CloudNativeSim: a toolkit for modeling and simulation of cloud-native applications

Jingfeng Wu, Minxian Xu, Yiyuan He, Kejiang Ye, Chengzhong Xu
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Abstract

Cloud-native applications are increasingly becoming popular in modern software design. Employing a microservice-based architecture into these applications is a prevalent strategy that enhances system availability and flexibility. However, cloud-native applications also introduce new challenges, such as frequent inter-service communication and the complexity of managing heterogeneous codebases and hardware, resulting in unpredictable complexity and dynamism. Furthermore, as applications scale, only limited research teams or enterprises possess the resources for large-scale deployment and testing, which impedes progress in the cloud-native domain. To address these challenges, we propose CloudNativeSim, a simulator for cloud-native applications with a microservice-based architecture. CloudNativeSim offers several key benefits: (i) comprehensive and dynamic modeling for cloud-native applications, (ii) an extended simulation framework with new policy interfaces for scheduling cloud-native applications, and (iii) support for customized application scenarios and user feedback based on Quality of Service (QoS) metrics. CloudNativeSim can be easily deployed on standard computers to manage a high volume of requests and services. Its performance was validated through a case study, demonstrating higher than 94.5% accuracy in terms of response time. The study further highlights the feasibility of CloudNativeSim by illustrating the effects of various scaling policies.
CloudNativeSim:云原生应用程序建模和仿真工具包
云原生应用在现代软件设计中越来越受欢迎。在这些应用中采用基于微服务的架构是一种流行的策略,可以提高系统的可用性和灵活性。然而,云原生应用也带来了新的挑战,如频繁的服务间通信以及管理异构代码库和硬件的复杂性,从而导致不可预测的复杂性和动态性。此外,随着应用规模的扩大,只有有限的研究团队或企业拥有大规模部署和测试的资源,这阻碍了云原生领域的发展。为了应对这些挑战,我们提出了云原生模拟器(CloudNativeSim),这是一种基于微服务架构的云原生应用模拟器。CloudNativeSim具有以下几个主要优点:(i) 云原生应用的全面动态建模;(ii) 带有用于调度云原生应用的新策略接口的扩展模拟框架;(iii) 支持基于服务质量(QoS)指标的定制应用场景和用户反馈。云原生模拟可轻松部署在标准计算机上,管理大量请求和服务。其性能已通过案例研究得到验证,在响应时间方面的准确率高于 94.5%。该研究通过说明各种扩展策略的效果,进一步突出了云原生模拟的可行性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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