CLOUD COMPUTING - 2022/3
Module code: COMM034
In light of the Covid-19 pandemic the University has revised its courses to incorporate the ‘Hybrid Learning Experience’ in a departure from previous academic years and previously published information. The University has changed the delivery (and in some cases the content) of its programmes. Further information on the general principles of hybrid learning can be found at: Hybrid learning experience | University of Surrey.
We have updated key module information regarding the pattern of assessment and overall student workload to inform student module choices. We are currently working on bringing remaining published information up to date to reflect current practice in time for the start of the academic year 2021/22.
This means that some information within the programme and module catalogue will be subject to change. Current students are invited to contact their Programme Leader or Academic Hive with any questions relating to the information available.
The need for computational power and data storage continues to drive demand for more highly capable systems. Highly data intensive applications demand fast access to terabytes, petabytes, even exabytes of storage; processor intensive applications demand access to various types of processors in various configurations. Such applications are increasingly being developed in both scientific and industrial contexts and need to be variously scalable and supportable for large numbers of geographically distributed users. This module will provide insights into how Cloud Computing attempts to meet the varying needs of such applications.
GILLAM Lee (Computer Sci)
Number of Credits: 15
ECTS Credits: 7.5
Framework: FHEQ Level 7
JACs code: I100
Module cap (Maximum number of students): N/A
Overall student workload
Independent Learning Hours: 106
Laboratory Hours: 22
Captured Content: 22
Prerequisites / Co-requisites
- Defining Cloud Computing and placing it in the context of related systems
- Understanding and using Cloud Technologies
- Developing Cloud applications
- Persistence, Storage, and Data Clusters
- Justification for Cloud Computing in scientific and industrial contexts
- System, Data and Application Security
- Price-related performance of Cloud Systems
- Legislative, Regulatory, and Environmental aspects of Cloud Computing
|Assessment type||Unit of assessment||Weighting|
The assessment strategy is designed to provide students with the opportunity to demonstrate that they have achieved the module learning outcomes.
Thus, the summative assessment for this module consists of:
- Two individual courseworks with a set of theoretical and practical tasks.
The first coursework addresses LO1 and LO2. The second coursework addresses LO3, LO4 and LO5.
- A viva involving demonstration and discussion of the resulting system.
This addresses LO2, LO4 and LO5.
The two courseworks will be due around weeks 7 or 8 and 11, respectively, with the former dependent on when the Easter break falls. The viva will take place during the examination period.
Formative assessment and feedback
Evaluative feedback on the first coursework is intended for use formatively for subsequent parts.
- The aim of this module is to provide a practical introduction to applications which place significant and varying demands on computational resources, with a focus on the emerging topic of Cloud Computing. Current considerations of Clouds are variously all-encompassing. The module will introduce the key concepts of Clouds and address relationships to other distributed computing paradigms such as Grids, High Performance Computing (HPC) and Peer to Peer (P2P) systems for computationally-intensive and data-intensive applications. Technologies variously used for Clouds in a variety of academic and industrial contexts (e.g. Amazon EC2, Google App Engine, Apache Hadoop, Eucalyptus, OpenStack, Condor) will be introduced to demonstrate principles and concepts including architectures, systems, supporting software applications, resource management and information services.
|1||Articulate an understanding of the need for and evolution of Cloud Computing and the various challenges involved||KC|
|2||Critically evaluate technologies such as Amazon EC2, Google App Engine and Apache Hadoop in specific industrial and academic contexts||KCT|
|3||Demonstrate a critical appreciation of related approaches, technologies and systems||KC|
|4||Contrast and evaluate architectures, key characteristics, and requirements of Cloud infrastructures||KCT|
|5||Specify, design, implement and critically evaluate solutions to data or computationally intensive problems by applying relevant knowledge of architectures, systems and software||KPT|
C - Cognitive/analytical
K - Subject knowledge
T - Transferable skills
P - Professional/Practical skills
Methods of Teaching / Learning
The learning and teaching strategy is designed to achieve the module aims.
The learning and teaching methods include:
- 22 hours of lectures incorporating in-class discussions
- 12 hours of pre-prepared computing labs
- 10 hours of supported lab-based and student-led coursework development
- Research tasks set in lectures in preparation for subsequent lectures, including guided background reading
Students will be expected to undertake self-study where necessary, and to prepare appropriately for , assessments.
Indicated Lecture Hours (which may also include seminars, tutorials, workshops and other contact time) are approximate and may include in-class tests where one or more of these are an assessment on the module. In-class tests are scheduled/organised separately to taught content and will be published on to student personal timetables, where they apply to taken modules, as soon as they are finalised by central administration. This will usually be after the initial publication of the teaching timetable for the relevant semester.
Upon accessing the reading list, please search for the module using the module code: COMM034
Programmes this module appears in
|Information Security MSc||2||Optional||A weighted aggregate mark of 50% is required to pass the module|
|Data Science MSc||2||Compulsory||A weighted aggregate mark of 50% is required to pass the module|
|Computer and Internet Engineering MEng||2||Optional||A weighted aggregate mark of 50% is required to pass the module|
Please note that the information detailed within this record is accurate at the time of publishing and may be subject to change. This record contains information for the most up to date version of the programme / module for the 2022/3 academic year.