Proactive self-adaptation using Model Predictive Control (MPC) is well studied for software systems operating in dynamic environments. However, its practical adoption is limited by two key challenges. First, the modeling gap: traditional MPC requires state-space models that are difficult to construct for complex software systems, often demanding extensive domain expertise and incurring high computational costs. Second, the tuning gap: configuring MPC to balance abstract and competing objectives—such as performance, resource efficiency, and control stability—typically relies on manual, expert-driven tuning, impeding autonomous operation. To address these challenges, we propose a dual-layer MPC framework. At the lower layer, an Input Convex Neural Network (ICNN) is used to learn complex nonlinear dynamics directly from data, enabling tractable optimization and reducing the modeling burden. At the upper layer, a delta-driven controller manager adaptively tunes the lower-layer MPC by monitoring deviations in system behavior and estimating the impact of different cost components on overall utility, thereby automating the tuning process. We evaluate our approach on SIMDEX, a job scheduling simulator, where it demonstrates superior performance over both the traditional requirements-oriented MPC framework CobRA and the non-adaptive ICNN-based controller, achieving a better balance of performance, resource efficiency, and control stability.