Edge and Fog computing paradigms overcome the limitations of cloud-centric execution for different latency-sensitive Internet of Things (IoT) applications by offering computing resources closer to the data sources. Small single-board computers (SBCs) like Raspberry Pis (RPis) are widely used as computing nodes in both paradigms. These devices are usually equipped with moderate speed processors and provide support for peripheral interfacing and networking, making them well-suited to deal with IoT-driven operations such as data sensing, analysis, and actuation. However, these small Edge devices are constrained in facilitating multi-tenancy and resource sharing. The management of computing and peripheral resources through centralized entities further degrades their performance and service quality significantly. To address these issues, a fully distributed framework, named Con-Pi, is proposed in this work to manage resources at the Edge or Fog environments. Con-Pi exploits the concept of containerization and harnesses Docker containers to run IoT applications as micro-services. %Moreover, Con-Pi operates in a distributed manner across multiple RPis and enables them to share resources. The software system of the proposed framework also provides a scope to integrate different IoT applications, resource and energy management policies for Edge and Fog computing. Its performance is compared with the state-of-the-art frameworks through real-world experiments. The experimental results show that Con-Pi outperforms others in enhancing response time and managing energy usage and computing resources through its distributed offloading model. Further, we have developed an automated pest bird deterrent system using Con-Pi to demonstrate its suitability in developing practical solutions for various IoT-enabled use cases, including smart agriculture.
翻译:边远和 Fog 计算模式克服了对不同悬浮敏感物(IoT)互联网应用进行以云为中心的执行的局限性。 这些模式通过提供更接近数据源的计算机资源,克服了对不同悬浮敏感物(IoT)互联网应用进行以云为中心的执行的局限性。 小型单机计算机(SBC),如Raspberry Pis(RPis),在两种模式中被广泛用作计算节点。 这些设备通常配备中速处理器,为外围的互换和网络提供支持,使它们非常适合处理由IoT驱动的操作,如数据遥感、分析和操作操作等。 然而,这些小型的Edge设备在便利多强度和资源共享方面受到限制。 通过集中实体对计算和外围资源进行管理,进一步降低其性能和服务质量。 为了解决这些问题,在这项工作中建议一个完全分布的框架,名为Con-Pi,用来管理Edge 或Fog 环境的资源。 Con-Pi 利用集装箱模型概念概念和Docke 软件应用程序运行智能软件。%reover, Con-Pi 运行一个分布一个分布方式管理系统,用来在多个REFRPislot 系统, 并让它们能应用数据库数据库中, 展示一个不同的资源应用。