ADVISE: Understanding Reconfigurations in Self-Adaptive Cloud Systems

Self-adaptive cloud systems dynamically adapt their configuration to achieve Service Level Objectives (SLOs), for example, to ensure a response time below a certain threshold, in changing environments. However, understanding these recon-figurations is challenging. To address this issue, we introduce ADVISE, a visualization tool for reconfigurations in self-adaptive systems. Our adaption approach leverages the Palladio ecosystem to predict system performance and plan optimal reconfigurations by exploring the state space. The optimal path in the state space graph, determined by a utility value based on the SLOs, represents the ideal policy. Our tool provides real-time visualizations of performance predictions and reconfiguration plans aiding developers in managing and optimizing system behavior effectively. We enable both the analysis of individual states and the examination of paths within the state space graph, enabling users to analyze not only single adaptation decisions but also the overall reconfiguration behavior. Video URL: [1]