用AI动态调度微服务资源，降本增效，峰值时延迟降20%。
AI-Driven Resource Allocation Framework for Microservices in Hybrid Cloud Platforms
- 基于强化学习实时调整资源，预测需求避免浪费。
- 相比人工和规则方法，成本降低30%-40%，资源利用率提升20%-30%。
- 适合需要弹性扩容与低延迟的云原生应用开发者参考。
随着混合云环境中对可扩展、高效资源管理的需求增长，本文提出一种面向微服务的AI驱动资源分配框架。该框架采用强化学习（RL）实现资源利用优化，以降低成本并提升性能。通过将AI模型与云管理工具集成，能够应对动态扩缩容及低成本低延迟服务交付的挑战。强化学习模型可根据微服务需求持续调整资源配置，并预测未来资源消耗趋势，从而最小化资源不足或过度配置。初步仿真结果显示，相较于手动配置和基于阈值的自动伸缩方案，使用AI进行资源调配可使支出减少30%-40%；资源利用率预计提升20%-30%，在高峰负载期间延迟降低15%-20%。研究对比了静态与规则基方法，表明该AI方法在灵活性与实时响应方面更具优势。结果表明，强化学习能显著提升混合云平台的优化能力，带来25%-35%的成本效率改善与更强的微服务扩展能力。所提框架是动态、高性能关键场景下管理云资源的有力且可扩展的解决方案。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。