arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2107.01427cs.NI
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多目标拥塞控制

Multi-Objective Congestion Control

Yiqing Ma, Han Tian, Xudong Liao, Junxue Zhang, Weiyan Wang, Kai Chen, Xin Jin

摘要:

几十年来,对Internet拥塞控制(CC)的研究已经产生了大量针对不同性能目标进行优化的算法。应用程序面临的挑战是根据其需求选择最合适的算法,当出现新的需求时,需要付出巨大的努力和专业知识来定制CC算法。本文探讨了一个基本问题:我们能否设计一种CC算法来满足不同的目标?我们提出MOCC,第一个多目标拥塞控制算法,试图解决这个挑战。MOCC的核心是一个新的多目标强化学习框架,可以自动学习不同应用需求之间的相关性和相应的最优控制策略。在此框架下,MOCC进一步应用迁移学习将过去经验中的知识转移到新的应用中,即使是不可预见的,也能快速适应新的目标。我们提供了用户空间和内核空间的MOCC实现。真实世界的实验和广泛的模拟表明,MOCC很好地支持多目标,在各个目标上与现有的最佳CC算法竞争或优于现有的最佳CC算法,并快速适应新的应用(例如,比以前的工作快14.2倍),而不影响旧的。

英文摘要:

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. In this paper, 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 challenge. The core of MOCC is a novel multi-objective reinforcement learning framework for CC that can 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 experiments and extensive simulations show that MOCC well supports multi-objective, competing or outperforming the best existing CC algorithms on individual objectives, and quickly adapting to new applications (e.g., 14.2x faster than prior work) without compromising old ones.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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