Monitoring, Prediction and Prevention of SLA Violations in Composite Services

Abstract

We propose the PREvent framework, which is a system that integrates event-based monitoring, prediction of SLA violations using machine learning techniques, and automated runtime prevention of those violations by triggering adaptation actions in service compositions. PREvent improves on related work in that it can be used to prevent violations ex ante, before they have negatively impacted the provider's SLAs. We explain PREvent in detail and show the impact on SLA violations based on a case study.