Bayesian Optimization (BO) is an efficient method for finding optimal cloud computing configurations for several types of applications. On the other hand, Machine Learning (ML) methods can provide useful knowledge about the application at hand thanks to their predicting capabilities. In this paper, we propose a hybrid algorithm that is based on BO and integrates elements from ML techniques, to find the optimal configuration of time-constrained recurring jobs executed in cloud environments. The algorithm is tested by considering edge computing and Apache Spark big data applications. The results we achieve show that this algorithm reduces the amount of unfeasible executions up to 2-3 times with respect to state-of-the-art techniques.

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We describe BayesMix, a C++ library for MCMC posterior simulation for general Bayesian mixture models. The goal of BayesMix is to provide a self-contained ecosystem to perform inference for mixture models to computer scientists, statisticians and practitioners. The key idea of this library is extensibility, as we wish the users to easily adapt our software to their specific Bayesian mixture models. In addition to the several models and MCMC algorithms for posterior inference included in the library, new users with little familiarity on mixture models and the related MCMC algorithms can extend our library with minimal coding effort. Our library is computationally very efficient when compared to competitor software. Examples show that the typical code runtimes are from two to 25 times faster than competitors for data dimension from one to ten. Our library is publicly available on Github at https://github.com/bayesmix-dev/bayesmix/.

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.