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Module

CSC8103 : Distributed Algorithms

  • Offered for Year: 2021/22
  • Module Leader(s): Dr Paul Ezhilchelvan
  • Owning School: Computing
  • Teaching Location: Newcastle City Campus
Semesters
Semester 1 Credit Value: 10
ECTS Credits: 5.0

Aims

Distributed algorithms are the foundation on which system services are built. The aim of the module is to cover core algorithms by concentrating on three key attributes that are very significant in building responsive applications: processing and communication delays and component failures.

Outline Of Syllabus

Preliminaries: Synchronous and Asynchronous communication models, precedence relations, non- deterministic computations and execution configurations, basics of tree structures, and basics of cryptography.
Fundamental Algorithms: Wave and Election Algorithms for trees, rings, and arbitrary topological structures. Example applications on Routing Algorithms and e-auction sites.
Algorithms in e-Commerce: Fair Exchange Algorithms. On-line and Off-line algorithms. Contract Exchange Applications.
Algorithms for Distributed Data Management: Database Commit Protocols: 2-phase and 3-phase protocols. The requirements and the limitations of commit protocols.

Teaching Methods

Teaching Activities
Category Activity Number Length Student Hours Comment
Structured Guided LearningLecture materials101:0010:00Recorded Lectures (10 lectures)
Scheduled Learning And Teaching ActivitiesLecture101:0010:00In-person lectures (10)
Guided Independent StudyAssessment preparation and completion161:0016:00Problem solving exercise
Scheduled Learning And Teaching ActivitiesSmall group teaching41:004:00PiP for guidance on Summative Coursework
Structured Guided LearningStructured non-synchronous discussion100:305:00Support for coursework and deep learning
Guided Independent StudyIndependent study551:0055:00Background reading
Total100:00
Teaching Rationale And Relationship

Lecture materials will introduce the learning material and demonstrate the key concepts by examples. Students are expected to follow-up within a few days by re-reading and annotating lecture notes to engage in deep learning. They will also be helped in this process through Structured non-synchronous discussions.

This is a very fundamental subject and it is therefore important that the learning materials are supported by plenty of examples and, if possible, by the animation software that interactively explains the workings of the algorithms. Students are expected to spend time on working out examples in their independent study hours and, in case of difficulties, raise questions during the structured non-synchronous discussion sessions which are generously fixed to be 10 in total.

Students aiming for 1st class marks are expected to widen their knowledge beyond the content of lecture notes through background reading.

Assessment Methods

The format of resits will be determined by the Board of Examiners

Exams
Description Length Semester When Set Percentage Comment
Written Examination901A60Closed book exam
Other Assessment
Description Semester When Set Percentage Comment
Prob solv exercises1M40Problem Solving Exercises: (set end of 6th lecture)
Assessment Rationale And Relationship

The assessment assess the knowledge of techniques and theory presented in lectures and also application skills in the context of a more realistic and open-ended problems.

If a closed book examination is not feasible to hold, there will be a Problem Solving Exercise 2 that will be set and released before the 8th In-person Lecture.

N.B. This module has both “Exam Assessment” and “Other Assessment” (e.g. coursework). If the total mark for either assessment falls below 40%, the maximum mark returned for the module will normally be 40%.

Reading Lists

Timetable