Computer Laboratory

Course pages 2015–16

Data Centric Systems and Networking

Principal lecturer: Dr Eiko Yoneki
Taken by: MPhil ACS, Part III
Code: R212
Hours: 16 (Eight 2-hour seminar sessions (combination of lectures and reading club))
Class limit: 17 students
Prerequisites: Undergraduate network architectures and operating systems courses


This module provides an introduction to data centric systems and networking, where data is a token in programming flow and networking and its impact on the computer system's architecture. Large-scale distributed applications with big data processing will grow ever more in importance and become a pervasive aspect of the lives of millions of users. Supporting the design and implementation of robust, secure, and heterogeneous large-scale distributed systems is essential.


This course provides various perspectives on data centric systems and networking, including content-based routing, data-flow programming, stream processing, and large-scale graph data processing, thus providing a solid basis to work on the next generation of distributed systems and communication paradigms.

The module consists of 8 sessions, with 5 sessions on specific aspects of data-centric systems and networking research. Each session discusses 2-3 papers, led by the assigned students. One session is a hands-on tutorial on MapReduce using data flow programming with Amazon EC2. The 1st session advises on how to read/review a paper together with a brief introduction of different perspectives in data-centric systems. The last session is dedicated to the presentation of the open-source project studies presented by the students. One guest lecture is planned, covering inspiring current research on stream processing systems.

  1. Introduction to data centric systems and networking 
  2. Programming in data centric environment
  3. Large-scale graph data processing: Storage, processing model and parallel processing
  4. MapReduce hands-on tutorial using data-flow programming with Amazon EC2   
  5. Scheduling irregular tasks: Optimisation in parallel computing environments
  6. Stream data processing and data/query model 
  7. Data centric aspects in networking (Content centric mdoel in Internet/data center)  
  8. Presentation of Open Source Project Study


On completion of this module, students should:

  • Understand key concepts of data centric approaches in future networking and systems.
  • Obtain a clear understanding of building distributed systems using data centric programming and large-scale data processing.


Reading Club:

  • The reading club will involve 1-3 papers every week. At each session, around 2-3 papers are selected under the given topic, and the students present their review work.
  • Hands-on tutorial session of MapReduce parallel computing using data flow programming with Amazon EC2, including writing an application of processing streaming in Twitter data.


The following three reports are required, which could be extended from the assignment of the reading club or a different one within the scope of data centric systems and networking.

  1. Review report on a full length of paper (max 1800 words)
    • Describe the contribution of the paper in depth with criticisms
    • Crystallise the significant novelty in contrast to other related work
    • Suggestions for future work
  2. Survey report on sub-topic in data centric networking (max 2000 words)
    • Pick up to 5 papers as core papers in the survey scope
    • Read the above and expand reading through related work
    • Comprehend the view and finish an own survey paper
  3. Project study and exploration of a prototype (max 2500 words)
    • What is the significance of the project in the research domain?
    • Compare with similar and succeeding projects
    • Demonstrate the project by exploring its prototype

The reports 1 and 2 should be handed in by the end of 5th week and 7th week of the course (not in any particular order). The report 3 should be handed in by the end of the Michaelmas Term.


The final grade for the course will be provided as a percentage, and the assessment will consist of two parts:

  1. 20%: for reading club (participation, presentation)
  2. 80%: for the three reports:
    • 20%: Intensive review report
    • 25%: Survey report
    • 35%: Project study

Recommended reading

[1] Malewicz, G., Austern, M., Bik, A., Dehnert, J., Horn, I., Leiser, N. & G. Czajkowski: Pregel: A System for Large-Scale Graph Processing, SIGMOD, 2010.
[2] Jacobson, V., Smetters, D.K., Thornton, J.D., Plass, M.F., Briggs, N.H., & R.L. Braynard: Networking named content, CoNEXT, 2009.
[3] Bhatotia, P., Wieder, A., Rodrigues, R., Acar, A., Pasquini A: Incoop: MapReduce for incremental computation, ACM SOCC, 2011.
[4] Hong, S., Chafi, H., Sedlar, E., Olukotun, K.: Green-Marl: A DSL for Easy and Efficient Graph Analysis, ASPLOS, 2012.
[5] E. Zeitler and T.Risch: Massive scale-out of expensive continuous queries, VLDB, 2011.
[6] A. Kyrola and G. Blelloch: Graphchi: Large-scale graph computation on just a PC, OSDI, 2012. 
[7] D. Murray, F. McSherry, R. Isaacs, M. Isard, P. Barham, M. Abadi: Naiad: A Timely Dataflow System, SOSP, 2013. 
[8] J. E. Gonzalez, Y. Low, H. Gu, D. Bickson, and C. Guestrin: Powergraph: distributed graph-parallel computation on natural
graphs. OSDI, 2012.

A complete list can be found on the course material web page. See also 2014-2015 course material web page: