In healthcare systems, practitioners are responsible for making decisions when a patient’s health, or even life, are at stake. Real-time data-driven modeling, analysis, and prediction approaches, such as the Digital Shadow (DS) paradigm, inform and support decision-makers in such critical situations. We introduce GENGAR, a DS-based methodology to identify critical scenarios in a patient-device-physician (PDP) triad with human agents and cyber-physical devices interacting under uncertainty. The proposed solution relies on automata-based modeling and formal analysis techniques to predict and inform the practitioner of critical contingencies that may compromise patient safety, enhancing the system’s dependability. In particular, it leverages automata learning to infer and evolve a realistic patient model from clinical logs. GENGAR then exploits mutational and searchbased fuzzing to generate scenarios and detect failure cases, i.e., those violating predefined dependability requirements. Failure scenarios are then filtered using qualitative criteria on clinical plausibility, yielding up to $60 \%$ realistic cases.

Virtual screening applications are highly parameterized to optimize the balance between quality and execution performance. While output quality is critical, the entire screening process must be completed within a reasonable time. In fact, a slight reduction in output accuracy may be acceptable when dealing with large datasets. Finding the optimal quality-throughput trade-off depends on the specific HPC system used and should be re-evaluated with each new deployment or significant code update. This paper presents two parallel autotuning techniques for constrained optimization in distributed High-Performance Computing (HPC) environments. These techniques extend sequential Bayesian Optimization (BO) with two parallel asynchronous approaches, and they integrate predictions from Machine Learning (ML) models to help comply with constraints. Our target application is LiGen, a real-world virtual screening software for drug discovery. The proposed methods address two relevant challenges: efficient exploration of the parameter space and performance measurement using domain-specific metrics and procedures. We conduct an experimental campaign comparing the two methods with a popular state-of-the-art autotuner. Results show that our methods find configurations that are, on average, up to 35–42% better than the ones found by the autotuner and the default expert-picked LiGen configuration. • We propose two parallel algorithms for black-box constrained optimization. • We integrate Bayesian Optimization and Machine Learning for constraint estimation. • We use a meta-scheduler to hinge on the resources available in HPC settings. • We evaluate the benefits of the proposed approach using a relevant case study.

This paper proposes an automated framework for efficient application profiling and training of Machine Learning (ML) performance models, composed of two parts: OSCAR-P and aMLLibrary. OSCAR-P is an auto-profiling tool designed to automatically test serverless application workflows running on multiple hardware and node combinations in cloud and edge environments. OSCAR-P obtains relevant profiling information on the execution time of the individual application components. These data are later used by aMLLibrary to train ML-based performance models. This makes it possible to predict the performance of applications on unseen configurations. We test our framework on clusters with different architectures (x86 and arm64) and workloads, considering multi-component use-case applications. This extensive experimental campaign proves the efficiency of OSCAR-P and aMLLibrary, significantly reducing the time needed for the application profiling, data collection, and data processing. The preliminary results obtained on the ML performance models accuracy show a Mean Absolute Percentage Error lower than 30% in all the considered scenarios.

Bayesian Optimization (BO) is a family of powerful algorithms designed to solve complex optimization problems involving expensive black-box functions. These sequential algorithms iteratively update a surrogate model of the objective function (OF), effectively balancing exploration and exploitation to identify near-optimal solutions within a limited number of iterations. Originally designed for continuous, unconstrained domains, its efficiency has inspired adaptations for discrete, constrained optimization problems. On the other hand, Machine Learning (ML) models allow accurate predictions for black-box functions, although they typically require large amounts of data for training. Leveraging the strengths of BO and ML, research tackles the challenge of identifying optimal configurations in the context of cloud computing. This paradigm has become pervasive due to its ability to provide flexible and scalable resources. Identifying the optimal hardware-software configuration is essential for minimizing costs while meeting Quality of Service constraints. This task involves solving complex optimization problems over multidimensional discrete domains and black-box objective functions and constraints, within a limited number of iterations. To address this challenge, this work introduces d-MALIBOO , a BO-based algorithm that integrates ML techniques to enhance the efficiency of finding near-optimal solutions in discrete and bounded domains. While BO builds the surrogate model of the OF, ML models determine the feasible region of the black-box constraints and guide the BO algorithm toward promising regions of the discrete domain. Furthermore, we introduce an ɛ ɛ -greedy approach to favor exploration in domains with multiple local optima. Experimental results show that our algorithm outperforms OpenTuner, a popular framework for constrained optimization, by reducing the average regret by 29%, and SVM-CBO, a BO-based algorithm that integrates SVM models to determine the feasible region, by 82%.

Cloud computing has become essential for delivering flexible and scalable resources. In this environment, finding the optimal hardware-software configuration is crucial and often involves solving constrained black-box problems on discrete multidimensional domains. These optimizations must be performed within a limited number of evaluations to contain costs. Bayesian Optimization (BO) addresses this problem by providing near-optimal solutions with few iterations. However, the original BO algorithm was designed for continuous and unbounded domains. On the other hand, Machine Learning (ML) models are powerful tools for predicting the values of a black-box function. In this work, we present d-MALIBOO, a framework that integrates BO and ML techniques to improve the efficiency of finding optimal solutions in discrete and bounded domains. While BO suggests the next point to be evaluated by effectively balancing exploration and exploitation, ML models are valuable for determining feasibility regions and focusing the search for the optimum. Experimental results show that our algorithm outperforms alternative methods from the literature in all tested environments, especially in complex optimization scenarios, resulting in an improvement in regret by approximately 2–8 times.

Bayesian Optimization (BO) is an efficient method for finding optimal cloud configurations for several types of applications. On the other hand, Machine Learning (ML) can provide helpful knowledge about the application at hand thanks to its predicting capabilities. This work proposes a general approach based on BO, which integrates elements from ML techniques in multiple ways, to find an optimal configuration of recurring jobs running in public and private cloud environments, possibly subject to black-box constraints, e.g., application execution time or accuracy. We test our approach by considering several use cases, including edge computing, scientific computing, and Big Data applications. Results show that our solution outperforms other state-of-the-art black-box techniques, including classical autotuning and BO- and ML-based algorithms, reducing the number of unfeasible executions and corresponding costs up to 2–4 times.

Today digital revolution is having a dramatic impact on the pharmaceutical industry and the entire healthcare system. The implementation of machine learning, extreme-scale computer simulations, and big data analytics in the drug design and development process offers an excellent opportunity to lower the risk of investment and reduce the time to the patient.

aMLLibrary is an open-source, high-level Python package that allows the parallel building of multiple Machine Learning (ML) regression models. It is focused on performance modeling and supports several methods for feature engineering/selection and hyperparameter tuning. The library implements fault tolerance mechanisms to recover from system crashes, and only a simple declarative text file is required to launch a full experimental campaign for all required models. Its modular structure allows users to implement their own plugins and model-building wrappers and easily add them to the library. We test aMLLibrary on building the performance models of neural networks and image processing applications, with the best model produced often having less than 20% prediction error.