Kubernetes, an open-source container orchestration platform, enables high availability and scalability through diverse autoscaling mechanisms such as Horizontal Pod Autoscaler (HPA), Vertical Pod Autoscaler and Cluster Autoscaler. Amongst them, HPA helps provide seamless service by dynamically scaling up and down the number of resource units, called pods, without having to restart the whole system. Kubernetes monitors default Resource Metrics including CPU and memory usage of host machines and their pods. On the other hand, Custom Metrics, provided by external software such as Prometheus, are customizable to monitor a wide collection of metrics. In this paper, we investigate HPA through diverse experiments to provide critical knowledge on its operational behaviors. We also discuss the essential difference between Kubernetes Resource Metrics (KRM) and Prometheus Custom Metrics (PCM) and how they affect HPA’s performance. Lastly, we provide deeper insights and lessons on how to optimize the performance of HPA for researchers, developers, and system administrators working with Kubernetes in the future.

The recent increase in the number of Internet of Things (IoT) devices has led to the generation of a large amount of data. These data are generally processed by cloud servers because of their high scalability and ability to provide resources on demand. However, processing large amounts of data in the cloud is an impractical solution for the strict requirements of IoT services, such as low latency and high bandwidth. Fog computing, which brings computational resources closer to the IoT devices, has emerged as a suitable solution to mitigate these problems. Resource provisioning and application orchestration are two of the key challenges when running IoT applications in a Fog computing environment. In this paper, we present ElasticFog, which runs on top of the Kubernetes platform and enables real-time elastic resource provisioning for containerized applications in Fog computing. Specifically, ElasticFog collects network traffic information in real time and allocates computational resources proportionally to the distribution of network traffic. The experimental results prove that ElasticFog achieves a significant improvement in terms of throughput and network latency compared with the default mechanism in Kubernetes.

KubeEdge (KE) is a container orchestration platform for deploying and managing containerized IoT applications in an edge computing environment based on Kubernetes. It is intended to be hosted at the edge and provides seamless cloud-edge coordination as well as an offline mode that allows the edge to function independently of the cloud. However, there are unreliable communication links between edge nodes in edge computing environments, implying that load balancing in an edge computing environment is not guaranteed while using KE. Furthermore, KE lacks Horizontal Pod Autoscaling (HPA), implying that KE cannot dynamically deploy new resources to efficiently handle increasing requests. Both of the aforementioned issues have a significant impact on the performance of the KE-based edge computing system, particularly when traffic volumes vary over time and geographical location. In this study, a node-based horizontal pod autoscaler (NHPA) is proposed to provide dynamical adjustment for the number of pods of individual nodes independently from each other in an edge computing environment where the traffic volume fluctuates over time and location, and the communication links between edge nodes are not stable. The proposed NHPA can dynamically adjust the number of pods depending on the incoming traffic at each node, which will improve the overall performance of the KubeEdge-based edge computing environment. In the KubeEdge-based edge computing environment, the experimental findings reveal that NHPA outperforms KE in terms of throughput and response time by a factor of about 3 and 25, respectively.

With the exponential growth of the Internet of Things (IoT), edge computing is in the limelight for its ability to quickly and efficiently process numerous data generated by IoT devices. EdgeX Foundry is a representative open-source-based IoT gateway platform, providing various IoT protocol services and interoperability between them. However, due to the absence of container orchestration technology, such as automated deployment and dynamic resource management for application services, EdgeX Foundry has fundamental limitations of a potential edge computing platform. In this paper, we propose EdgeX over Kubernetes, which enables remote service deployment and autoscaling to application services by running EdgeX Foundry over Kubernetes, which is a product-grade container orchestration tool. Experimental evaluation results prove that the proposed platform increases manageability through the remote deployment of application services and improves the throughput of the system and service quality with real-time monitoring and autoscaling.

In order to establish a smart farm, many kinds of equipment are built and operated inside and outside of a pig house. Thus, the environment for livestock (limited to pigs in this paper) in the barn is properly maintained for its growth conditions. However, due to poor environments such as closed pig houses, lack of stable power supply, inexperienced livestock management, and power outages, the failure of these environment equipment is high. Thus, there are difficulties in detecting its malfunctions during equipment operation. In this paper, based on deep learning, we provide a mechanism to quickly detect anomalies of multiple equipment (environmental sensors and controllers, etc.) in each pig house at the same time. In particular, environmental factors (temperature, humidity, CO₂, ventilation, radiator temperature, external temperature, etc.) to be used for learning were extracted through the analysis of data accumulated for the generation of predictive models of each equipment. In addition, the optimal recurrent neural network (RNN) environment was derived by analyzing the characteristics of the learning RNN. In this way, the accuracy of the prediction model can be improved. In this paper, the real-time input data (only in the case of temperature) was intentionally induced above the threshold, and 93% of the abnormalities were detected to determine whether the equipment was abnormal.