为糖尿病预测应用构建可扩展后端，支持万级并发且延迟低于1秒。
Scalable Back-End for an AI-Based Diabetes Prediction Application
- 通过水平扩容、数据库分片和消息队列实现系统可扩展性。
- 83%功能达标，支持1万并发用户，平均延迟低于1000毫秒。
- 用RabbitMQ异步处理预测请求，降低错误率至5%以下。
糖尿病全球发病率上升，早期检测至关重要。AI驱动的预测应用虽具前景，但需高效可扩展的后端支撑大规模用户。本文设计并评估了一个面向移动端糖尿病预测应用的可扩展后端系统，目标为故障率低于5%，平均延迟低于1000毫秒。系统采用水平扩展、数据库分片及基于消息队列的异步通信。性能测试显示，24项功能中有20项（83%）满足目标要求。用户资料管理、活动追踪及读密集型预测操作均达预期性能。系统在10,000并发用户下运行稳定，验证了其可扩展性。使用RabbitMQ的异步通信有效降低计算密集型预测请求的错误率，防止高负载下数据丢失，保障系统可靠性。
原文摘要 · Abstract (English)
The rising global prevalence of diabetes necessitates early detection to prevent severe complications. While AI-powered prediction applications offer a promising solution, they require a responsive and scalable back-end architecture to serve a large user base effectively. This paper details the development and evaluation of a scalable back-end system designed for a mobile diabetes prediction application. The primary objective was to maintain a failure rate below 5% and an average latency of under 1000 ms. The architecture leverages horizontal scaling, database sharding, and asynchronous communication via a message queue. Performance evaluation showed that 83% of the system's features (20 out of 24) met the specified performance targets. Key functionalities such as user profile management, activity tracking, and read-intensive prediction operations successfully achieved the desired performance. The system demonstrated the ability to handle up to 10,000 concurrent users without issues, validating its scalability. The implementation of asynchronous communication using RabbitMQ proved crucial in minimizing the error rate for computationally intensive prediction requests, ensuring system reliability by queuing requests and preventing data loss under heavy load.
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