Aims and Fit of Module

The aim of this module is to train students on the principles of
software architectures based on distributed computing technologies,
design of complex applications and deployment techniques. This
ranges from conventional techniques such as Java RMI to Web-based
middleware techniques such as Web services, and enterprise computing
technologies. The module equips students with experiences and skills
of applying these techniques to complex distributed system
development. It enables students to understand the influence of
artificial intelligence techniques and evaluate their environmental
and societal impact on the solutions to sophisticated distributed
systems. It is a compulsory module for MSc Applied Informatics and
MSc Financial Computing.

Learning outcomes

A. Understand and design architectures for complex distributed
software systems;
B. Effectively design and implement a distributed computing system
to specifications in a creative and consistent manner;
C. Critically evaluate distributed systems using relevant
technologies;
D. Critically analyse the practical and theoretical problems which
exist in distributed computing systems;
E. Participate in the legal, social, ethical and professional
framework in the development of distributed systems;
F. Analyse the influence of artificial intelligence techniques and
evaluate their environmental and societal impact on the solutions to
sophisticated distributed systems, and minimise adverse impacts.

Method of teaching and learning

This module fosters both independent learning and team working, within a framework that includes a series of formal lectures, seminars and computer labs.
Formal lectures: Students are expected to attend two hours of formal lectures and seminars in a typical week during Semester 2.
Seminars: These allow students to familiarise themselves with recent publications relevant to the lectures. Students will discuss and report their findings. Some seminars will invite experts in areas relevant to the module for guest lectures.
Computer labs: The supervised lab sessions allow students to undertake practical exercises with immediate support and feedback.
Private study: In a typical week, students are expected to devote around 10 hours of unsupervised time to private study. This typically includes 4 hours for lecture materials, background reading, reflection and group discussions, and 6 hours for completion of the assessment tasks.