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Liao Xiaojian, Yang Zhe, Yang Hongzhang, Tu Yaofeng, Shu Jiwu. A Low-Latency Storage Engine with Low CPU Overhead[J]. Journal of Computer Research and Development, 2022, 59(3): 489-498. DOI: 10.7544/issn1000-1239.20210574
Citation: Liao Xiaojian, Yang Zhe, Yang Hongzhang, Tu Yaofeng, Shu Jiwu. A Low-Latency Storage Engine with Low CPU Overhead[J]. Journal of Computer Research and Development, 2022, 59(3): 489-498. DOI: 10.7544/issn1000-1239.20210574

A Low-Latency Storage Engine with Low CPU Overhead

Funds: This work was supported by the National Key Research and Development Program of China (2018YFB1003301), the National Natural Science Foundation of China (61832011), and the Project of ZTE (20182002008).
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  • Published Date: February 28, 2022
  • The latency of solid-state drive (SSD) has improved dramatically in recent years. For example, an ultra-low latency SSD can process 4 KB data in 10 microseconds. With this low latency, how to reap the I/O completion efficiently becomes an important issue in modern storage systems. Traditional storage systems reap I/O completion through hardware interrupt, which introduces extra context switches overhead and further prolongs the overall I/O latency. Existing work use polling as an alternative to the hardware interrupt, thereby eliminating the context switches, but at the cost of high CPU consumption. This paper proposes a CPU-efficient and low-latency storage engine namely NIO, to take full advantage of the ultra-low latency SSDs. The key idea of NIO is to separate the I/O paths of short I/Os from that of long I/Os; NIO uses classic hardware interrupt for long I/Os, as polling long I/Os does not bring significant improvement but incurs huge CPU overhead; for short I/Os, NIO introduces lazy polling, which lets the I/O thread sleep for a variable time interval before continuously polling, thereby achieving low latency with low CPU consumption. NIO further introduces transaction-aware I/O reaping mechanism to reduce the transaction latency, and a dynamic adjustment mechanism to cope with the dynamic changes of the workload and internal activities of the device. Under dynamic workloads, NIO shows comparable performance against polling-based storage engine while reducing the CPU consumption by at least 59%.
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