Exploring Persistent Configuration Decisions for Adaptive Data Provisioning

Abstract: Adaptive data provisioning is a key challenge in modern systems operating over heterogeneous and dynamic data sources, where multiple providers may offer the same functionality with evolving quality characteristics. Existing approaches typically perform data source selection at each invocation based on current context and quality constraints. While this strategy maximizes responsiveness, it introduces runtime overhead and may lead to unstable behavior under noisy or delayed observations. The paper proposes ARES, a configuration-driven architecture for adaptive data provisioning. The approach elevates data source selection to an explicit architectural concern by separating the set of available providers from the active runtime configuration used to resolve requests, which is maintained over time and updated according to externally defined policies. We evaluate ARES through a controlled simulation involving multiple data sources with dynamic quality profiles. Preliminary results sugges t that ARES can substantially reduce decision cost while maintaining acceptable levels of execution quality under the considered conditions. Moreover, configuration-level decisions exhibit higher effectiveness, enabling more robust adaptation despite imperfect estimated information. These findings provide initial evidence that configuration-level decision strategies may represent a promising architectural direction for balancing decision cost, execution quality, and adaptation stability in dynamic environments.