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Wen-Li Zhang, Ke Liu, Yi-Fan Shen, Ya-Zhu Lan, Hui Song, Ming-Yu Chen, Yuan-Fei Chen. Labeled Network Stack: A High-Concurrency and Low-Tail Latency Cloud Server Framework for Massive IoT Devices[J]. Journal of Computer Science and Technology, 2020, 35(1): 179-193. DOI: 10.1007/s11390-020-9651-x
Citation: Wen-Li Zhang, Ke Liu, Yi-Fan Shen, Ya-Zhu Lan, Hui Song, Ming-Yu Chen, Yuan-Fei Chen. Labeled Network Stack: A High-Concurrency and Low-Tail Latency Cloud Server Framework for Massive IoT Devices[J]. Journal of Computer Science and Technology, 2020, 35(1): 179-193. DOI: 10.1007/s11390-020-9651-x

Labeled Network Stack: A High-Concurrency and Low-Tail Latency Cloud Server Framework for Massive IoT Devices

  • 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.
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