A model driven engineering approach for flexible and distributed monitoring of cross-cloud applications

Abstract

Cloud computing and its computing as a utility paradigm offers on-demand resources, enabling its users to seamlessly adapt applications to the current demand. With its (virtually) unlimited elasticity, managing deployed applications becomes more and more complex raising the need for automation. Such autonomous systems leverage the importance to constantly monitor and analyse the deployed workload and the underlying infrastructure serving as knowledge-base for deriving corrective actions like scaling. Existing monitoring solutions, however are not designed to cope with a frequently changing topology. We propose a monitoring and event processing framework following a model-driven approach, that allows users to express i) the monitoring demand by directly referencing entities of the deployment context, ii) aggregate the monitoring data using mathematical expressions, iii) trigger and process events based on the monitoring data and finally iv) attach scalability rules to those events. We accompany the modelling language with a monitoring orchestration and distributed complex event processing framework, capable of enacting the model in a frequently changing multi-cloud infrastructure, considering cloud-specific aspects like communication costs.