AI 中文总结
本文提出LOBIN方案，将机器学习推理卸载到可编程交换机实现网络内市场预测，混合部署可降延迟、保性能，部分流量与交易价值由交换机处理。
AI 中文摘要
机器学习正在深刻变革算法交易，但仍需满足快速执行速度的要求。尽管这两个方面都旨在提高盈利能力，但将高级机器学习技术嵌入以降低交易延迟是一个显著挑战。采用网络内机器学习，即将推理任务卸载到可编程网络设备，为这种权衡提供了微妙的平衡。本文提出了LOBIN，一种利用网络内机器学习基于高频市场数据馈进行市场预测的解决方案。LOBIN擅长构建限价订单簿，并直接在可编程交换机中执行推理。与基于服务器的基准相比，LOBIN不仅能以更高的吞吐量预测未来股票价格走势，还能保持稳健的机器学习性能。它比纳斯达克订单匹配服务器基准实现了超过10%的延迟降低，并达到微秒级延迟。此外，通过采用集成交换机和服务器的混合部署方法，LOBIN的机器学习性能可进一步提升。我们的评估表明，在所有被评估股票的数据馈中，混合部署的应用使得约45%的流量和38%的潜在总交易价值在无服务器干预的情况下由交换机处理，在降低延迟的同时，预测错误率的平均变化相对于仅基于服务器的基准保持在约3%。
英文摘要
Machine learning is significantly transforming algorithmic trading, yet the requirement for rapid execution speeds persists. While both aspects aim to boost profitability, embedding advanced machine-learning techniques with reduced trading latency presents a notable challenge. Adopting in-network machine learning, which involves offloading inference to programmable network devices, offers a delicate equilibrium in this trade-off. In this paper, we present LOBIN, a solution that utilizes machine learning within the network for market prediction based on high-frequency market data feeds. LOBIN is adept at constructing limit order books and performing inference directly within programmable switches. When compared to server-based benchmarks, LOBIN not only predicts future stock price movements with higher throughput but also maintains robust machine learning performance. It achieves over a 10% reduction in latency compared to the NASDAQ order-matching server benchmark and delivers microsecond-level latency. Furthermore, the machine learning performance of LOBIN can be further enhanced through the adoption of a hybrid deployment approach that integrates both the switch and the servers. Our evaluation demonstrates that among all data feeds of evaluated stocks, the application of hybrid deployment results in approximately 45% of the traffic and 38\% of the total potential transaction value being processed within switches without server intervention, reducing latency while ensuring that the average change in error rate of predictions remains at around 3% relative to benchmarks based solely on server use.
