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

TL;DR

This paper introduces FQL4KE, a self-learning fuzzy cloud controller that adapts and evolves its rules at runtime, reducing the need for pre-defined rules and improving scaling decisions in cloud environments.

Contribution

It presents a novel fuzzy Q-learning approach for cloud control that learns and updates rules dynamically during operation.

Findings

FQL4KE outperforms static fuzzy controllers in experiments.

FQL4KE surpasses native Azure auto-scaling in performance.

Runtime learning improves cloud resource management.

Abstract

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.