Registry
Module Specifications
Archived Version 2021 - 2022
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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. | |||||||||||||||||||||||||||||||||||||
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 |
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Indicative Content and
Learning Activities Introduction to Cloud SystemsBasics of Cloud Systems. Cloud Architecture and Infrastructure. Cloud Platforms and Economics. Cloud Engineering and Challenges.Big Data computing in CloudIntroduction 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 VirtualisationFundamentals of cloud virtualization. Hypervisor technologies. Cloud containers and use cases. Cloud storage and network virtualization.Cloud Scheduling TechnologiesScheduling in Cloud Systems. Load-balancing in Cloud services. Workflow scheduling in Cloud computing. Cloud Resource Optimization.Cloud Management and OperationsIntroduction 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. | |||||||||||||||||||||||||||||||||||||
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Other Resources None | |||||||||||||||||||||||||||||||||||||
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