Module Code: |
H9CLAR |
Long Title
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Cloud Architectures
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Title
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Cloud Architectures
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Module Level: |
LEVEL 9 |
EQF Level: |
7 |
EHEA Level: |
Second Cycle |
Module Coordinator: |
Horacio Gonzalez-Velez |
Module Author: |
Horacio Gonzalez-Velez |
Departments: |
School of Computing
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Specifications of the qualifications and experience required of staff |
PhD degree in computer science or cognate discipline. Experience lecturing in the field. May have industry experience also.
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Learning Outcomes |
On successful completion of this module the learner will be able to: |
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Learning Outcome Description |
LO1 |
Critically compare and contrast distinct parallel and distributed architectures in terms of their functional and non-functional characteristics and associated enabling technologies. |
LO2 |
Demonstrate in-depth knowledge of different types of computing systems for data storing, staging, and processing. |
LO3 |
Evaluate and assess virtualisation and software environments for cloud computing. |
LO4 |
Construct and present a business case for a complex, dynamic high performance computing solution for clouds. |
LO5 |
Apply data governance and ethical frameworks to complex computational problems and recommend cloud-based solutions. |
Dependencies |
Module Recommendations
This is prior learning (or a practical skill) that is required before enrolment on this module. While the prior learning is expressed as named NCI module(s) it also allows for learning (in another module or modules) which is equivalent to the learning specified in the named module(s).
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No recommendations listed |
Co-requisite Modules
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No Co-requisite modules listed |
Entry requirements |
Internal to the programme
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Module Content & Assessment
Indicative Content |
Quantitative Design and Analysis
Computer Architecture review. Classes of Computers. Trends in Technology, Power, and Cost. Dependability
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Performance
Measuring, Reporting, and Summarising Performance. Performance, Price and Power. Amdahl’s Law. Fallacies and Pitfalls.
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Memory Hierarchy
Levels of memory hierarchy. Cache: associativity and optimisations. Main memory. SRAM, DRAM, and SDRAM.
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Memory Systems
Virtual Memory and Virtual Machines. Virtual Machine monitors. Cache coherency. Containers.
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Parallel Computing Architectures
Flynn’s Taxonomy; SIMD vs. MIMD. GPUs, TPUs, FPGAs, Neuromorphic computing. Vector and Loop-Level Parallelism
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Warehouse-scale Computing
Programming Models and benchmarks. Workloads. Computer architecture of warehouse-scale computers.
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Cloud Datacentres
Physical infrastructure, location, and power considerations for data centres.
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Cloud Delivery Models
NIST Model. DGI Data Governance Framework. Concepts for delivering infrastructure, platform, and software as a service.
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Non-functional characteristics of cloud systems
SLAs/QoS, MTTR/MTTF, Availability, Mobility, and Optimisation for Cloud
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Cloud Infrastructures and Services
Computation, storage and general resource deployment; Private and public cloud services (e.g. OpenStack, AWS and GAE service offerings).
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Data-intensive storage management
Graph parallel and Microservices. CAP Theorem; distributed file organisations, application staging.
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Utility Computing
Total cost of ownership. Influence of server cost and power. CAPEX vs. OPEX. ACM Code of Ethics.
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Assessment Breakdown | % |
Coursework | 50.00% |
AssessmentsFull Time
Coursework |
Assessment Type: |
Project |
% of total: |
50 |
Assessment Date: |
Week 10 |
Outcome addressed: |
3,4,5 |
Non-Marked: |
No |
Assessment Description: Develop a complex business case for a cloud computing solution with specific emphasis on technical, ethical, and data governance constraints. |
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End of Module Assessment |
Assessment Type: |
Terminal Exam |
% of total: |
50 |
Assessment Date: |
End-of-Semester |
Outcome addressed: |
1,2 |
Non-Marked: |
No |
Assessment Description: The test will assess learners’ knowledge and understanding of computing architectures, programming models, and storage concepts. |
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Reassessment Requirement |
Repeat examination
Reassessment of this module will consist of a repeat examination. It is possible that there will also be a requirement to be reassessed in a coursework element.
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Reassessment Description Reassessment of this module will be via proctored examination or a project examining all learning outcomes.
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NCIRL reserves the right to alter the nature and timings of assessment
Module Workload
Module Target Workload Hours 0 Hours |
Workload: Full Time |
Workload Type |
Workload Description |
Hours |
Frequency |
Average Weekly Learner Workload |
Practical |
No Description |
24 |
Per Semester |
2.00 |
Lecture |
No Description |
36 |
Per Semester |
3.00 |
Independent Learning Time |
No Description |
190 |
Per Semester |
15.83 |
Total Weekly Contact Hours |
5.00 |
Module Resources
Recommended Book Resources |
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Ian Foster,Dennis B. Gannon. (2017), Cloud Computing for Science and Engineering, MIT Press, Cambridge, p.392, [ISBN: 978-0-262-03724-2].
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J. Hennessy, D. Patterson. (2017), Computer Architecture: A Quantitative Approach, 6. Morgan Kaufmann, Amsterdam, [ISBN: 978-0128119051].
| Supplementary Book Resources |
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Dan C. Marinescu. (2017), Cloud Computing, Morgan Kaufmann, Amsterdam, p.586, [ISBN: 0128128100].
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Kai Hwang. (2017), Cloud Computing for Machine Learning and Cognitive Applications, MIT Press, Cambridge, p.624, [ISBN: 026203641X].
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Maurice Herlihy,Nir Shavit. (2012), The Art of Multiprocessor Programming, Elsevier, Amsterdam, p.508, [ISBN: 0123973376].
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William Gropp,Ewing Lusk,Anthony Skjellum. (2014), Using MPI, MIT Press, Cambridge, p.336, [ISBN: 0262527391].
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Irv Englander. (2014), The Architecture of Computer Hardware, Systems Software, and Networking, 5. Wiley, New York, p.696, [ISBN: 1118322630].
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Joanna Kołodziej,Horacio González-Vélez. (2019), High-Performance Modelling and Simulation for Big Data Applications, Springer, Cham, p.352, [ISBN: 978-3-030-16271-9].
| Recommended Article/Paper Resources |
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R. Buyya et al.. (2019), A Manifesto for Future Generation Cloud
Computing: Research Directions for the
Next Decade, ACM Computing Surveys, 51, p.105:1,
| Supplementary Article/Paper Resources |
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H. González-Vélez, M. Leyton. (2010), A survey of algorithmic skeleton
frameworks: high-level structured
parallel programming enablers, Software: Practice and Experience, 40, p.1135-,
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N. P. Jouppi et al.. (2017), In-Datacenter Performance Analysis of a
Tensor Processing Unit, SIGARCH Comput. Archit. News, 45, p.12,
| Other Resources |
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[website], K. Brittle. (2019), Cloud Computing Subject Guide, Dublin, NCI Library,
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