Fuzzy Self-Learning Controllers for Elasticity Management in Dynamic Cloud Architectures

Cloud controllers support the operation and quality management of dynamic cloud architectures by automatically scaling the compute resources to meet performance guarantees and minimize resource costs. Existing cloud controllers often resort to scaling strategies that are codified as a set of architecture adaptation rules. However, for a cloud provider, deployed application architectures are 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. We propose the dynamic learning of adaptation rules for deployed application architectures in the cloud. We introduce FQL4KE, a self-learning fuzzy controller that learns and modifies fuzzy rules at runtime. The benefit is that we do not have to rely solely on precise design-time knowledge, which may be difficult to acquire. FQL4KE empowers users to configure cloud controllers by simply adjusting weights representing priorities for architecture quality instead of defining complex rules. FQL4KE has been experimentally validated using the cloud application framework ElasticBench in Azure and OpenStack. The experimental results demonstrate that FQL4KE outperforms both a fuzzy controller without learning and the native Azure auto-scalin

Control Strategies for Self-Adaptive Software Systems

The pervasiveness and growing complexity of software systems are challenging software engineering to design systems that can adapt their behavior to withstand unpredictable, uncertain, and continuously changing execution environments. Control theoretical adaptation mechanisms have received growing interest from the software engineering community in the last few years for their mathematical grounding, allowing formal guarantees on the behavior of the controlled systems. However, most of these mechanisms are tailored to specific applications and can hardly be generalized into broadly applicable software design and development processes. This article discusses a reference control design process, from goal identification to the verification and validation of the controlled system. A taxonomy of the main control strategies is introduced, analyzing their applicability to software adaptation for both functional and nonfunctional goals. A brief extract on how to deal with uncertainty complements the discussion. Finally, the article highlights a set of open challenges, both for the software engineering and the control theory research communities

LOVER: Light-weight fOrmal Verification of adaptivE systems at Run time

Adaptive systems are able to modify their behaviors to re- spond to signicant changes at run time such as component failures. In many cases, run-time adaptation is simply replacing a piece of system with a new one without interrupting the system operation. In terms of component-based systems, an adaptation may be dened as replacing a system component with a new version at run time. However, updating a system with new components requires the assurance that the new conguration will fully satisfy the expected requirements. Formal verication has been widely used to guarantee that a system specication satises a set of properties. However, applying verication techniques at run time for any potential change can be very expensive and sometimes unfeasible. In this paper, we present a framework, called LOVER, for the lightweight verication of component-based adaptive systems at run time. LOVER provides a new process model supported with formalisms, verication algorithms and tool to verify a signicant subset of CTL properties