Anomaly detection in supercomputers is a very difficult problem due to the big scale of the systems and the high number of components. The current state of the art for automated anomaly detection employs Machine Learning methods or statistical regression models in a supervised fashion, meaning that the detection tool is trained to distinguish among a fixed set of behaviour classes (healthy and unhealthy states).

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The benefits of combinatorial optimization techniques for the solution of real-world industrial problems are an acknowledged evidence; yet, the application of those approaches to many practical domains still encounters active resistance by practitioners, in large part due to the difficulty to come up with accurate declarative representations. We propose a simple and effective technique to bring hard-todescribe systems within the reach of Constraint Optimization methods; the goal is achieved by embedding into a combinatorial model a softcomputing paradigm, namely Neural Networks, properly trained before their insertion. The approach is flexible and easy to implement on top of available Constraint Solvers. To provide evidence for the viability of the proposed method, we tackle a thermal aware task allocation problem for a multi-core computing platform.

Classical scheduling formulations typically assume static resource requirements and focus on deciding when to start the problem activities, so as to optimize some performance metric. In many practical cases, however, the decision maker has the ability to choose the resource assignment as well as the starting times: this is a far-from-trivial task, with deep implications on the quality of the final schedule. Joint resource assignment and scheduling problems are incredibly challenging from a computational perspective. They have been subject of active research in Constraint Programming (CP) and in Operations Research (OR) for a few decades, with quite difference techniques. Both the approaches report individual successes, but they overall perform equally well or (from a different perspective) equally poorly. In particular, despite the well known effectiveness of global constraints for scheduling, comparable results for joint filtering of assignment and scheduling variables have not yet been achieved. Recently, hybrid approaches have been applied to this class of problems: most of them work by splitting the overall problem into an assignment and a scheduling subparts; those are solved in an iterative and interactive fashion with a mix of CP and OR techniques, often reporting impressive speed-ups compared to pure CP and OR methods. Motivated by the success of hybrid algorithms on resource assignment and scheduling, we provide a cross-disciplinary survey on such problems, including CP, OR and hybrid approaches. The main effort is to identify key modeling and solution techniques: they may then be applied in the construction of new hybrid algorithms, or they may provide ideas for novel filtering methods (possibly based on decomposition, or on alternative representations of the domain store). In detail, we take a constraint-based perspective and, according to the equation CP = model + propagation + search, we give an overview of state-of-art models, propagation/bounding techniques and search strategies.