Efficient Autoscaling in the Cloud Using Predictive Models for Workload Forecasting
Efficient Autoscaling in the Cloud Using Predictive Models for Workload Forecasting
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DOI：
10.1109/cloud.2011.42
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发表时间：
2011-07
期刊：
影响因子：
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通讯作者：
N. Roy;A. Dubey;A. Gokhale
中科院分区：
文献类型：
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作者：
N. Roy;A. Dubey;A. Gokhale
利用云资源的大型基于组件的企业应用程序期望根据客户和服务提供商之间的服务级别协议提供服务质量(Qos)保证。在云计算环境中，自动扩展机制承诺为应用程序提供服务质量属性，同时有效利用资源，并为服务提供商保持较低的运营成本。尽管自动扩展的优势显而易见，但实现自动扩展的全部潜力是困难的，因为面对客户端工作负载模式的显著变化，需要准确估计资源使用情况带来了多重挑战。针对目前普遍缺乏有效的工作量预测和资源优化分配技术的问题，本文提出了三点改进意见。首先，讨论了在云中自动伸缩所涉及的挑战。其次，提出了一种用于资源自动伸缩的负载预测模型预测算法。实验结果表明，该算法可以在满足应用服务质量的前提下，以较低的运行成本对资源进行分配和处理。
Large-scale component-based enterprise applications that leverage Cloud resources expect Quality of Service(QoS) guarantees in accordance with service level agreements between the customer and service providers. In the context of Cloud computing, auto scaling mechanisms hold the promise of assuring QoS properties to the applications while simultaneously making efficient use of resources and keeping operational costs low for the service providers. Despite the perceived advantages of auto scaling, realizing the full potential of auto scaling is hard due to multiple challenges stemming from the need to precisely estimate resource usage in the face of significant variability in client workload patterns. This paper makes three contributions to overcome the general lack of effective techniques for workload forecasting and optimal resource allocation. First, it discusses the challenges involved in auto scaling in the cloud. Second, it develops a model-predictive algorithm for workload forecasting that is used for resource auto scaling. Finally, empirical results are provided that demonstrate that resources can be allocated and deal located by our algorithm in a way that satisfies both the application QoS while keeping operational costs low.