论文标题
大规模智能微服务
Large-Scale Intelligent Microservices
论文作者
论文摘要
由于现代ML算法的各种计算足迹和众多的数据库技术，每个数据库技术都具有自己的限制性语法，因此在数据库中部署机器学习（ML）算法是一个挑战。我们介绍了一个基于Apache Spark的微服务编排框架，该框架扩展了数据库操作以包括Web Service Pirinitives。我们的系统可以在数百台机器上精心策划Web服务，并充分利用群集，线程和异步并行性。使用此框架，我们为智能服务提供了大规模的客户，例如语音，愿景，搜索，异常检测和文本分析。这允许用户将现成的智能集成到Apache Spark Connector中的任何数据存储中。为了消除网络通信中的大多数开销，我们还引入了架构的低延迟容器化版本。最后，我们证明了我们研究的服务在各种基准上都具有竞争力，并介绍了该框架的两个应用程序来创建智能搜索引擎和实时自动竞赛分析系统。
Deploying Machine Learning (ML) algorithms within databases is a challenge due to the varied computational footprints of modern ML algorithms and the myriad of database technologies each with its own restrictive syntax. We introduce an Apache Spark-based micro-service orchestration framework that extends database operations to include web service primitives. Our system can orchestrate web services across hundreds of machines and takes full advantage of cluster, thread, and asynchronous parallelism. Using this framework, we provide large scale clients for intelligent services such as speech, vision, search, anomaly detection, and text analysis. This allows users to integrate ready-to-use intelligence into any datastore with an Apache Spark connector. To eliminate the majority of overhead from network communication, we also introduce a low-latency containerized version of our architecture. Finally, we demonstrate that the services we investigate are competitive on a variety of benchmarks, and present two applications of this framework to create intelligent search engines, and real-time auto race analytics systems.
