CLOUD COMPUTING - 2020/1

Module code: COMM034

Module Overview

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.

Module provider

Computer Science

Module Leader

GILLAM Lee (Computer Sci)

Number of Credits: 15

ECTS Credits: 7.5

Framework: FHEQ Level 7

Module cap (Maximum number of students): N/A

Overall student workload

Independent Learning Hours: 106

Lecture Hours: 22

Laboratory Hours: 22

Module Availability

Semester 2

Prerequisites / Co-requisites

None

Module content

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 pattern

Assessment type	Unit of assessment	Weighting
Coursework	COURSEWORK 1	20
Coursework	COURSEWORK 2	60
Oral exam or presentation	VIVA	20

Alternative Assessment

N/A

Assessment Strategy

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.

Module aims

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.

Learning outcomes

		Attributes Developed
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

Attributes Developed

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.

Reading list

https://readinglists.surrey.ac.uk
Upon accessing the reading list, please search for the module using the module code: COMM034

Programmes this module appears in

Programme	Semester	Classification	Qualifying conditions
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 2020/1 academic year.