A Coordinated Reactive and Predictive Approach to Cloud Elasticity
A Coordinated Reactive and Predictive Approach to Cloud Elasticity
复制标题
协调反应性和预测性的云弹性方法
DOI：
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发表时间：
2013
期刊：
影响因子：
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通讯作者：
T. Ellahi
中科院分区：
文献类型：
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作者：
Laura R. Moore;Kathryn Bean;T. Ellahi
基于付费按服务为导向的架构，云计算范式承诺具有成本效益的IT解决方案。为了有效地满足波动需求，平台-ASA服务解决方案提供了具有按需可扩展性的共享环境。对于服务提供商来说，实施能够最佳利用资源的弹性可扩展性机制，同时保证应用程序性能继续满足服务质量指标的弹性可伸缩性机制仍然是一个开放的挑战。通常，云提供商仅提供基于反应性规则的机制来触发缩放操作。我们引入了一个新的弹性管理框架，该框架结合了反应性和预测控制器。我们的弹性控制器根据反应性规则在线构建预测模型，代表了通用产品的自然扩展。我们讨论了框架的基本体系结构，并描述控制器在同时工作并相互补充。我们提出了一个基于实际数据集的案例研究，该案例研究证明了我们实时云容量框架的可行性。关键字弹性；预测自动缩放；平台 - 无效。
Based on pay-per-use service-oriented architectures, the cloud computing paradigm promises cost-efficient IT solutions. To meet fluctuating demands efficiently, Platform-asa-Service solutions offer shared environments with on-demand scalability. It remains an open challenge for service providers to implement elastic scalability mechanisms capable of optimally utilizing resource whilst simultaneously guaranteeing that application performance continues to meet Quality of Service metrics. Typically, cloud providers offer only reactive rulebased mechanisms for triggering scaling actions. We introduce a new elasticity management framework that combines reactive and predictive controllers. Our elasticity controller builds predictive models online based on the reactive rules, representing a natural extension to the common offering. We discuss the underlying architecture of the framework and describe how the controllers operate in tandem and complement each other. We present a case study based on real datasets that demonstrates the feasibility of our real-time cloud capacity framework. Keywords-elasticity; predictive; auto-scaling; platform-as-aservice.