Performance Bottleneck Analysis of Kubernetes-based Microservices under Dynamic Traffic Patterns 


Vol. 51,  No. 3, pp. 673-685, Mar.  2026
10.7840/kics.2026.51.3.673


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  Abstract

In modern cloud-native environments, the widespread adoption of microservice architecture (MSA) has led to frequent performance bottlenecks caused by increasing inter-service call complexity and resource contention. This paper quantitatively analyzes the spatial and temporal characteristics of such bottlenecks under time-varying traffic conditions in a Kubernetes-based MSA environment. We deployed the DeathStarBench social network workload on a Kubernetes cluster and applied realistic traffic patterns to collect and analyze time-series data on resource usage and response latency. The results show that when computational load concentrates on specific pods, the results show that when computational load concentrates on specific pods, CPU saturation and queue buildup occur, which propagate performance degradation across the entire system. This study provides empirical insights into dynamic bottlenecks in microservice environments, offering a practical foundation for resource management and scheduling policy development.

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[IEEE Style]

S. Woo, K. Lee, D. Lee, K. Lee, "Performance Bottleneck Analysis of Kubernetes-based Microservices under Dynamic Traffic Patterns," The Journal of Korean Institute of Communications and Information Sciences, vol. 51, no. 3, pp. 673-685, 2026. DOI: 10.7840/kics.2026.51.3.673.

[ACM Style]

Sooyeon Woo, Kyoungbin Lee, Dongin Lee, and Kyungwoon Lee. 2026. Performance Bottleneck Analysis of Kubernetes-based Microservices under Dynamic Traffic Patterns. The Journal of Korean Institute of Communications and Information Sciences, 51, 3, (2026), 673-685. DOI: 10.7840/kics.2026.51.3.673.

[KICS Style]

Sooyeon Woo, Kyoungbin Lee, Dongin Lee, Kyungwoon Lee, "Performance Bottleneck Analysis of Kubernetes-based Microservices under Dynamic Traffic Patterns," The Journal of Korean Institute of Communications and Information Sciences, vol. 51, no. 3, pp. 673-685, 3. 2026. (https://doi.org/10.7840/kics.2026.51.3.673)
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pISSN : 1226 - 4717
eISSN : 2287 - 3880