arXiv 1507.005672015-07-02eess.SYcs.AIcs.DCcs.LGcs.SEcs.SY

自学习云控制器：用于知识演化的模糊Q学习

Self-Learning Cloud Controllers: Fuzzy Q-Learning for Knowledge Evolution

Imperial College London, UK（伦敦帝国学院）；IC4, Dublin City University, Ireland（IC4，都柏林城市大学）；University of Duisburg-Essen, Germany（杜伊斯堡- Essen大学）

Pooyan Jamshidi, Amir Sharifloo, Claus Pahl, Andreas Metzger, Giovani Estrada

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AI总结：

本文提出FQL4KE自学习模糊云控制器，通过在运行时学习和修改模糊规则，使用户能通过调整优先级权重来指定控制器，而非复杂适应规则，实验表明其优于传统控制器。

摘要：

云控制器旨在通过在运行时自动扩展计算资源来响应应用需求，以满足性能保证并最小化资源成本。现有云控制器通常依赖预定义的适应规则集，但云服务提供商难以在设计时定义最优或预置的适应规则，因为上层应用是黑箱。因此，适应决策的负担常转嫁给云应用。然而，大多数情况下，应用开发者对云基础设施了解有限。本文提出在运行时学习适应规则。为此，我们引入FQL4KE，一种自学习模糊云控制器。FQL4KE在运行时学习和修改模糊规则。其优势在于设计云控制器时无需依赖仅靠精确的设计时知识，这可能难以获取。FQL4KE使用户能够通过简单调整代表系统目标优先级的权重来指定云控制器，而不是指定复杂的适应规则。FQL4KE的适用性已在云应用框架ElasticBench中得到实验评估。实验结果表明，FQL4KE优于我们之前开发的无学习机制的模糊控制器和原生Azure自动扩展。

英文摘要：

Cloud controllers aim at responding to application demands by automatically scaling the compute resources at runtime to meet performance guarantees and minimize resource costs. Existing cloud controllers often resort to scaling strategies that are codified as a set of adaptation rules. However, for a cloud provider, applications running on top of the cloud infrastructure are more or less black-boxes, making it difficult at design time to define optimal or pre-emptive adaptation rules. Thus, the burden of taking adaptation decisions often is delegated to the cloud application. Yet, in most cases, application developers in turn have limited knowledge of the cloud infrastructure. In this paper, we propose learning adaptation rules during runtime. To this end, we introduce FQL4KE, a self-learning fuzzy cloud controller. In particular, FQL4KE learns and modifies fuzzy rules at runtime. The benefit is that for designing cloud controllers, we do not have to rely solely on precise design-time knowledge, which may be difficult to acquire. FQL4KE empowers users to specify cloud controllers by simply adjusting weights representing priorities in system goals instead of specifying complex adaptation rules. The applicability of FQL4KE has been experimentally assessed as part of the cloud application framework ElasticBench. The experimental results indicate that FQL4KE outperforms our previously developed fuzzy controller without learning mechanisms and the native Azure auto-scaling.

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