GSOC2019DCN

Project Overview

Project name: Framework of Studying Flow Completion Time Minimization for Data Center Networks

Abstract: This project aims to make NS-3 more friendly to researchers working on the contemporary research topic in Data Center Networks (DCN) to use NS-3 as an effective simulation tool to evaluate their ideas. The theme of the project is to augment NS-3 with further support of Data Center Network (DCN) related simulation and research, with a special focus in the flow-based performance optimization, e.g., implementing useful modules in NS-3 ecosystem including Multi Level Feedback Queue scheduling, spine-leaf topology helper, flow statistics analysis helper and so forth.

About Me: I will soon join the University of Pennsylvania as a first-year Ph.D. student focusing on Computer Systems and Networking. I obtained my master degree in Wireless Systems at KTH Royal Institute of Technology, Sweden and Bachelor of Engineering in Automatic Control at Zhejiang University, China.

To update.

The entire GSoC period is divided into 3 phases. The deliverable at the end of each phase is as mentioned below:

Implementation of Multi-Level Feedback Queue scheduling.
Implementation of Shorted Job First based scheduling.
Implementation of the tagging of the metadata of the (remaining) flow size in the packet with corresponding application or helpers.

Implementation of the spine-leaf DCN topology helper and NetAnim support.
Implementation of flow synthesizer and flow statistics analysis helper.
Provide examples of using representative traffic distribution in DCN for DCN simulation.

Provide example program for data center networking simulation in NS-3 reproducing the classical work in the domain, e.g., pFabric.
Buffer time for other prioritized targets for supporting DCN research in NS-3, e.g., the load balancing algorithms, TCP incast simulation in DCN.

To update.