Self-Learning Cloud Controllers: Fuzzy Q-Learning for Knowledge Evolution
This addresses the challenge for cloud providers and application developers in automating resource scaling more effectively, though it appears incremental as it builds on existing fuzzy control methods.
The paper tackles the problem of cloud controllers struggling to define optimal adaptation rules due to limited knowledge of applications and infrastructure, by proposing FQL4KE, a self-learning fuzzy controller that learns rules at runtime, which experimentally outperforms a non-learning fuzzy controller and Azure auto-scaling.
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.