Modeling Uncertainty of Predictive Inputs in Anticipatory Dynamic Configuration

Dynamic adaptive systems based on multiple concurrent applications typically employ optimization models to decide how to allocate scarce resources among the applications and how to tune their runtime settings for optimal quality-of-service according to the preferences of an end user. Traditionally, such systems have avoided dealing with uncertainty by assuming that current snapshots of the relevant inputs are precise and by solving for an optimal system point. To achieve dynamic behavior, a system performs an optimization loop upon discovering changes in the input variables (e.g. changes in the available level of resources) and adapts the applications according to the new optimal solution. Unfortunately, when certain adaptation actions incur costs, such reactive adaptation strategies suffer from a significant shortcoming: several locally optimal decisions over time may often be less than optimal globally. By using predictive information about the future values of the problem inputs, we can model and implement an anticipatory adaptation strategy that helps improve the global behavior of the system in many situations. However, modeling predictions requires representing and dealing with uncertainty from different sources. In this paper, we describe our proposed approach to represent multiple sources of uncertainty and outline algorithms for solving the anticipatory configuration problem with predictive inputs.