Autoscaling for Cost Efficiency in Cloud Services

Authors

  • Akash Trivedi

Keywords:

Kubernetes, Autoscaling, Cloud Services, Cost Optimization, Resource Allocation, Horizontal Pod Autoscaler, Vertical Pod Autoscaler, Cluster Autoscaler, Machine Learning, Predictive Scaling

Abstract

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. 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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Published

2024-07-22

How to Cite

Akash Trivedi. (2024). Autoscaling for Cost Efficiency in Cloud Services. International Journal of Research Radicals in Multidisciplinary Fields, ISSN: 2960-043X, 3(2), 143–155. Retrieved from https://www.researchradicals.com/index.php/rr/article/view/114