An Efficient Hybrid Scheduler Using Dynamic Slack for Real-Time Critical Task Scheduling in Multicore Automotive ECUs
Year: 2024
Type: article
Abstract: Task intensive electronic control units (ECUs) in automotive domain, equipped with multicore processors ,real time operating systems (RTOSs) and various application software, should perform efficiently and timedeterministically. The parallel computational capability offered by this multicore hardware can only beexploited and utilized if the ECU application software is parallelized. Having provided with suchparallelized software, the real time operating system scheduler component should schedule the time criticaltasks so that, all the computational cores are utilized to a greater extent and the safety critical deadlinesare met. As original equipment manufacturers (OEMs) are always motivated towards adding moresophisticated features to the existing ECUs, a large number of task sets can be effectively scheduled forexecution within the bounded time limits. In this paper, a hybrid scheduling algorithm has been proposed,that meticulously calculates the running slack of every task and estimates the probability of meetingdeadline either being in the same partitioned queue or by migrating to another. This algorithm was run andtested using a scheduling simulator with different real time task models of periodic tasks . This algorithmwas also compared with the existing static priority scheduler, which is suggested by Automotive OpenSystems Architecture (AUTOSAR). The performance parameters considered here are, the % of coreutilization, average response time and task deadline missing rate. It has been verified that, this proposedalgorithm has considerable improvements over the existing partitioned static priority scheduler based oneach performance parameter mentioned above.
Author: Mishra, Dr Geetishree
Institution: B.M.S. College of Engineering
Language: English
Topic: Real-Time Systems Scheduling
Subfield: Hardware and Architecture
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W6930342312
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