SWP：无优先级的微秒级网络SLO
SWP: Microsecond Network SLOs Without Priorities
摘要：
云计算越来越多地用于延迟敏感的应用程序，重新激发了人们对提供网络尾部延迟的严格限制的兴趣。由于高度突发的应用需求、更快的链路速度和重尾消息大小的组合，在合理的网络利用率下实现这一目标已被证明是难以实现的。虽然优先级调度可以用于减少某些流量的尾部延迟，但这是以网络上所有其他流量的更差延迟行为为代价的。大多数运营商选择以非常低的平均利用率运行他们的网络，尽管增加了成本，但仍然遭受不良的尾部行为。本文采取了不同的方法。我们建立了一个系统swp，帮助运营商（和网络设计者）理解和控制尾部延迟，而不依赖于优先级调度。当网络工作负载发生变化时，swp被设计为实时提供网络交换机配置的建议，以保持每个流量类别的尾部延迟目标。swp的核心是一个有效的模型，用于模拟流量特性，端到端拥塞控制，和交换机调度的服务水平目标（SLO）的综合效果，以及一个优化器，调整分配给每个类的交换机级调度权重。通过对具有不同SLO的各种工作负载进行模拟，我们发现，为了满足与swp相同的SLO，FIFO将需要增加65%的链路容量，对于突发流量类具有严格SLO的场景，需要增加79%的链路容量。
英文摘要：
The increasing use of cloud computing for latency-sensitive applications has sparked renewed interest in providing tight bounds on network tail latency. Achieving this in practice at reasonable network utilization has proved elusive, due to a combination of highly bursty application demand, faster link speeds, and heavy-tailed message sizes. While priority scheduling can be used to reduce tail latency for some traffic, this comes at a cost of much worse delay behavior for all other traffic on the network. Most operators choose to run their networks at very low average utilization, despite the added cost, and yet still suffer poor tail behavior. This paper takes a different approach. We build a system, swp, to help operators (and network designers) to understand and control tail latency without relying on priority scheduling. As network workload changes, swp is designed to give real-time advice on the network switch configurations needed to maintain tail latency objectives for each traffic class. The core of swp is an efficient model for simulating the combined effect of traffic characteristics, end-to-end congestion control, and switch scheduling on service-level objectives (SLOs), along with an optimizer that adjusts switch-level scheduling weights assigned to each class. Using simulation across a diverse set of workloads with different SLOs, we show that to meet the same SLOs as swp provides, FIFO would require 65% greater link capacity, and 79% more for scenarios with tight SLOs on bursty traffic classes.
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。
