A review of machine learning and meta-heuristic methods for scheduling parallel computing systems
Authors:
- Memeti Suejb,
- Pllana Sabri,
- Alécio Binotto,
- Joanna Kołodziej,
- Ivona Brandic
Abstract
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
- Record ID
- CUT607c96a19c1349b58e07c65912d763b1
- Publication categories
- ; ;
- Author
- Pages
- [1-6]
- Other elements of collation
- tab.; Bibliografia (na s.) - 5-6; Bibliografia (liczba pozycji) - 45
- Book
- LOPAL '18 Proceedings of the International Conference on Learning and Optimization Algorithms: Theory and Applications, 2018, New York, Association for Computing Machinery, ACM, 6 p., ISBN 978-1-4503-5304-5
- Keywords in English
- machine learning, meta-heuristics, parallel computing, scheduling
- DOI
- DOI:10.1145/3230905.3230906 Opening in a new tab
- URL
- https://dl.acm.org/citation.cfm?id=3230906&dl=ACM&coll=DL Opening in a new tab
- Language
- eng (en) English
- Score (nominal)
- 20
- Additional fields
- Indeksowana w: Scopus
- Uniform Resource Identifier
- https://cris.pk.edu.pl/info/article/CUT607c96a19c1349b58e07c65912d763b1/
- URN
urn:pkr-prod:CUT607c96a19c1349b58e07c65912d763b1
* presented citation count is obtained through Internet information analysis, and it is close to the number calculated by the Publish or PerishOpening in a new tab system.