Primary Honours degree, Level 8 in Electronic/Electrical/Computer Engineering, Applied Physics, Computer Sciences or other Cognate/Engineering Disciplines. Applications are also invited from diverse educational and/or employment backgrounds, with applications evaluated on a case-by-case basis. 
And also to indicate the required documentation:

• Please provide Academic Transcripts for final year of study where appropriate (English translation)
• All applicants must submit a copy of their passport
• Please upload a CV
• If applicable, evidence of competence in the English language as per DCU entry requirements.  Please see here.

There is no availability for a deferred entry onto a micro-credential.

For further information regarding the HCI learner subsidy eligibility criteria please click here. (https://hea.ie/skills-engagement/hci-pillar-3-micro-credentials-learner-fee-subsidy/).