发表机构
Hasso-Plattner-Institute Internet Technologies and Softwarization（哈索·普拉特纳研究所互联网技术与软件化）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对可重构网络尾延迟控制问题，提出机会性承诺策略DART，在6种应力拓扑下其P99逗留时间较最强基线低达23%，适配异构等重构时间场景。
AI 中文摘要
许多系统通过在不同配置间切换来服务不同作业类，重构所需时间具有随机性，且取决于切换方向，在不同配置间的分布存在差异，到达目标配置可能需要经过中间配置。在高百分位延迟目标下，很少被服务或作为中转的配置会累积长等待作业，这些作业主导了所谓的逗留时间尾。控制该尾需要结合三类决策：选择哪个目标、走哪条路径、以及是否在途经的配置处服务多少作业。我们提出DART，这是一种机会性承诺策略，承诺选择目标配置以避免短视绕行，仅当中途配置的加权延迟或积压值得暂停时才服务该配置。我们展示了这些决策如何影响加权逗留时间尾。在六个应力拓扑（每个隔离不同的尾控制挑战）上，DART在所有情况下均实现最低的P99逗留时间，较最强基线低达23%。该优势在异构、重尾和非对称重构时间下依然成立。
英文摘要
Many systems serve different job classes by switching among configurations. Often, reconfiguration takes a stochastic amount of time that depends on direction and can differ in distribution between configurations. Reaching a target configuration may require crossing intermediate ones. Under a high-percentile delay objective, rarely served or pass-through configurations accumulate the long-waiting jobs that dominate the sojourn-time tail. Controlling that tail couples three decisions: which target to choose, which path to take, and whether and how many jobs to serve at the configurations crossed on the way. We introduce DART, an opportunistic commitment policy that commits to a target configuration to avoid myopic detours while serving a traversed configuration only when its weighted delay or backlog justifies the pause. We show how these decisions shape the weighted sojourn-time tail. Across six stress topologies, each isolating a different tail-control challenge, DART achieves the lowest P99 sojourn time in every case, up to 23% below the strongest baseline. The advantage holds under heterogeneous, heavy-tailed, and asymmetric reconfiguration times.
