Optimization of Automotive Software Distribution on Multi-core Systems using Machine Learning Approaches
| dc.contributor.author | Raza, Syed Aoun | |
| dc.contributor.author | Vallavanthara, Amal Jose | |
| dc.contributor.author | Nidavan, Rakesh | |
| dc.contributor.editor | Kelter, Udo | |
| dc.date.accessioned | 2024-07-26T10:18:37Z | |
| dc.date.available | 2024-07-26T10:18:37Z | |
| dc.date.issued | 2020 | |
| dc.description.abstract | Multi-core software should be partitioned under different constraints e.g., balanced execution load on cores, timing behavior and optimized level of communication/ synchronization among different system components. The objective is to efficiently distribute the processes onto multi-core hardware such that the system has reduced communication/ synchronization complexity. Moreover, a bad distribution strategy during migration from single- to multi-core and from multi- to many-core hardware does not always return the expected performance gain. This paper presents two novel AI-based approaches for optimal distribution (minimal inter-core communication inspite of no deadline misses) of software system on multi-core hardware architecture. We discuss the comparisons of our machine learning solutions based on unsupervised and reinforcement learning. We share the benefits and limitations of using unsupervised learning and reinforcement learning based on our experience. | en |
| dc.identifier.issn | 0720-8928 | |
| dc.identifier.uri | https://dl.gi.de/handle/20.500.12116/44147 | |
| dc.language.iso | en | |
| dc.pubPlace | Bonn | |
| dc.publisher | Gesellschaft für Informatik e.V. | |
| dc.relation.ispartof | Softwaretechnik-Trends Band 40, Heft 2 | |
| dc.relation.ispartofseries | Softwaretechnik-Trends | |
| dc.subject | multi-core software | |
| dc.subject | partitioning | |
| dc.subject | load | |
| dc.subject | synchronization | |
| dc.subject | distribution strategy | |
| dc.subject | performance | |
| dc.subject | machine learning | |
| dc.title | Optimization of Automotive Software Distribution on Multi-core Systems using Machine Learning Approaches | en |
| dc.type | Text/Conference Paper | |
| mci.conference.date | 16.-18. September 2020 | |
| mci.conference.location | Paderborn | |
| mci.conference.sessiontitle | 22. Workshop Software-Reengineering und -Evolution (WSRE) und 11. Workshop Design for Future (DFF) | |
| mci.reference.pages | 30-31 |
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