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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-ordinatorSemester 1: Alessandra Mileo
Semester 2: Alessandra Mileo
Autumn: Alessandra Mileo
Module TeachersAlessandra Mileo
Michael Scriney
NFQ level 9 Credit Rating 7.5
Pre-requisite None
Co-requisite None
Compatibles None
Incompatibles None
Repeat examination

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.

Workload Full-time hours per semester
Type Hours Description
Directed learning36Online learning material on FutureLearn
Independent Study151No 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 Assessment40% Examination Weight60%
Course Work Breakdown
TypeDescription% of totalAssessment Date
Group assignmentDevelop a cloud application: obtain, explore, curate, and analyse a massive dataset, applying relevant analytical approaches, deploy on a cloud platform and report on the results25%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

    Programme or List of Programmes
    GCCMGraduate Certificate in Computing
    MCMM.Sc. in Computing
    MCMECMicro-Credential Modules Eng & Comp
    SMPECSingle Module Programme (Eng & Comp)

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