Scalable Fog and Cloud Computing for Industrial IoT

适用于工业物联网的可扩展雾和云计算

• 批准号: 539276-2019
• 负责人: Krishnamurthy, Diwakar
• 金额: $ 1.82万
• 依托单位: University of Calgary
• 依托单位国家: 加拿大
• 项目类别: Engage Grants Program
• 财政年份: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=681123
• 关键词: Scalable; Fog; Cloud; Computing; Industrial

Networked sensor rich systems, i.e., "Industrial Internet of Things (IIoT)" systems, are being increasinglydeployed to support a diverse range of applications such as oil and gas asset monitoring, energy metering, farming, connected vehicles, and supply chain management. Performance and energy usage are key concerns in such systems. Specifically, providing timely responses is critical for many applications, e.g., intrusion and fault detection. Typically, sensors have limited battery life and do not have powerful computation capabilities to meet stringent performance requirements. This necessitates smart strategies tooffload computation between fog resources, i.e., compute resources residing close to the sensors, and more centralized cloud resources. Despite the rapid advancements in this area, there is a lack of tools and techniques that system operators can use to evaluate such strategies before deploying a sensor rich system. Consequently, operators can be saddled with expensive deployments that do not meet performance, scalability, and energy usage requirements. This project will address this gap by initiating the development of a framework called PROFESS that supports performance evaluation of sensor rich systems. PROFESS will exploit recent advances in container virtualization technology to develop a realistic, cost-effective and controlled experimentation environment where large scale systems can be emulated on commodity multi-core servers. Specifically, PROFESS will seamlessly use containers to mimic sensor devices as well as fog and cloud resources with various computation and communication characteristics connected through operator-specified network topologies. It will provide an environment where various automated control strategies to coordinate computation between fog and cloud resources can be developed and evaluated by an operator in a controlled manner prior to a real deployment. Optimized deployments facilitated by PROFESS can allow Canadian IoT operators to reap cost savings due to efficient sizing and scalability of their infrastructure as well as the reduction of financial penalties arising out of poor performance.

网络化的传感器丰富的系统，即，“工业物联网（IIoT）”系统正在越来越多地部署，以支持各种应用，如石油和天然气资产监控、能源计量、农业、联网车辆和供应链管理。性能和能源使用是这种系统的关键问题。具体而言，提供及时的响应对于许多应用来说是至关重要的，例如，入侵和故障检测。通常，传感器的电池寿命有限，并且不具备强大的计算能力来满足严格的性能要求。这需要智能策略来卸载雾资源之间的计算，即，靠近传感器的计算资源，以及更集中的云资源。尽管在这一领域的快速进步，有一个缺乏的工具和技术，系统操作员可以使用部署传感器丰富的系统之前，评估这样的策略。因此，运营商可能会承担不符合性能、可扩展性和能源使用要求的昂贵部署。该项目将通过启动一个名为PROFESS的框架的开发来解决这一差距，该框架支持传感器丰富系统的性能评估。PROFESS将利用容器虚拟化技术的最新进展，开发一个现实的、具有成本效益的和可控的实验环境，在这个环境中，可以在商用多核服务器上模拟大规模系统。具体来说，PROFESS将无缝地使用容器来模拟传感器设备以及雾和云资源，这些资源具有通过运营商指定的网络拓扑连接的各种计算和通信特性。它将提供一种环境，在这种环境中，操作员可以在真实的部署之前以受控的方式开发和评估各种自动控制策略，以协调雾和云资源之间的计算。PROFESS促进的优化部署可以使加拿大物联网运营商通过其基础设施的有效规模和可扩展性以及减少因性能差而产生的财务处罚来节省成本。