A review of machine learning and meta-heuristic methods for scheduling parallel computing systems / Suejb Memeti, Sabri Pllana, Alécio Binotto, Joanna Kołodziej, Ivona Brandic // W: LOPAL '18 Proceedings ... więcej

A review of machine learning and meta-heuristic methods for scheduling parallel computing systems / Suejb Memeti, Sabri Pllana, Alécio Binotto, Joanna Kołodziej, Ivona Brandic // W: LOPAL '18 Proceedings of the International Conference on Learning and Optimization Algorithms: Theory and Applications [Dokument elektroniczny]. – New York : ACM, 2018. – 6 s. : tab. – Tryb dostępu: https://dl.acm.org/citation.cfm?id=3230906&dl=ACM&coll=DL. – Oznaczenie ref./art.: Article No.: 5. – Bibliogr. 45 poz. – doi: 10.1145/3230905.3230906. – ISBN 978-1-4503-5304-5

Optimized software execution on parallel computing systems demands consideration of many parameters at run-time. Determining the optimal set of parameters in a given execution context is a complex task, ... więcej

Optimized software execution on parallel computing systems demands consideration of many parameters at run-time. Determining the optimal set of parameters in a given execution context is a complex task, and therefore to address this issue researchers have proposed different approaches that use heuristic search or machine learning. In this paper, we undertake a systematic literature review to aggregate, analyze and classify the existing software optimization methods for parallel computing systems. We review approaches that use machine learning or meta-heuristics for scheduling parallel computing systems. Additionally, we discuss challenges and future research directions. The results of this study may help to better understand the state-of-the-art techniques that use machine learning and meta-heuristics to deal with the complexity of scheduling parallel computing systems. Furthermore, it may aid in understanding the limitations of existing approaches and identification of areas for improvement