AidOps:在云中提供数据驱动的高可用性服务

D. Lugones, Jordi Arjona Aroca, Yue Jin, A. Sala, V. Hilt
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引用次数: 8

摘要

具有高可用性需求的服务虚拟化需要重新审视传统的操作和供应流程。提供商正在虚拟机上的软件中实现服务,而不是使用专用设备来动态调整服务容量以适应不断变化的需求。云编排系统控制部署的服务实例的数量,以确保每个服务都有足够的容量来满足传入的工作负载。然而,确定服务的合适构建是具有挑战性的,因为安装新实例需要时间,并且过度的重新配置(即伸缩入/出)可能导致稳定性降低。在本文中,我们介绍了AidOps,这是一个云编排系统,利用机器学习和特定领域的知识来预测流量需求,优化服务性能和成本。AidOps不需要保守的服务供应来满足最坏情况的需求,并且在满足服务质量期望的同时显着降低了运营成本。我们在私有云中使用企业应用程序和通信服务对我们的框架进行了实际流量评估。我们的结果显示,与现有的编排系统相比,服务性能指标提高了4倍。AidOps达到99.985%的可用性水平,同时将运营成本降低至少20%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
AidOps: a data-driven provisioning of high-availability services in cloud
The virtualization of services with high-availability requirements calls to revisit traditional operation and provisioning processes. Providers are realizing services in software on virtual machines instead of using dedicated appliances to dynamically adjust service capacity to changing demands. Cloud orchestration systems control the number of service instances deployed to make sure each service has enough capacity to meet incoming workloads. However, determining the suitable build-out of a service is challenging as it takes time to install new instances and excessive re-configurations (i.e. scale in/out) can lead to decreased stability. In this paper we present AidOps, a cloud orchestration system that leverages machine learning and domain-specific knowledge to predict the traffic demand, optimizing service performance and cost. AidOps does not require a conservative provisioning of services to cover for the worst-case demand and significantly reduces operational costs while still fulfilling service quality expectations. We have evaluated our framework with real traffic using an enterprise application and a communication service in a private cloud. Our results show up to 4X improvement in service performance indicators compared to existing orchestration systems. AidOps achieves up to 99.985% availability levels while reducing operational costs at least by 20%.
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