Module: Cloud Systems

Module Specifications..

Current Academic Year 2023 - 2024

Please note that this information is subject to change.

Module Title	Cloud Systems
Module Code	CA687I
School	School of Computing
Module Co-ordinator	Semester 1: Alessandra Mileo Semester 2: Alessandra Mileo Autumn: Alessandra Mileo
Module Teachers	Alessandra Mileo Michael Scriney
NFQ level	9	Credit Rating	7.5
Pre-requisite	None
Co-requisite	None
Compatibles	None
Incompatibles	None

Repeat examination

Description

This module provides a postgraduate-level comprehensive introduction to cloud systems with an emphasis on 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 centers. The third course concentrates on virtualization 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. As the outcomes of this module, you will have a deep understanding about the architecture and the core techniques used in the current cloud systems. Moreover, you will be able to develop your own ideas on innovation applications using cloud and/or new approaches on optimizing cloud systems.

Learning Outcomes

1. Summarize 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. Compare and contrast the core technologies used in cloud systems including parallel data processing technologies, batch computation and in-memory computation approaches.
3. Demonstrate an understanding of the technical challenges and the potential security and privacy issues for cloud adoption and growth.
4. Implement massively parallel data processing using high-level programming primitives.
5. Discuss the role and advantages of virtualisation in cloud computing.
6. Understand the principles and techniques of hypersors and their features.
7. Explain the differences between Docker containers and virtual machines.
8. Apply detailed knowledge and practices of Docker and KVM in cloud virtualization.
9. Attempt to generate new ideas and innovations on scheduling techniques for cloud Systems.
10. Design and launch a distributed Hadoop Cluster on a cloud platform.
11. Discuss case studies and understand the rationale behind cloud related choices.
12. Understand synergies and complementarities among schedulers, VM and containers Technologies.

*Type*	*Hours*	*Description*
Workload	Full-time hours per semester
Directed learning	36	Online learning material on FutureLearn
Independent Study	151	No Description
Total Workload: 187

All module information is indicative and subject to change. For further information,students are advised to refer to the University's Marks and Standards and Programme Specific Regulations at: http://www.dcu.ie/registry/examinations/index.shtml

Indicative Content and Learning Activities

Introduction to Cloud Systems
Basics of Cloud Systems. Cloud Architecture and Infrastructure. Cloud Platforms and Economics. Cloud Engineering and Challenges.

Big Data computing in Cloud
Introduction to Apache Hadoop (HDFS, YARN, MapReduce). Big data computing using Pig and Hive. The Apache Spark system and its core programming. The core concepts of Apache Storm and its working example.

Cloud Virtualisation
Fundamentals of cloud virtualization. Hypervisor technologies. Cloud containers and use cases. Cloud storage and network virtualization.

Cloud Scheduling Technologies
Scheduling in Cloud Systems. Load-balancing in Cloud services. Workflow scheduling in Cloud computing. Cloud Resource Optimization.

Cloud Management and Operations
Introduction to cloud management and operations. Amazon Web Services and Elastic Map Reduce. Cases Discussion: AWS and beyond. Introduction to Openstack. Comparison of cloud management in VMs, Schedulers and Containers.

Assessment Breakdown
Continuous Assessment	40%	Examination Weight	60%

Type	Description	% of total	Assessment Date
Course Work Breakdown
Group assignment	Develop a cloud application: obtain, explore, curate, and analyse a massive dataset, applying relevant analytical approaches, deploy on a cloud platform and report on the results	25%	Week 7
Group project	Use SDN technology to design a real application based on Mininet.	15%	n/a

Reassessment Requirement Type
Resit arrangements are explained by the following categories; 1 = A resit is available for all components of the module 2 = No resit is available for 100% continuous assessment module 3 = No resit is available for the continuous assessment component
This module is category 1

Indicative Reading List

Other Resources

None

Programme or List of Programmes

GCCM	Graduate Certificate in Computing
MCM	M.Sc. in Computing
MCMEC	Micro-Credential Modules Eng & Comp
SMPEC	Single Module Programme (Eng & Comp)

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