Traffic-Aware Horizontal Pod Autoscaler in Kubernetes-Based Edge Computing Infrastructure

Le Hoang Phuc,Linh-An Phan,Taehong Kim

doi:10.1109/access.2022.3150867

Le Hoang Phuc, Linh-An Phan + Show 1 more

Open Access

https://doi.org/10.1109/access.2022.3150867

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Journal: IEEE Access	Publication Date: Jan 1, 2022
Citations: 24	License type: CC BY 4.0

Affiliation: Chungbuk National University

Abstract

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.

Highlights

W ITH the massive boom in Internet of Things (IoT) applications in daily life, such as in industry, healthcare, logistics, and military [1], [2], many IoT devices collect data from the environment and generate huge amounts of data
Edge computing infrastructure has emerged to address the challenge of handling a massive number of IoT devices in many IoT applications requiring low response time
We showed that Kubernetes’ HPA (KHPA) is not suitable for an edge computing environment where edge nodes are geographically dispersed and the amounts of traffic accessing nodes are imbalanced because new pods are scheduled to nodes regardless of traffic distribution

Summary

INTRODUCTION

W ITH the massive boom in Internet of Things (IoT) applications in daily life, such as in industry, healthcare, logistics, and military [1], [2], many IoT devices collect data from the environment and generate huge amounts of data. When the resource demands of the applications change, Kubernetes’ HPA (KHPA) only tries to evenly distribute new pods to nodes or terminate redundant pods on nodes based on pod status without considering the network delay between edge nodes and the volume of network traffic accessing them in real time This limitation of KHPA can result in the degradation of the quality of service and overall throughput of the system [17]. To solve the aforementioned problem of KHPA in a Kubernetes-based edge computing infrastructure, this paper proposes traffic-aware HPA (THPA), which operates on top of Kubernetes to provide dynamic resource autoscaling by considering the IoT service demand at each edge node.

RELATED WORKS

TRAFFIC-AWARE HORIZONTAL POD AUTOSCALER

PERFORMANCE EVALUATION

Findings

CONCLUSION