Towards an Adaptive, Fully Automated Performance Modeling Methodology for Cloud Applications

The advent of the Cloud computing era along with the wide adoption of the distributed paradigm has enabled applications to increase their performance standards and greatly extend their scalability limits. Nevertheless, the ability of modern applications to be deployed in numerous different ways has complicated their structure and enormously increased the difficulty of extracting accurate performance models for them. This capability is crucial for many operations, such as the identification of the most appropriate execution setups for an anticipated workload, finding bottlenecks, etc. In this work, we propose a fully automated performance modeling methodology that aims at the creation of highly accurate performance models for a given maximum number of deployments. The main idea of the proposed methodology lies on the "smart" exploration of the application configuration space, the selection and deployment of a set of representative application configurations and the construction of the performance model through the adoption of Machine Learning techniques. Moreover, taking into consideration the often unstable and error-prone nature of the cloud, the proposed system attempts to overcome transient cloud failures that occur during the application deployment phase through the re-execution of the parts that failed. Our evaluation, conducted for a set of real-world applications frequently deployed over cloud infrastructures, indicates that our system is capable of both constructing performance models of high accuracy and doing so in an efficient manner, fixing application deployments that present errors without requiring human intervention.