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An Efficient Resource Monitoring Service for Fog Computing Environments

机译：用于雾计算环境的有效资源监控服务

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摘要

With the increasing number of Internet of Things (IoT) devices, the volume and variety of data being generated by these devices are increasing rapidly. Cloud computing cannot process this data due to its high latency and scalability. In order to process this data in less time, fog computing has evolved as an extension to Cloud computing. In a fog computing environment, a resource monitoring service plays a vital role in providing advanced services, such as scheduling, scaling and migration. Most of the research in fog computing has assumed that a resource monitoring service is already available. Conventional methods proposed for other distributed systems may not be suitable due to the unique features of a fog environment. To improve the overall performance of fog computing and to optimise resource usage, effective resource monitoring techniques are required. Hence, we propose a support and confidence based (SCB) technique which optimises the resource usage in the resource monitoring service. The performance of our proposed system is evaluated by examining a real-time traffic use case in a fog emulator with synthetic data. The experimental results obtained from the fog emulator show that the proposed technique consumes 19 percent lesser resources compared with the existing technique.
机译：随着越来越多的东西（物联网）设备，这些设备产生的数据的体积和各种迅速增加。由于其高延迟和可扩展性，云计算无法处理此数据。为了在更少的时间内处理此数据，雾计算已将其发展为云计算的扩展。在雾计算环境中，资源监控服务在提供高级服务方面发挥着至关重要的作用，例如调度，缩放和迁移。大多数雾计算研究假定资源监控服务已经可用。由于雾环境的独特特征，所提出的其他分布式系统提出的常规方法可能不适合。为了提高雾计算的整体性能并优化资源使用，需要有效的资源监控技术。因此，我们提出了一种基于支持和置信（SCB）技术，其优化了资源监控服务中的资源使用。通过在具有合成数据的雾模拟器中检查实时流量用例来评估我们提出的系统的性能。从雾仿函数获得的实​​验结果表明，与现有技术相比，该提出的技术消耗了19％的资源。

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