A methodology for automated service level agreement compliance prediction

Service Level Agreement (SLA) specification languages express monitorable contracts between service providers and consumers. It is of interest to determine if predictive models can be derived for SLAs expressed in such languages, if possible in a fashion that is as automated as possible. Assuming that the service developer or user uses some SLA specification languages during the service development or deployment process, the Service level agreement Compliance Prediction (SlaCP) methodology is proposed as a general engineering methodology for predicting SLA compliance. This methodology helps contractual parties to assess the probability of SLA compliance, as automatically as is feasible, by mapping an existing SLA on a stochastic model of the service and using existing numerical solution algorithms or discrete event simulation to solve the model. The SlaCP methodology is generic, but the methodology is mostly described, in this thesis, assuming the use of the Web Service Level Agreement (WSLA) and the Stochastic Discrete Event Systems (SDES) formalism. The approach taken in this methodology is firstly to associate formal semantics with WSLA elements in order to be understood mathematically precise. Then, a five-step mapping process between the source and the target formalisms is conducted. These steps include: mapping into model primitives, reward metrics, expressions for functions of these metrics, the time at which the prediction occurs, and the ultimate probability of SLA compliance. The proposed methodology is implemented in a software tool that automates most of its steps using Möbius and SPNP. The methodology is evaluated using a case study which shows the methodology’s feasibility and limitations in both theoretical and practical terms.