[网络领域顶会追踪系列#26][NSDI'22]近似遥测请求的动态调度

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Dynamic Scheduling of Approximate Telemetry Queries

背景

网络遥测系统提供了重要的网络状态可视性。尽管大家在利用可编程交换机硬件在高和随时间变化的流量负载上扩展遥测系统方面，已经做出了很多的努力，在动态性的事实（如在流量的组成和查询的数量和类型）面前，高效地利用限制的硬件资源却鲜有人关注。这些动态性都对资源需求和查询的准确性有着影响。

挑战

在本文中，作者认为这个动态性问题促使将遥测系统重塑（reframing）为资源调度器---与最先进的方案最大的不同。

更具体的，遥测系统应该自行实时决定在数据面上执行主动查询集合的时间和时长，而不是统计方式来在软硬件上平台划分查询。

工作

为了达成这个目的，作者提出了一个高效的近似和调度算法，在查询执行方面，暴露了准确性和时延的权衡，以减少硬件资源用量。

实验

作者通过构建DynATOS，一个通过围绕ASIC编程的重配置方法建立的硬件原型系统，评估了其算法。

在单交换机上，作者表明他们的方法可以满足查询准确性和目标要求的情况下，在对待流量动态性时更加具有鲁棒性，并且可以执行由多个复杂的并发和顺序查询组成的动态工作负载。

原文摘要

Network telemetry systems provide critical visibility into the state of networks. While significant progress has been made by leveraging programmable switch hardware to scale these systems to high and time-varying traffic workloads, less attention has been paid towards efficiently utilizing limited hardware resources in the face of dynamics such as the composition of traffic as well as the number and types of queries running at a given point in time. Both these dynamics have implications on resource requirements and query accuracy.

In this paper, we argue that this dynamics problem motivates reframing telemetry systems as resource schedulers---a significant departure from state-of-the-art. More concretely, rather than statically partition queries across hardware and software platforms, telemetry systems ought to decide on their own and at runtime when and for how long to execute the set of active queries on the data plane. To this end, we propose an efficient approximation and scheduling algorithm that exposes accuracy and latency tradeoffs with respect to query execution to reduce hardware resource usage. We evaluate our algorithm by building DynATOS, a hardware prototype built around a reconfigurable approach to ASIC programming. We show that our approach is more robust than state-of-the-art methods to traffic dynamics and can execute dynamic workloads comprised of multiple concurrent and sequential queries of varied complexities on a single switch while meeting per-query accuracy and latency goals.