SLO-ML: A Language for Service Level Objective Modelling in Multi-cloud Applications
This addresses the problem of inefficient cloud service selection for customers in multi-cloud applications, though it appears incremental as it builds on existing cloud modelling languages.
The authors tackled the lack of practical support for automating service selection based on service level objectives in cloud modelling languages by introducing SLO-ML, a generative language that captures requirements and generates deployment code, showing profound potential in productivity and usability through real-world case studies and scalability tests.
Cloud modelling languages (CMLs) are designed to assist customers in tackling the diversity of services in the cloud market. While many CMLs have been proposed in the literature, they lack practical support for automating the selection of services based on the specific service level objectives of a customer's application. We put forward SLO-ML, a novel and generative CML to capture service level requirements and, subsequently, to select the services to honour customer requirements and generate the deployment code appropriate to these services. We present the architectural design of SLO-ML and the associated broker that realises the deployment operations. We rigorously evaluate SLO-ML using a mixed methods approach. First, we exploit an experimental case study with a group of researchers and developers using a real-world cloud application. We also assess overheads through an exhaustive set of empirical scalability tests. Through expressing the levels of gained productivity and experienced usability, we highlight SLO-ML's profound potential in enabling user-centric cloud brokers. We also discuss limitations as application requirements grow.