Cloud and edge computing have been widely adopted in many application scenarios. With the increasing demand of fast iteration and complexity of business logic, it is challenging to achieve rapid development and continuous delivery in such highly distributed cloud and edge computing environment. At present, microservice-based architecture has been the dominant deployment style, and a microservice system has to evolve agilely to offer stable Quality of Service (QoS) in the situation where user requirement changes frequently. Many research have been conducted to optimally re-deploy microservices to adapt to changing requirements. Nevertheless, complex dependencies between microservices and the existence of multiple instances of one single microservice in a microservice system have not been fully considered in existing works. This paper defines SPPMS, the Service Placement Problem in Microservice Systems that feature complex dependencies and multiple instances, as a Fractional Polynomial Problem (FPP) . Considering the high computation complexity of FPP, it is then transformed into a Quadratic Sum-of-Ratios Fractional Problem (QSRFP) which is further solved by the proposed greedy-based algorithms. Experiments demonstrate that our models and algorithms outperform existing approaches in both quality and computation speed.
翻译:在许多应用情景中,云层和边缘计算被广泛采用。随着快速迭代和复杂商业逻辑的需求日益增长,在这种高度分布的云层和边缘计算环境中实现快速发展和持续提供具有挑战性。目前,以微观服务为基础的结构一直是主要部署风格,微观服务系统必须灵活发展,以便在用户需求经常变化的情况下提供稳定的服务质量(QOS),许多研究已经进行,以最佳的方式重新配置微观服务,以适应不断变化的要求。然而,微服务与微服务系统中存在多种单一微观服务的情况之间存在着复杂的依赖性,但现有工作尚未充分考虑到这一点。这份文件界定了SPPMS、微观服务系统中服务安置问题,这些系统具有复杂的依赖性和多重情况,即多功能问题。考虑到FPPP的计算复杂性很高,随后将它转变为一个夸大性苏姆-拉蒂奥斯·弗里杰尔问题(QSRFP),而这种问题又由基于贪婪的算法和速度计算方法进一步解决。实验显示,我们现有的模型和算法都表明,我们现有的模型和算法是建立在基于贪婪和速度计算方法中进一步解决的。