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.

MALIBOO: When Machine Learning meets Bayesian Optimization

B. Guindani;D. Ardagna;A. Guglielmi
2022-01-01

Abstract

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.
2022
IEEE 7th International Conference on Smart Cloud (SmartCloud)
9781665451796
Cloud computing, Bayesian optimization, Black- box optimization, Machine learning
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1224707
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