Coverage-Based metrics for cloud adaptation

Abstract

This work introduces novel combinatorial coverage based metrics for deciding upon automated Cloud infrastructure adaptation. Our approach utilizes a Combinatorial Testing engine, traditionally used for testing at the development phase, in order to measure the load behavior of a system in production. We determine how much the measured load behavior at runtime differs from the one observed during testing. We further estimate the involved risk of encountering untested behavior in the current configuration of the system as well as when transitioning to a new Cloud configuration using possible adaptation actions such as migration and scale-out. Based on our risk assessment, a Cloud adaptation engine may consequently decide on an adaptation action in order to transform the system to a configuration with a lesser associated risk. Our work is part of a larger project that deals with automated Cloud infrastructure adaptation. We introduce the overall approach for automated adaptation, as well as our coverage-based metrics for risk assessment and the algorithms to calculate them. We demonstrate our metrics on an example setting consisting of two sub-components with multiple instances, comprising a typical installation of a telephony application.