论文标题
在数据中心网络中限制了网络内计算，并且较低的拥塞
Constrained In-network Computing with Low Congestion in Datacenter Networks
论文作者
论文摘要
当今分布式计算已成为一种常见的实践，最近将重点放在具有网络内计算功能的智能网络设备上。具有近线速率计算和聚合功能的最先进的开关可以加速和改进各种现代应用程序的性能，例如大数据分析以及大规模分布式和联合机器学习。 在本文中，我们制定和研究了这种方法的理论算法基础，并专注于如何在数据中心内部署和使用受约束的网络内计算能力。我们将注意力集中在减少网络拥堵的原因上，即网络中最拥挤的链接，同时支持给定的工作量。我们提出了类似树状网络拓扑的有效的最佳算法，并表明我们的解决方案对常见替代方法的改进提供了同样多的改进。特别是，我们的结果表明，仅具有支持网络内聚合的一小部分网络设备可以大大减少网络拥塞，无论是单个工作负载还是多个工作负载。
Distributed computing has become a common practice nowadays, where the recent focus has been given to the usage of smart networking devices with in-network computing capabilities. State-of-the-art switches with near-line rate computing and aggregation capabilities enable acceleration and improved performance for various modern applications like big data analytics and large-scale distributed and federated machine learning. In this paper, we formulate and study the theoretical algorithmic foundations of such approaches, and focus on how to deploy and use constrained in-network computing capabilities within the data center. We focus our attention on reducing the network congestion, i.e., the most congested link in the network, while supporting the given workload(s). We present an efficient optimal algorithm for tree-like network topologies and show that our solution provides as much as an x13 improvement over common alternative approaches. In particular, our results show that having merely a small fraction of network devices that support in-network aggregation can significantly reduce the network congestion, both for single and multiple workloads.
