To keep up with demand, servers will scale up to handle hundreds of thousands of clients simultaneously. Much of the focus of the community has been on scaling servers in terms of aggregate traffic intensity (packets transmitted per second). However, bottlenecks caused by the increasing number of concurrent clients, resulting in a large number of concurrent flows, have received little attention. In this work, we focus on identifying such bottlenecks. In particular, we define two broad categories of problems; namely, admitting more packets into the network stack than can be handled efficiently, and increasing per-packet overhead within the stack. We show that these problems contribute to high CPU usage and network performance degradation in terms of aggregate throughput and RTT. Our measurement and analysis are performed in the context of the Linux networking stack, the the most widely used publicly available networking stack. Further, we discuss the relevance of our findings to other network stacks. The goal of our work is to highlight considerations required in the design of future networking stacks to enable efficient handling of large numbers of clients and flows.

中文翻译：

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侦查通往百万客户端服务器的路径

为了满足需求，服务器将扩大规模以同时处理数十万个客户端。社区的大部分关注点是根据总流量强度（每秒传输的数据包）扩展服务器。但是，由于并发客户端数量增加而导致的瓶颈（导致大量并发流）很少受到关注。在这项工作中，我们着重于确定此类瓶颈。特别是，我们定义了两大类问题：即，允许更多的数据包进入网络堆栈，而不是有效处理的数据包，并增加堆栈中每个数据包的开销。我们表明，这些问题导致总体吞吐量和RTT方面的CPU使用率高和网络性能下降。我们的测量和分析是在Linux网络堆栈（最广泛使用的公共可用网络堆栈）的上下文中进行的。此外，我们讨论了我们的发现与其他网络堆栈的相关性。我们工作的目标是突出设计未来的网络堆栈时需要考虑的因素，以实现对大量客户端和流量的有效处理。