No abstract

In the past decade, Internet of Things (IoT) technology has been widely used in various applications in people's daily life. Currently, IoT applications mainly depend on the powerful cloud datacenters as the computing and storage centers. However, with such cloud-centric frameworks, a large amount of data will be transferred between the end devices and the remote cloud datacenters via a long wide-area network, which may potentially result in intolerable latency and a lot of energy consumption. To alleviate this problem, the edge computing (EC) paradigm is exploited to sink the cloud computing capability from the network core to network edges in proximity to end devices, so as to enable computationintensive and latency-critical edge intelligence applications to be executed in a real-time manner. With the increasing amount of edge devices, it is essential to obtain the status of devices in real-time for realizing the overall resources of heterogeneous edge devices. Thus, it is important to construct a system which can be used to monitor each device's status and obtain the performance of each device. In this study, a cluster-based heterogeneous edge computing environment is implemented for resource monitoring and performance evaluation. In the experiment, three deep learning models of object detection are used to evaluate the performance of the implemented system. Through the experimental results, we can easily realize the resource usage, including the high range dynamics of electricity and power consumption of heterogeneous edge devices via the visualization results.INDEX TERMS Edge computing, resource monitoring, Kubernetes, Prometheus, Grafana.