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

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 n...

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Bibliographic Details
Published in:IEEE access 2022, Vol.10, p.18966-18977
Main Authors: Phuc, Le Hoang, Phan, Linh-An, Kim, Taehong
Format: Article
Language:English
Subjects:
Cloud computing
Communications traffic
Containers
Delays
Edge computing
horizontal pod autoscaler
Infrastructure
Internet of Things
IoT
Kubernetes
network-aware resource provisioning
Nodes
Response time
Telecommunication traffic
Time factors
Traffic information
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Summary: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.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2022.3150867