Computer Laboratory

Data Centric Systems and Networking (2016-2017 Lent Term)

DCSN - R212












This module provides an introduction to data centric systems, where data is a token in programming flow, and its impact on the computer system's architecture. Large-scale distributed applications with big data processing will grow ever more in importance. Supporting the design and implementation of robust, secure, and heterogeneous large-scale distributed systems is essential. To deal with distributed systems with a large parameter space, tuning and optimising computer systems is becoming an important and complex task. Integrating machine learning approaches for system optimisation will also be explored in this course. This course provides various perspectives on data centric systems, including data-flow programming, stream processing, large-scale graph data processing and computer system's optimisation especially use of machine learning approaches, thus providing a solid basis to work on the next generation of distributed systems.

 On completion of this module, the students should:

  • Understand key concepts of data centric approaches in future systems.
  • Obtain a clear understanding of building distributed systems using data centric programming and large-scale data processing.
  • Use of Machine Learning in computer system's optimisation
Further detail of the course will be up on the web soon! This year, one of the new focuses of the course is set to the optimisation of computer systems using Machine Learning and as a case study we will run a tutorial of Google TensorFlow and optimise the Deep Neural Network operation over TensorFlow.

You can look a the previous year's course material at

Contact Email

Please email to for your submission of course work or any question.