Estimating short connection capacity on high performance user level network stack

Short connections are generally used to transfer small-size messages, which contribute a large part of workload in modern applications. The maximum sustainable short connection rate, which is called short connection capacity, is an important index for admission control, Web QoS control, and energy saving. A capacity estimation mechanism aims to find the workload just saturating the server, and it relies on both workload information and system information. Past researches point out that kernel space network stack becomes the bottleneck when a huge number of concurrent short connections coexist. On the other hand, high performance user level network stacks have been proved to eliminate such bottleneck, thus become a hot research topic in both academia and industry. However, they also bring challenges for estimating short connection capacity, making traditional methods ineffective. Therefore, it is important to find a new method to estimate short connection capacity on high performance user level network stacks. In this paper, we prove that the effective CPU utilization is an adaptive index to different workload patterns and application complexities, which can reflect the server state. Then we design and implement an online capacity estimator on the Seastar platform. We conduct experiments to verify the effectiveness of our online capacity estimator. The results show that our estimator can actually estimate the capacity online. When the server is near saturated, the 90th percentile relative estimating error is no more than 9.18%. Furthermore, our capacity estimator only introduces no more than 1.38% of capacity loss in our experiments.