Implementation of a Cluster-Based Heterogeneous Edge Computing System for Resource Monitoring and Performance Evaluation

In the past decade, Internet of Things (IoT) technology has been widely used in various applications in daily life. Currently, IoT applications primarily depend on powerful cloud data centers as computing and storage centers. However, with such cloud-centric frameworks, numerous data are transferred between end devices and remote cloud data centers via a long wide-area network, which will result in intolerable latency and a lot of energy consumption. The edge computing paradigm is exploited to sink the cloud computing capability from the network core to network edges in proximity to end devices to enable computation-intensive and latency-critical edge intelligence applications to be executed in a real-time manner to alleviate this problem. With the increasing number of edge devices, it is essential to obtain the status of devices in real time to realize the overall resources of heterogeneous edge devices. Thus, constructing a system that can monitor each device's status and performance is important. This study implements a cluster-based heterogeneous edge computing system by integrating the Docker, Kubernetes, Prometheus, Grafana and Node Exporter technologies for resource monitoring and performance evaluation. In the experiment, three deep learning models for object detection evaluate the performance of the implemented system. Through the constructed resource monitoring platform, the resource usage status of various edge devices can be monitored easily. In addition, the overall system performance can also be evaluated effectively.