AI-Driven Resource Allocation Framework for Microservices in Hybrid Cloud Platforms
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arXiv
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| author | Barua, Biman Kaiser, M. Shamim |
| author_facet | Barua, Biman Kaiser, M. Shamim |
| contents | The increasing demand for scalable, efficient resource management in hybrid cloud environments has led to the exploration of AI-driven approaches for dynamic resource allocation. This paper presents an AI-driven framework for resource allocation among microservices in hybrid cloud platforms. The framework employs reinforcement learning (RL)-based resource utilization optimization to reduce costs and improve performance. The framework integrates AI models with cloud management tools to respond to challenges of dynamic scaling and cost-efficient low-latency service delivery. The reinforcement learning model continuously adjusts provisioned resources as required by the microservices and predicts the future consumption trends to minimize both under- and over-provisioning of resources. Preliminary simulation results indicate that using AI in the provision of resources related to costs can reduce expenditure by up to 30-40% compared to manual provisioning and threshold-based auto-scaling approaches. It is also estimated that the efficiency in resource utilization is expected to improve by 20%-30% with a corresponding latency cut of 15%-20% during the peak demand periods. This study compares the AI-driven approach with existing static and rule-based resource allocation methods, demonstrating the capability of this new model to outperform them in terms of flexibility and real-time interests. The results indicate that reinforcement learning can make optimization of hybrid cloud platforms even better, offering a 25-35% improvement in cost efficiency and the power of scaling for microservice-based applications. The proposed framework is a strong and scalable solution to managing cloud resources in dynamic and performance-critical environments. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_02610 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | AI-Driven Resource Allocation Framework for Microservices in Hybrid Cloud Platforms Barua, Biman Kaiser, M. Shamim Artificial Intelligence Computational Engineering, Finance, and Science Performance Software Engineering Systems and Control The increasing demand for scalable, efficient resource management in hybrid cloud environments has led to the exploration of AI-driven approaches for dynamic resource allocation. This paper presents an AI-driven framework for resource allocation among microservices in hybrid cloud platforms. The framework employs reinforcement learning (RL)-based resource utilization optimization to reduce costs and improve performance. The framework integrates AI models with cloud management tools to respond to challenges of dynamic scaling and cost-efficient low-latency service delivery. The reinforcement learning model continuously adjusts provisioned resources as required by the microservices and predicts the future consumption trends to minimize both under- and over-provisioning of resources. Preliminary simulation results indicate that using AI in the provision of resources related to costs can reduce expenditure by up to 30-40% compared to manual provisioning and threshold-based auto-scaling approaches. It is also estimated that the efficiency in resource utilization is expected to improve by 20%-30% with a corresponding latency cut of 15%-20% during the peak demand periods. This study compares the AI-driven approach with existing static and rule-based resource allocation methods, demonstrating the capability of this new model to outperform them in terms of flexibility and real-time interests. The results indicate that reinforcement learning can make optimization of hybrid cloud platforms even better, offering a 25-35% improvement in cost efficiency and the power of scaling for microservice-based applications. The proposed framework is a strong and scalable solution to managing cloud resources in dynamic and performance-critical environments. |
| title | AI-Driven Resource Allocation Framework for Microservices in Hybrid Cloud Platforms |
| topic | Artificial Intelligence Computational Engineering, Finance, and Science Performance Software Engineering Systems and Control |
| url | https://arxiv.org/abs/2412.02610 |