SPECTRA: A Markovian Framework for Managing NFR Tradeoffs in Systems with Mixed Observability

Non-Functional Requirements (NFRs) play a critical role in driving self-adaptation in software systems. In Self-Adaptive Systems (SAS), satisfying multiple NFRs simultaneously introduces significant complexity, as these requirements often conflict—improving one NFR can negatively impact others. Addressing such trade-offs becomes even more challenging due to the varying degrees of observability of NFRs, with some being fully observable and others only partially observable. Traditional approaches to SAS decision-making, such as those based on Markov Decision Processes (MDPs), often assume homogeneous observability, which limits their ability to address these challenges effectively. We argue that treating NFRs as having mixed observability—where some are fully observable and others are partially observable—enables more effective decision-making. How can self-adaptive systems model and resolve trade-offs among NFRs with mixed observability to achieve better outcomes? This paper introduces SPECTRA, a multi-objective decision framework based on MDPs. SPECTRA addresses trade-offs among NFRs by leveraging a multi-objective Mixed Observability Markov Decision Process (MOMDP), which models and handles the varying observability of NFRs effectively. The approach is evaluated using scenarios from MirrorNet, a realistic Remote Data Mirroring (RDM) system utilizing Software-Defined Networking (SDN). Results show that SPECTRA achieves higher utility values, faster policy planning, and more effective trade-offs compared to existing approaches.