As part of a broader organisational restructure, data networking research at Swinburne University of Technology has moved from the Centre for Advanced Internet Architecture (CAIA) to the Internet For Things (I4T) Research Lab.

Although CAIA no longer exists, this website reflects CAIA's activities and outputs between March 2002 and February 2017, and is being maintained as a service to the broader data networking research community.

MCC: Microburst Congestion Control

Overview

Microburst triggered congestion affects environments where unsynchronised data sources send data via a common path, which converges on the wire as a highly synchronised and bursty traffic spike. Core networking equipment is generally provisioned to adequately handle full line rate traffic from the network's edges and can therefore cope with such bursts. As the burst moves closer to the destination, the potential for queue build up and end host processing overhead to cause momentary congestion increases.

Bursts directed at the same end point are the most problematic, and this scenario is commonly referred to as the incast problem. Typically, the switch port closest to the end host becomes inundated with packets, its buffer overflows, and the tail of the burst is dropped at the switch. The faster the data rate and lower the latency, the more packets that can potentially be lost.

Clustered data centre environments with low latency, high bandwidth connectivity are the obvious places where microbursts and incast can be observed. Other scenarios found commonly on the wider Internet also have the potential to experience such problems and require examination as well.

Project Goals

  • Analyse anonymised network trace information obtained from Google's production networks, looking for existence and details related to the nature of congestion experienced.
  • Run evaluative and exploratory testing of congestion management schemes based on real world scenarios/conditions identified by the Google production network trace analysis.
  • Provide new insights and perhaps make recommendations to both Google and the Internet community at large based on the findings and outcomes of the project.

Program Members

Collaborators

  • Greg Chesson (Google)

This research draws on anonymised data provided by Google Inc., to promote a greater common understanding of the web.

This project has concluded, and in 2013/2014 the research topic is being pursued here.

Last Updated: Sunday 5-Jan-2014 06:46:10 EST | Maintained by: Lawrence Stewart (lastewart@swin.edu.au) | Authorised by: Grenville Armitage (garmitage@swin.edu.au)