CLOUD COMPUTING - 2017/8

Module code: COMM034

Module provider

Computer Science

Module Leader

GILLAM L Dr (Computer Sci)

Number of Credits

15

ECT Credits

7.5

Framework

FHEQ Level 7

JACs code

I100

Module cap (Maximum number of students)

N/A

Module Availability

Semester 2

Overall student workload

Independent Study Hours: 110

Lecture Hours: 22

Laboratory Hours: 22

Assessment pattern

Assessment type Unit of assessment Weighting
Coursework COURSEWORK 1 30
Coursework COURSEWORK 2 40
Oral exam or presentation VIVA 30

Alternative Assessment

N/A

Prerequisites / Co-requisites

None

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 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
Articulate an understanding of the need for and evolution of Cloud Computing and the various challenges involved KC
Critically evaluate technologies such as Amazon EC2, Google App Engine and Apache Hadoop in specific industrial and academic contexts KCT
Demonstrate a critical appreciation of related approaches, technologies and systems KC
Contrast and evaluate architectures, key characteristics, and requirements of Cloud infrastructures KCT
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

Module content


Defining Cloud Computing and placing it in the context of Grids, HPC and P2P systems
Cloud Technologies
Infrastructure as a Service (IaaS), Platform as a Service (PaaS) and Software as a Service (SaaS)
Persistence and Storage
Justification for Cloud Computing in scientific and industrial contexts
Data and Application Security in the Cloud
Legislative and Regulatory challenges of Cloud Computing
Developing Cloud applications
Cloud Economics and Green IT

Methods of Teaching / Learning

The learning and teaching strategy is designed to achieve the module aims.

The learning and teaching methods include:


20 hours of lectures incorporating in-class discussions
12 hours of pre-prepared computing labs
8 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.

 

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 for the presentation and software demonstration.


           This addresses LO2, LO4 and LO5.

The two courseworks will be due around weeks 7 and 11, respectively. 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.

 

Reading list

Reading list for CLOUD COMPUTING : http://aspire.surrey.ac.uk/modules/comm034

Programmes this module appears in

Programme Semester Classification Qualifying conditions
Computer and Internet Engineering MEng 2 Optional A weighted aggregate mark of 50% is required to pass the module
Information Security MSc 2 Optional A weighted aggregate mark of 50% is required to pass the module
Information Systems MSc 2 Compulsory 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 2017/8 academic year.