一个雾计算仿真框架

Moysis Symeonides, Z. Georgiou, Demetris Trihinas, G. Pallis, M. Dikaiakos
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引用次数: 36

摘要

雾计算正在成为一种主流模式,它弥合了传感设备和延迟敏感服务之间的计算和连接差距。然而,试验和评估物联网服务是一项艰巨的任务,涉及手动配置和部署具有不同资源和网络需求的地理分布物理和虚拟基础设施的混合。这将导致次优的、昂贵的、容易出错的部署,这是由于在设计阶段没有最初设想到的大量意外开销,以及与最终环境不同的令人印象深刻的测试条件。在本文中,我们介绍了Fogify,一个仿真器,简化了雾和边缘测试平台的建模,部署和大规模实验。Fogify提供了一个工具集:(i)模拟由异构资源、网络能力和QoS标准组成的复杂雾拓扑;(ii)使用流行的容器化描述将建模的配置和服务部署到云或本地环境;(iii)通过在运行时注入故障和调整配置来测试不同的“假设”场景,从而在向公众介绍服务之前揭示服务的局限性,从而对部署进行实验、测量和评估。在评估中,引入了具有真实工作负载的概念验证物联网服务,以展示通过Fogify快速原型的广泛适用性和优势。
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Fogify: A Fog Computing Emulation Framework
Fog Computing is emerging as the dominating paradigm bridging the compute and connectivity gap between sensing devices and latency-sensitive services. However, experimenting and evaluating IoT services is a daunting task involving the manual configuration and deployment of a mixture of geodistributed physical and virtual infrastructure with different resource and network requirements. This results in sub-optimal, costly and error-prone deployments due to numerous unexpected overheads not initially envisioned in the design phase and underwhelming testing conditions not resembling the end environment. In this paper, we introduce Fogify, an emulator easing the modeling, deployment and large-scale experimentation of fog and edge testbeds. Fogify provides a toolset to: (i) model complex fog topologies comprised of heterogeneous resources, network capabilities and QoS criteria; (ii) deploy the modelled configuration and services using popular containerized descriptions to a cloud or local environment; (iii) experiment, measure and evaluate the deployment by injecting faults and adapting the configuration at runtime to test different “what-if” scenarios that reveal the limitations of a service before introduced to the public. In the evaluation, proof-of-concept IoT services with real-world workloads are introduced to show the wide applicability and benefits of rapid prototyping via Fogify.
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