Automated resource provisioning techniques enable the implementation of elastic services, by adapting the available resources to the service demand. This is essential for reducing power consumption and guaranteeing QoS and SLA fulfillment, especially for those services with strict QoS requirements in terms of latency or response time, such as web servers with high traffic load, data stream processing, or real-time big data analytics. Elasticity is often implemented in cloud platforms and virtualized data-centers by means of auto-scaling mechanisms. These make automated resource provisioning decisions based on the value of specific infrastructure and/or service performance metrics. This paper presents and evaluates a novel predictive auto-scaling mechanism based on machine learning techniques for time series forecasting and queuing theory. The new mechanism aims to accurately predict the processing load of a distributed server and estimate the appropriate number of resources that must be provisioned in order to optimize the service response time and fulfill the SLA contracted by the user, while attenuating resource over-provisioning in order to reduce energy consumption and infrastructure costs. The results show that the proposed model obtains a better forecasting accuracy than other classical models, and makes a resource allocation closer to the optimal case.

中文翻译：

---

基于机器学习技术的弹性云服务的高效资源配置

自动化的资源供应技术通过使可用资源适应服务需求来实现弹性服务。这对于降低功耗并确保QoS和SLA履行至关重要，特别是对于在延迟或响应时间方面具有严格QoS要求的服务，例如具有高流量负载的Web服务器，数据流处理或实时大数据分析。弹性通常通过自动扩展机制在云平台和虚拟化数据中心中实现。这些基于特定基础结构和/或服务性能指标的价值做出自动资源供应决策。本文提出并评估了一种基于机器学习技术的新型预测自动缩放机制，用于时间序列预测和排队论。新机制旨在准确预测分布式服务器的处理负载，并估计必须提供的适当数量的资源，以优化服务响应时间并满足用户签定的SLA，同时按顺序减少资源的过度供应以减少能源消耗和基础设施成本。结果表明，所提出的模型比其他经典模型具有更好的预测精度，并使资源分配更接近最优情况。同时减少资源过度配置，以减少能源消耗和基础设施成本。结果表明，所提出的模型比其他经典模型具有更好的预测精度，并使资源分配更接近最优情况。同时减少资源过度配置，以减少能源消耗和基础设施成本。结果表明，所提出的模型比其他经典模型具有更好的预测精度，并使资源分配更接近最优情况。