H9CLAR - Cloud Architectures

Module Code: H9CLAR
Long Title Cloud Architectures
Title Cloud Architectures
Module Level: LEVEL 9
EQF Level: 7
EHEA Level: Second Cycle
Credits: 10
Module Coordinator: Horacio Gonzalez-Velez
Module Author: Horacio Gonzalez-Velez
Departments: School of Computing
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. 
Learning Outcomes
On successful completion of this module the learner will be able to:
# 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).
No recommendations listed
Co-requisite Modules
No Co-requisite modules listed
Entry requirements

Internal to the programme
 

Module Content & Assessment

Indicative Content
Quantitative Design and Analysis
Computer Architecture review. Classes of Computers. Trends in Technology, Power, and Cost. Dependability
Performance
Measuring, Reporting, and Summarising Performance. Performance, Price and Power. Amdahl’s Law. Fallacies and Pitfalls.
Memory Hierarchy
Levels of memory hierarchy. Cache: associativity and optimisations. Main memory. SRAM, DRAM, and SDRAM.
Memory Systems
Virtual Memory and Virtual Machines. Virtual Machine monitors. Cache coherency. Containers.
Parallel Computing Architectures
Flynn’s Taxonomy; SIMD vs. MIMD. GPUs, TPUs, FPGAs, Neuromorphic computing. Vector and Loop-Level Parallelism
Warehouse-scale Computing
Programming Models and benchmarks. Workloads. Computer architecture of warehouse-scale computers.
Cloud Datacentres
Physical infrastructure, location, and power considerations for data centres.
Cloud Delivery Models
NIST Model. DGI Data Governance Framework. Concepts for delivering infrastructure, platform, and software as a service.
Non-functional characteristics of cloud systems
SLAs/QoS, MTTR/MTTF, Availability, Mobility, and Optimisation for Cloud
Cloud Infrastructures and Services
Computation, storage and general resource deployment; Private and public cloud services (e.g. OpenStack, AWS and GAE service offerings).
Data-intensive storage management
Graph parallel and Microservices. CAP Theorem; distributed file organisations, application staging.
Utility Computing
Total cost of ownership. Influence of server cost and power. CAPEX vs. OPEX. ACM Code of Ethics.
Assessment Breakdown%
Coursework50.00%

Assessments

Full 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.
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.
No Workplace Assessment
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.
Reassessment Description
Reassessment of this module will be via proctored examination or a project examining all learning outcomes.

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
  • Ian Foster,Dennis B. Gannon. (2017), Cloud Computing for Science and Engineering, MIT Press, Cambridge, p.392, [ISBN: 978-0-262-03724-2].
  • J. Hennessy, D. Patterson. (2017), Computer Architecture: A Quantitative Approach, 6. Morgan Kaufmann, Amsterdam, [ISBN: 978-0128119051].
Supplementary Book Resources
  • Dan C. Marinescu. (2017), Cloud Computing, Morgan Kaufmann, Amsterdam, p.586, [ISBN: 0128128100].
  • Kai Hwang. (2017), Cloud Computing for Machine Learning and Cognitive Applications, MIT Press, Cambridge, p.624, [ISBN: 026203641X].
  • Maurice Herlihy,Nir Shavit. (2012), The Art of Multiprocessor Programming, Elsevier, Amsterdam, p.508, [ISBN: 0123973376].
  • William Gropp,Ewing Lusk,Anthony Skjellum. (2014), Using MPI, MIT Press, Cambridge, p.336, [ISBN: 0262527391].
  • Irv Englander. (2014), The Architecture of Computer Hardware, Systems Software, and Networking, 5. Wiley, New York, p.696, [ISBN: 1118322630].
  • 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
  • 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,
    https://doi.org/10.1145/3241737
Supplementary Article/Paper Resources
  • 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-,
    https://doi.org/10.1002/spe.1026
  • N. P. Jouppi et al.. (2017), In-Datacenter Performance Analysis of a Tensor Processing Unit, SIGARCH Comput. Archit. News, 45, p.12,
    https://doi.org/10.1145/3140659.3080246
Other Resources
Discussion Note: