THESIS
2021
1 online resource (ix, 49 pages) : illustrations (some color)
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 fu...[
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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 (14.2✕ faster than prior work) without compromising old ones.
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