Distributed Algorithms (COMP90020)
Graduate courseworkPoints: 12.5On Campus (Parkville)
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The Internet, World Wide Web, bank networks, mobile phone networks and many others are examples for Distributed Systems. Distributed Systems rely on a key set of algorithms and data structures to run efficiently and effectively. In this subject, we learn these key algorithms that professionals work with while dealing with various systems. Clock synchronization, leader election, mutual exclusion, and replication are just a few areas were multiple well known algorithms were developed during the evolution of the Distributed Computing paradigm.
Topics covered include:
- Synchronous and asynchronous network algorithms that address resource allocation, communication
- Consensus among distributed processes
- Distributed data structures
- Data consistency
- Deadlock detection
- Lader election, and
- Global snapshots issues.
Intended learning outcomes
INTENDED LEARNING OUTCOMES (ILO)
On completion of this subject the student is expected to:
- Have developed an understanding of distributed algorithm design
- Be able to implement and analyse distributed algorithms.
On completion of this subject students should have the following skills:
- Ability to undertake problem identification, formulation and solution
- Capacity for independent critical thought, rational inquiry and self-directed learning
- Profound respect for truth and intellectual integrity, and for the ethics of scholarship.
Last updated: 3 November 2022