Abstract
Recently many solutions have been proposed to lower the computational cost of feasibility analysis for real-time systems. The computational cost of feasibility tests can be lowered by strategies such as lowering the number of scheduling points needed during analysis, starting feasibility analysis from lowest priority, or starting schedulability tests for a task with larger scheduling point. All these techniques significantly reduce the computation time of feasibility analysis for fixed priority systems. The computation time of such tests can be further reduced by combining various solutions for efficient feasibility analysis of periodic task sets. In this work, we integrate both lowest priority first with largest points first solution to derive a faster feasibility analysis test for fixed priority system. Our experimental evaluations suggest that the proposed technique significantly lowers the computational cost of the test when system utilization is in the range of 80% or when the ratio between the task period of a lower priority task and the highest priority task is large.
Highlights
One of the main component of an operating system is multitasking that enables running multiple tasks on the same computer system
We show the experimental evaluation of Corollary 3.2 and compare our results with previous techniques
A similar analysis is done in (Min-Allah, 2019) but the focus in that work was on highest priority first while we study in feasibility problem using lowest priority first counterpart
Summary
One of the main component of an operating system is multitasking that enables running multiple tasks on the same computer system. A scheduler run process tasks based on some criteria such first come first out, shortest job first or round robin etc. All these solutions have pros and cons and no single scheduling policy is applicable to a diverse set of applications (Liu and Layland, 1973; Leung and Whitehead, 1982; George et al, 1996; Min-Allah, 2019; Bini and Buttazzo, 2004; 2001). The scheduling algorithm used is RMS and task set consist of n tasks, while the underlying system has a single processor system
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