模型驱动工程中分布式系统的动态自适应

Mufasir Muthaher Mohammed
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引用次数: 0

摘要

现代软件系统在复杂、不确定和高度动态的环境中运行。管理这样的系统是一项重大挑战;开发自我管理的自治系统是减少开发和维护工作的一种方法。在分布式系统的上下文中,由于多变的主机环境,通过动态适应实现这种自治尤其具有挑战性。模型驱动工程(MDE)是一种软件开发范例,它提倡使用模型作为主要工件,而不是源代码。MDE承诺通过抽象、自动化和分析以更低的成本获得更高质量的软件。我们工作的目标是利用MDE来促进具有动态适应功能的分布式应用程序的开发和维护。我们假设应用程序的结构和行为已经使用组件-连接器(C&C)范式和“共享知识的监控-分析-计划-执行”(MAPE-K)参考体系结构进行了建模。在最初的工作中,我们开发了一个模型级别的监视基础设施,并调整了现有的代码生成和部署支持,以从C&C模型生成分布式系统,并将其自动部署到合适的平台上。在未来的工作中,我们计划研究如何利用云原生容器化和编排平台(即Docker和Kubernetes)的监控和适应能力进行动态适应,以及如何将这种系统级的适应与任何模型级的监控、规划和适应能力有效地结合起来。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Dynamic adaptation for distributed systems in model-driven engineering
Modern-day software systems operate within complex, uncertain, and highly dynamic environments. Managing such systems is a significant challenge; developing self-managing autonomic systems is one way to reduce development and maintenance efforts. In the context of distributed systems, achieving this autonomy through dynamic adaptation is particularly challenging due to the volatile host environment. Model-Driven Engineering (MDE) is a software development paradigm that advocates the use of models as the primary artifacts rather than source code. MDE promises higher-quality software at a lower cost through abstractions, automation, and analyses. The goal of our work is to leverage MDE to facilitate the development and maintenance of distributed applications with dynamic adaptation capabilities. We assume that the structure and behavior of the application has been modeled using the Component-and-Connector (C&C) paradigm and the 'Monitor-Analyze-Plan-Execute with shared Knowledge' (MAPE-K) reference architecture. In the initial work, we have developed a model-level monitoring infrastructure, and adapted existing code generation and deployment support to generate a distributed system from the C&C models and deploy it automatically on a suitable platform. In future work, we plan to investigate how the monitoring and adaptation capabilities of cloud-native containerization and orchestration platforms (i.e., Docker and Kubernetes) can be leveraged for dynamic adaptation, and how this system-level adaptation can be combined effectively with any model-level monitoring, planning, and adaptation capabilities.
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