@article{Zhang2020, 
author = {Wen-Li Zhang and Ke Liu and Yi-Fan Shen and Ya-Zhu Lan and Hui Song and Ming-Yu Chen and Yuan-Fei Chen},
title = {Labeled Network Stack: A High-Concurrency and Low-Tail Latency Cloud Server Framework for Massive IoT Devices},
year = {2020},
journal = {Journal of Computer Science and Technology},
volume = {35},
number = {1},
pages = {179-193},
keywords = {tail latency, high concurrency, network stack, cloud server, Internet of Things (IoT) service},
url = {https://www.sciopen.com/article/10.1007/s11390-020-9651-x},
doi = {10.1007/s11390-020-9651-x},
abstract = {Internet of Things (IoT) applications have massive client connections to cloud servers, and the number of networked IoT devices is remarkably increasing. IoT services require both low-tail latency and high concurrency in datacenters. This study aims to determine whether an order of magnitude improvement is possible in tail latency and concurrency in mainstream systems by proposing a hardware–software codesigned labeled network stack (LNS) for future datacenters. The key innovation is a cross-layered payload labeling mechanism that distinguishes different requests by payload across the full network stack, including application, TCP/IP, and Ethernet layers. This type of design enables prioritized data packet processing and forwarding along the full datapath, such that latency-insensitive requests cannot significantly interfere with high-priority requests. We build a prototype datacenter server to evaluate the LNS design against a commercial X86 server and the mTCP research, using a cloud-supported IoT application scenario. Experimental results show that the LNS design can provide an order of magnitude improvement in tail latency and concurrency. A single datacenter server node can support over 2 million concurrent long-living connections for IoT devices as a 99-percentile tail latency of 50 ms is maintained. In addition, the hardware–software codesign approach remarkably reduces the labeling and prioritization overhead and constrains the interference of high-priority requests to low-priority requests.}
}