Multi-Objective Congestion Control

MPhil Thesis Defence


Title: "Multi-Objective Congestion Control"

By

Miss Yiqing MA


Abstract

Decades of research on Internet congestion control (CC) has produced a 
plethora of algorithms that optimize for different performance objectives. 
Applications face the challenge of choosing the most suitable algorithm 
based on their needs, and it takes tremendous efforts and expertise to 
customize CC algorithms when new demands emerge.

So we explore a basic question: can we design a single CC algorithm to 
satisfy different objectives? We propose MOCC, the first multi-objective 
congestion control algorithm that attempts to address this question. The 
core of MOCC is a novel multi-objective reinforcement learning framework 
for CC to automatically learn the correlations between different 
application requirements and the corresponding optimal control policies. 
Under this framework, MOCC further applies transfer learning to transfer 
the knowledge from past experience to new applications, quickly adapting 
itself to a new objective even if it is unforeseen. We provide both 
user-space and kernel-space implementation of MOCC. Real-world Internet 
experiments and extensive simulations show that MOCC well supports 
multi-objective, competing or outperforming the best existing CC 
algorithms on each individual objectives, and quickly adapting to new 
application objectives in 288 seconds ($14.2X faster than prior work) 
without compromising old ones.


Date:  			Monday, 1 November 2021

Time:			10:30am - 12:30pm

Zoom meeting:
https://hkust.zoom.us/j/99583054509?pwd=bW9ZMk0zamk1ZTdFSExlTUZBQ29vdz09

Committee Members:	Dr. Kai Chen (Supervisor)
 			Prof. Gary Chan (Chairperson)
 			Dr. Wei Wang


**** ALL are Welcome ****