Root-Of-Trust for Continuous Integration and Continuous Deployment Pipeline in Cloud Computing

Cloud computing has gained significant use over the last decade due to its several benefits, including cost savings associated with setup, deployments, delivery, physical resource sharing across virtual machines, and availability of on-demand cloud services. However, in addition to usual threats in almost every computing environment, cloud computing has also introduced a set of new threats as consumers share physical resources due to the physical co-location paradigm. Furthermore, since there are a growing number of attacks directed at cloud environments (including dictionary attacks, replay code attacks, denial of service attacks, rootkit attacks, code injection attacks, etc.), customers require additional assurances before adopting cloud services. Moreover, the continuous integration and continuous deployment of the code fragments have made cloud services more prone to security breaches. In this study, the model based on the root of trust for continuous integration and continuous deployment is proposed, instead of only relying on a single sign-on authentication method that typically uses only id and password. The underlying study opted hardware security module by utilizing the Trusted Platform Module (TPM), which is commonly available as a cryptoprocessor on the motherboards of the personal computers and data center servers. The preliminary proof of concept demonstrated that the TPM features can be utilized through RESTful services to establish the root of trust for continuous integration and continuous deployment pipeline and can additionally be integrated as a secure microservice feature in the cloud computing environment.     

基于微服务架构B/S系统的性能分析

传统的单体应用架构系统,随着用户需求和系统功能的变动,出现了单体应用功能模块边界模糊、部署效率低、扩展困难、技术更迭代价高等缺点,尤其是单个模块修改部署效率低的问题.因此,微服务技术得到关注和应用,微服务架构的业务边界确定服务边界,具有高内聚性,易于开发与维护、局部修改部署、技术选择不受限等优势.本文研究微服务应用系统的架构优势,设计了一B/S应用系统进行测试分析.实验设计测试指标为线程响应时间、吞吐量以及部署时间的实验方案,并使用Jmeter性能测试工具进行测试,分析了20个和50个并发用户的测试数据.实验结果表明微服务在响应时间、吞吐量等指标有明显的效率和性能优势.     

基于负载预测的微服务混合自动扩展

由于边缘云没有比中心云更强大的计算处理能力,在应对动态负载时很容易导致无意义的扩展抖动或资源处理能力不足的问题,所以在一个真实的边缘云环境中对微服务应用程序使用两个合成和两个实际工作负载进行实验评估,并提出了一种基于负载预测的混合自动扩展方法（predictively horizontal and vertical pod autoscaling,Pre-HVPA）。该方法首先采用机器学习对负载数据特征进行预测,并获得最终负载预测结果。然后利用预测负载进行水平和垂直的混合自动扩展。仿真结果表明,基于该方法所进行自动扩展可以减少扩展抖动和容器使用数量,所以适用于边缘云环境中的微服务应用。     

面向5G网络云原生应用资源调度的博弈优化策略

随着5G网络和云原生技术的发展,面向服务的5G云原生核心网应运而生,传统应用正朝着云原生化方向发展。目前云原生服务提供商和云原生应用商数量众多且关系复杂,使得应用在云原生化过程中的资源调度面临新挑战。提出一种5G网络云原生应用资源调度优化策略,将云原生应用商和云原生服务提供商构建为多主多从的Stackelberg博弈模型,对传统收益进行具体描述并联合能耗构建利润函数和策略空间,证明给定一组微服务资源定价的情况下存在云原生应用商的纳什均衡点。在此基础上,引入柯西分布对策略进行优化,提高其收敛性能,通过分布式迭代方法得到云原生服务提供商的最佳微服务定价和云原生应用商的最佳微服务租用比例。仿真结果表明,相比ACA算法、QOS PA算法以及GOS策略,该策略能够有效提高网络收益和用户体验质量,同时降低应用开发能耗。     

柔性微服务监控框架

微服务细化了服务的粒度,针对微服务的监控是微服务管理需要面对的核心问题,以可扩展全链路应用服务监控为目标,在已有服务管理层之上设计实现了一种柔性微服务监控框架,用于监控微服务状态和不断变化的服务负载,与已有的分布式监控架构相比,这种监控框架灵活度更高,对服务变化的感知能力更强,使用Raft算法增强了数据一致性,避免了单点故障的情况,经试验分析,代价更小,在实际项目中验证了框架及方法的有效性.     

A microservice‐based architecture for (customisable) analyses of Docker images

We introduce Docker Analyser , a microservice‐based tool that permits building customised analysers of Docker images. The architecture of Docker Analyser is designed to crawl Docker images from a remote Docker registry, to analyse each image by running an analysis function, and to store the results into a local database. Users can build their own image analysers by instantiating Docker Analyser with a custom analysis function and by configuring the architecture. More precisely, the steps needed to obtain new analysers are (1) replacing the analysis function used to analyse crawled Docker images, (2) setting the policy for crawling Docker images, and (3) setting the scalability options for obtaining a scalable architecture. In this paper, we also present 2 different use cases, ie, 2 different analysers of Docker images created by instantiating Docker Analyser with 2 different analysis functions and configuration options. The 2 use cases show that Docker Analyser decreases the effort required to obtain new analysers versus building them from scratch.     

基于Docker容器的快速开发网页服务器

C/S模式是早期的开发网页服务器架构,它使用复杂、成本高并且缺少通用性.B/S模式通过将系统功能集中到服务器中弥补了使用复杂成本高的缺点,但是没有对用户端的环境和数据传输率进行深入测试和研究,使得过多的外部用户访问网页,给服务器带来压力.针对传统C/S模式和B/S模式的局限性,引入Docker容器化开发思路,分别融合N...     

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.     

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.     

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,考虑了现有监控架构的不足之处,给出了云端智能监控系统的整体架构设计,运用智能化视频检测手段开展作业现场危险因素检测和分析,最后,对系统产生的危险记录等数据进行管理分析,以达到为现场安全管理提供决策支持的目的.目前,系统已经部署到油田作业现场中,并成功通过了功能以及性能测试,对于提升监控系统的有效性和实时性,提升紧急事件的处理能力,保障油田现场的作业安全生产具有重要意义.