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English(EN) Learning Adaptive SED for heterogeneous load balancing

新算法学习最优负载均衡，适用于未知服务速率

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一种在线学习算法，旨在优化具有异构且未知服务速率的系统中的负载均衡。该算法旨在通过精心平衡经验性最短预期延迟（SED）路由和强制探索阶段来采样所有服务器，从而使用SED策略路由客户。所提出的方法保证了有限的遗憾，这与典型的多臂老虎机设置不同，并且数值实验证实了其有效性，特别是在强制探索有益的情况下。 AI

影响 这项研究通过在事先不知道服务器能力的情况下实现自适应负载均衡，可以提高分布式系统的效率。

排序理由 该集群包含一篇详细介绍新负载均衡算法的学术论文。[lever_c_demoted from research: ic=1 ai=0.7]

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

新算法学习最优负载均衡，适用于未知服务速率

本文如何被排名

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Tool
该集群包含一篇详细介绍新负载均衡算法的学术论文。[lever_c_demoted from research: ic=1 ai=0.7]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Sanne van Kempen, Jaron Sanders, Fiona Sloothaak, Maarten G. Wolf ·

    异构负载均衡的自适应SED学习

    arXiv:2609.06881v1 Announce Type: new Abstract: We study a two-server load balancing system with heterogeneous service rates that are a priori unknown to the dispatcher. The goal is to route customers according to the Shortest--Expected--Delay (SED) policy, but this requires know…

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