Algorithm for Multi-core Device of Autonomous Train System for Adaptive Braking Control

Gorobetz, Mikhail; Potapovs, Andrejs

doi:10.1109/rtucon53541.2021.9711694

Cited by 2 publications

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An integration of autonomic computing with multicore systems for performance optimization in Industrial Internet of Things

et al. 2022

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The goal of this work is to investigate how the self‐awareness characteristic of autonomic computing, paired with existing performance optimization rules, may be used in applications to minimise multi‐core processor performance concerns. The suggested self‐awareness technique can assist applications in self‐execution while also assisting other applications in executing in the system with optimal resource usage and reducing conflicts in a collaborative manner. It means self‐awareness is created in the application to get resources, schedules itself, running autonomically with respect to runtime variations in the applications and system parameters. Further, self‐aware applications would help collaboratively resolving the performance issues like; contention, bandwidth bottleneck, efficient task to core mapping, and performance monitoring through analysis in a dynamic collaborative multi‐core environment. To show the usefulness of this research, “Autonomic Computing Interface” (ACI) for is proposed Multicore systems. The proposed firmware as an interface between applications and the multi‐core system can dynamically handle performance issues with coordination of applications in an autonomic way. Also, in a highly dynamic execution environment the proposed runtime parameter tuning, existing performance improvement policies and self‐awareness approach in totality, for monitoring, analysis, and possibility for performance improvement would be effective as compared to the existing approaches which work in isolation.

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