Integrating Kubernetes Autoscaling for Cost Efficiency in Cloud Services
DOI:
https://doi.org/10.32628/CSEIT241051038Keywords:
Kubernetes, Autoscaling, Cloud Services, Cost Optimization, Resource Allocation, Horizontal Pod Autoscaler, Vertical Pod Autoscaler, Cluster Autoscaler, Machine Learning, Predictive ScalingAbstract
Kubernetes Autoscaling Mechanism for Integration into Cloud Services to Achieve Cost Efficiency Organizations have turned towards containerized applications and microservices architecture. Optimizing and using resources appropriately as per the expected operational cost becomes the need of the hour. There are several autoscaling mechanisms within Kubernetes, that include Horizontal Pod Autoscaler, Vertical Pod Autoscaler, and Cluster Autoscaler, working towards cost optimization. We study predictive scaling algorithms, multi-dimensional autoscaling strategies, and machine learning-based approaches for resource allocation. Among the new challenges of implementing the solution are the methodologies followed in evaluating the research, which also involves complex advanced optimization techniques: from integrating serverless, towards multicloud autoscaling. Our findings will give an understanding of the status quo of Kubernetes autoscaling towards cost efficiency and recommendations for future research and industrial implementation.
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