Model based design of super schedulers managing catastrophic scenario in hard real time systems

TL;DR

This paper introduces a model-based super scheduler for hard real-time systems that effectively manages catastrophic scenarios by dynamically scheduling tasks, minimizing deadline misses, and maintaining process stability, even under complex conditions.

Contribution

It presents an optimal scheduling algorithm for catastrophic scenarios, incorporating a model for process stability and fault-tolerance with EDF scheduling in a multi-processor environment.

Findings

The proposed scheduler reduces deadline misses during catastrophes.

The model maintains system stability under extreme conditions.

Simulation results demonstrate effectiveness across various task patterns.

Abstract

The conventional design of real-time approaches depends heavily on the normal performance of systems and it often becomes incapacitated in dealing with catastrophic scenarios effectively. There are several investigations carried out to effectively tackle large scale catastrophe of a plant and how real-time systems must reorganize itself to respond optimally to changing scenarios to reduce catastrophe and aid human intervention. The study presented here is in this direction and the model accommodates catastrophe generated tasks while it tries to minimize the total number of deadline miss, by dynamically scheduling the unusual pattern of tasks. The problem is NP hard. We prove the methods for an optimal scheduling algorithm. We also derive a model to maintain the stability of the processes. Moreover, we study the problem of minimizing the number of processors required for scheduling with a set of periodic and sporadic hard real time tasks with primary/backup mechanism to achieve fault tolerance. EDF scheduling algorithms are used on each processor to manage scenario changes. Finally we present a simulation of super scheduler with small, medium and large real time tasks pattern for catastrophe management.