Abstract: 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.

Abstract: Self-adapting systems based on multiple concurrent applications must decide how to allocate scarce resources to applications and how to set the quality parameters of each application to best satisfy the user. Past work has made those decisions with analytic models that used current resource availability information: they react to recent changes in resource availability as they occur, rather than anticipating future availability. These reactive techniques may model each local decision optimally, but the accumulation of decisions over time nearly always becomes less than optimal.

 In this paper, we propose an approach to self adaptation, called anticipatory configuration that leverages

predictions of future resource availability to improve utility for the user over the duration of the task. The approach solves the following technical challenges: (1) how to express resource availability prediction, (2) how to combine prediction from multiple sources, and (3) how to leverage predictions continuously while improving utility to the user. Our experiments show that when certain adaptation operations are costly, anticipatory configuration provides better utility to the user than reactive configuration, while being comparable in resource demand.

Abstract: We propose an improved approach to dynamic configuration of resource-aware applications. The new anticipatory model of configuration maximizes utility based on three inputs: user preferences, application capability profiles, and resource availability. In this respect, the proposed model is similar to a model of configuration described in [2]. However, the latter addresses the dynamic nature of the problem by reacting to changes (such as decrease in resource availability), and maximizes the utility in a point-wise manner. The newly proposed anticipatory approach explicitly models the duration of the task and leverages possible information about the future (such as stochastic resource availability over the expected duration of the task). We expect that the anticipatory model will improve user's utility, conserve scarce resources, and reduce the amount of disruption to the user resulting from changes when compared to the reactive model. However, the optimization problem underlying the anticipatory model is computationally more difficult than the problem underlying the reactive model. We would like to investigate if the anticipatory approach is feasible and efficient in practice while delivering the above-mentioned improvements. In this paper, we carefully state the model of anticipatory configuration, highlight the sources of complexity in the problem, propose an algorithm to the anticipatory configuration problem, and provide a roadmap for research.