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Research on Elastic Service Scaling Methods for Generative AI Applications Under Cloud-native Architectures
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Abstract
This study focuses on the operational characteristics of generative AI applications under cloud-native architectures, analyzing their computational intensity and fluctuating load patterns, and exploring the adaptability and necessity of elastic service scaling. By reviewing the theoretical foundations, the research further investigates key techniques such as auto-scaling and resource scheduling, constructing an elastic scaling framework suited for cloud-native environments. Combined with load-aware dynamic scaling methods and incorporating containerization, microservices, and service mesh mechanisms, feasible strategic pathways are proposed. Experiments conducted in a representative environment evaluate and compare different scaling schemes, showing that the proposed strategies enhance resource utilization and system stability. The findings indicate that integrating cloud-native architectures with generative AI applications offers significant advantages and practical value in elastic service scaling.
Keywords
Cloud-native architecture; Generative AI; Elastic services; Auto-scaling
References
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How to cite this paper
Research on Elastic Service Scaling Methods for Generative AI Applications Under Cloud-native Architectures
How to cite this paper: Hongyan Qu. (2025) Research on Elastic Service Scaling Methods for Generative AI Applications Under Cloud-native Architectures. Advances in Computer and Communication, 6(4), 224-229.
DOI: http://dx.doi.org/10.26855/acc.2025.10.012
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