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.

ZigBee is the emerging industrial standard for ad hoc networks based on IEEE 802.15.4. Due to characteristics such as low data rate, low price, and low power consumption, ZigBee is expected to be used in wireless sensor networks for remote monitoring, home control, and industrial automation. Since one of the most important goals is to reduce the installation and running cost, ZigBee stack is embedded in small and cheap micro-controller units. Since tree routing does not require any routing tables to send the packet to the destination, it can be used in ZigBee end devices that have limited resources. However, tree routing has the problem that the packets follow the tree topology to the destination even if the destination is located nearby. We propose the shortcut tree routing protocol to reduce the routing cost of ZigBee tree routing by using the neighbor table that is originally defined in the ZigBee standard. While following the ZigBee tree routing algorithm, we suggest forwarding the packet to the neighbor node if it can reduce the routing cost to the destination. Simulation results show that the shortcut tree routing algorithm saves more than 30 percent of the hop count compared with ZigBee tree routing.

Container-based Internet of Things (IoT) applications in an edge computing environment require autoscaling to dynamically adapt to fluctuations in IoT device requests. Although Kubernetes’ horizontal pod autoscaler provides the resource autoscaling feature by monitoring the resource status of nodes and then making pod adjustments if necessary, it evenly allocates pods to worker nodes without considering the imbalance of resource demand between nodes in an edge computing environment. This paper proposes the traffic-aware horizontal pod autoscaler (THPA), which operates on top of Kubernetes to enable real-time traffic-aware resource autoscaling for IoT applications in an edge computing environment. THPA performs upscaling and downscaling actions based on network traffic information from nodes to improve the quality of IoT services in the edge computing infrastructure. Experimental results show that Kubernetes with THPA improves the average response time and throughput of IoT applications by approximately 150% compared to Kubernetes with the horizontal pod autoscaler. This indicates that it is important to provide proper resource scaling according to the network traffic distribution to maximize IoT applications performance in an edge computing environment.

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.