"大数据+云计算+微服务"在福建省精准扶贫中的应用

如何解决全国精准扶贫和精准脱贫问题,确保扶贫对象精准、项目安排精准、资金使用精准,成为中央亟需解决的重要问题.福建省财政厅利用大数据、云计算、微服务等前沿先进技术在全国率先研究开发福建省扶贫（惠民）资金在线监管系统,实现对扶贫（惠民）资金的全流程精准监控,确保资金精准发放.本文对该系统的体系架构、关键技术应用、实现效果及建设经验等进行了详述.该系统建设经验可为国家建设其他领域运用大数据、云计算、微服务等技术提供参考和借鉴.     

基于微服务的分布式数据安全整合应用系统

针对分布式数据安全整合应用中数据整合操作繁琐,实时性不足,数据应用共享困难的问题,设计并实现了基于微服务的分布式数据安全整合应用系统,包含有两个子系统：数据整合子系统与数据安全应用子系统。系统基于Spring Cloud微服务架构设计,服务模块功能单一,边界明确,服务之间相互解耦,便于便捷开发,快速部署,可扩展性强,拥有强大的负载均衡策略与容错机制。数据整合子系统中提出了一种轻量级的数据整合方案,可以进行可视化的数据便捷整合,具有较好的灵活性与实时性;数据安全应用子系统中通过数据分级与用户角色划分,实现了用户对数据的安全访问控制,同时,采用证书链模型,使用数据证书来认证用户身份,有效地解决了用户角色的管理问题与网络信任问题。     

Microservice transition and its granularity problem: A systematic mapping study

Microservices have gained wide recognition and acceptance in software industries as an emerging architectural style for autonomic, scalable, and more reliable computing. The transition to microservices has been highly motivated by the need for better alignment of technical design decisions with improving value potentials of architectures. Despite microservices' popularity, research still lacks disciplined understanding of transition and consensus on the principles and activities underlying that transition. In this paper, we report on a systematic mapping study that consolidates various views, approaches and activities that commonly assist in the transition to microservices. The study aims to provide a better understanding of the transition; it also contributes a working definition of the transition and technical activities underlying it. We term the transition and technical activities leading to microservice architectures as microservitization. We then shed light on a fundamental problem of microservitization: microservice granularity and reasoning about its adaptation as first-class entities. This study reviews state-of-the-art and -practice related to reasoning about microservice granularity; it reviews modeling approaches, aspects considered, guidelines and processes used to reason about microservice granularity. This study identifies opportunities for future research and development related to reasoning about microservice granularity.     

油田作业现场云端智能监控系统

随着数字化油田建设步伐的不断加快,云端智能视频监控在油田作业现场的应用成为一个热点问题.油田作业现场现有的视频监控系统,架构上存在客户端部署困难、系统拓展性差等缺陷,智能检测上主要依靠视频监控被动监视的方式来应对现场存在的安全隐患,对于监控日志数据不能有效的存档管理以及进行综合分析.本文首先针对油田作业现场实际需求,考...     

基于动态采样策略的微服务链路追踪方法

针对现有开源分布式链路追踪系统或框架的采样策略存在收集过多无助于故障分析等任务的正常执行的跟踪的问题,本文提出一种动态采样策略,通过采样策略树和执行轨迹图两种数据结构解决了跟踪采样率自动调整问题和如何快速准确地找到需要调整跟踪采样率的服务的问题,并通过两者的协作提高异常执行的跟踪占比.实验结果表明,本文方法在任一时间段内均有效提高了异常执行的跟踪占比.     

高并发高可用的分布式电商平台架构研究

为了克服软件架构在巨大QPS冲击下性能低、可拓展性差等弊端,解决在经常高并发场景下的程序崩溃、服务宕机以及数据出错等问题,文章针对亟需平稳运行的电商业务,设计并实现了一套基于分布式架构的高可用新型电子商务系统.该系统提供了高可靠的分布式框架,含有服务节点自动注册与发现、配置统一管理以及服务集群容错等功能.在可能出错的服...     

基于相似度匹配的微服务故障诊断方法

随着互联网服务的快速发展,分布式的微服务应用逐渐取代传统的单体应用成为互联网应用的主要形式之一.微服务应用在具有可伸缩性、容错性、高可用性等优点的同时,也存在着构建繁琐、部署复杂和维护困难等挑战.面向云计算环境的微服务监测与运维是当前的研究热点,但仍然存在粒度较粗、故障定位不准确等缺点.针对以上问题,本文提出了一种基于...     

微服务架构下安全监测智能平台的开发及部署

随着信息化新技术发展,水利工程在数字化、智慧化方面的需求日益增强,安全监测智能平台功能日渐增多,迭代优化也逐渐加快。为了提高安全监测平台开发、维护、迭代更新的效率,本文将分布式微服务应用架构应用于安全监测智能平台中,通过业务拆分、服务封装等构建一个微服务基础架构下的大坝安全监测智能平台,各服务之间互相协调、互相配合,能够独立地部署到生产环境、类生产环境中,最终实现系统的高可拓展性,快捷部署性等,并成功应用于阳江大河水库、广州黄龙带水库,取得良好的效果。     

SmartHerd management: A microservices-based fog computing–assisted IoT platform towards data-driven smart dairy farming

Internet of Things (IoT), fog computing, cloud computing, and data-driven techniques together offer a great opportunity for verticals such as dairy industry to increase productivity by getting actionable insights to improve farming practices, thereby increasing efficiency and yield. In this paper, we present SmartHerd, a fog computing–assisted end-to-end IoT platform for animal behavior analysis and health monitoring in a dairy farming scenario. The platform follows a microservices-oriented design to assist the distributed computing paradigm and addresses the major issue of constrained Internet connectivity in remote farm locations. We present the implementation of the designed software system in a 6-month mature real-world deployment, wherein the data from wearables on cows is sent to a fog-based platform for data classification and analysis, which includes decision-making capabilities and provides actionable insights to farmer towards the welfare of animals. With fog-based computational assistance in the SmartHerd setup, we see an 84% reduction in amount of data transferred to the cloud as compared with the conventional cloud-based approach.