High-Performance Computing (HPC) is a fundamental tool for tackling complex scientific and engineering problems. Optimizing applications for the heterogeneous and massively parallel nature of modern HPC hardware is essential for achieving timely and resource-efficient results. This paper introduces ABC-MLCO, a novel Artificial Bee Colony (ABC) algorithm enhanced with machine learning for constrained optimization in discrete configuration spaces, specifically targeting HPC scenarios with black-box performance metrics. ABC-MLCO extends the original ABC algorithm with mechanisms that define the feasible region, promote escape from local optima, prevent redundant evaluations, and intensify exploration and exploitation. We first evaluate the algorithm on benchmark functions, observing improvements in regret, feasibility rate, and convergence speed over the original ABC. Then, targeting a virtual screening application for drug discovery, ABC-MLCO outperforms well-known state-of-the-art methods in terms of final regret, achieving average improvements up to $93 \%$.

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%.

The growth, development, and commercialization of artificial intelligence-based technologies such as self-driving cars, augmented-reality viewers, chatbots, and virtual assistants are driving the need for increased computing power. Most of these applications rely on Deep Neural Networks (DNNs), which demand substantial computing capacity to meet user demands. However, this capacity cannot be fully provided by users’ local devices due to their limited processing power, nor by cloud data centers due to high transmission latency from long distances. Edge cloud computing addresses this issue by processing user requests through 5G, which reduces transmission latency from local devices to computing resources and allows the offloading of some computations to cloud back-ends. This paper introduces a model for a Mobile Edge Cloud system designed for an application based on a DNN. The interaction among multiple mobile users and the edge platform is formulated as a one-leader multi-follower Stackelberg game, resulting in a challenging non-convex mixed integer nonlinear programming (MINLP) problem. To tackle this, we propose a heuristic approach based on Karush-Kuhn-Tucker conditions, which solves the MINLP problem significantly faster than the commercial state-of-the-art solvers (up to 50,000 times). Furthermore, we present an algorithm to estimate optimal platform profit when sensitive user parameters are unknown. Comparing this with the full-knowledge scenario, we observe a profit loss of approximately 1%. Lastly, we analyze the advantages for an edge provider to engage in a Stackelberg game rather than setting a fixed price for its users, showing potential profit increases ranging from 16% to 66%.

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.

To remain competitive, manufacturing companies are marketing to customers business offerings based on integrated packages of products and services – Product-Service Systems (PSS). While PSS offerings allow foreseeing economical benefits for customers and manufacturers, the sustainability assessment of PSS has not been extensively studied in the literature. While it is recognized that sustainability can be evaluated under the three dimensions of the Triple Bottom Line (TBL) – i.e. economic, environmental, and social – existing assessment methods usually concentrate only on a single one, neglecting the interplay with the other two. Thus, a more comprehensive method for the evaluation of PSS sustainability is required. This paper proposes the use of Agent-Based Modelling (ABM) as a method for jointly assessing the economic and environmental sustainability of PSS offerings. The social dimension is not considered since the research about the impacts of PSS offering on the social dimension is still at its beginning, as a consequence of the difficulties in finding common social sustainability indicators. The proposed approach overcomes the limitations of traditional sustainability assessment methods by allowing for dealing with the stochastic nature of reality, testing multiple scenarios, and understanding the impacts of PSS solutions on the economic and environmental sustainability in the long-term perspective.