An Algorithm to Reduce the Time Complexity of Earliest Deadline First Scheduling Algorithm in Real-Time System

TL;DR

This paper proposes modifications to the Earliest Deadline First (EDF) scheduling algorithm in multiprocessor real-time systems to reduce its time complexity, decrease task migrations, and improve predictability, with a focus on sensitivity analysis using a new EFDF approach.

Contribution

The paper introduces a modified EFDF algorithm that reduces task migrations and enhances predictability in EDF scheduling for multiprocessor real-time systems.

Findings

Modified EFDF reduces task migrations.

Partitioning schemes excel in hard real-time systems.

Global schemes are better suited for soft real-time systems.

Abstract

In this paper I have study to Reduce the time Complexity of Earliest Deadline First (EDF), a global scheduling scheme for Earliest Deadline First in Real Time System tasks on a Multiprocessors system. Several admission control algorithms for Earliest Deadline First (EDF) are presented, both for hard and soft real-time tasks. The average performance of these admission control algorithms is compared with the performance of known partitioning schemes. I have applied some modification to the global Earliest Deadline First (EDF) algorithms to decrease the number of task migration and also to add predictability to its behavior. The Aim of this work is to provide a sensitivity analysis for task deadline context of multiprocessor system by using a new approach of EFDF (Earliest Feasible Deadline First) algorithm. In order to decrease the number of migrations we prevent a job from moving one processor to another processor if it is among the m higher priority jobs. Therefore, a job will continue its execution on the same processor if possible (processor affinity). The result of these comparisons outlines some situations where one scheme is preferable over the other. Partitioning schemes are better suited for hard real-time systems, while a global scheme is preferable for soft real-time systems.