Open Access

ARTICLE

Fault Tolerance in the Joint EDF-RMS Algorithm: A Comparative Simulation Study

by Rashmi Sharma¹, Nitin Nitin², Deepak Dahiya^3,*

1 University of Petroleum & Energy Studies (UPES), School of Computer Science, Energy Acres Building, Bidholi, Dehradun, 248007, Uttarakhand, India
2 Department of Electrical Engineering and Computer Science, College of Engineering and Applied Science, University of Cincinnati, 2600 Clifton Ave, Cincinnati, OH 45221, United States
3 College of Computer and Information Sciences (CCIS), Majmaah University, Majmaah, 11952, Kingdom of Saudi Arabia

* Corresponding Author: Deepak Dahiya. Email:

Computers, Materials & Continua 2022, 72(3), 5197-5213. https://doi.org/10.32604/cmc.2022.025059

Received 10 November 2021; Accepted 21 February 2022; Issue published 21 April 2022

Abstract

Failure is a systemic error that affects overall system performance and may eventually crash across the entire configuration. In Real-Time Systems (RTS), deadline is the key to successful completion of the program. If tasks effectively meet the deadline, it means the system is working in pristine order. However, missing the deadline means a systemic fault due to which the system can crash (hard RTS) or degrade inclusive performance (soft RTS). To fine-tune the RTS, tolerance is the critical issue and must be handled with extreme care. This article explains the context of fault tolerance with improvised Joint EDF-RMS algorithm in RTS. The backup method has been derived to prevent the system from being recursively migrating the same task. If any task migrates three times, this migrated task will get shifted to the backup queue. This backup queue assigns the task to a backup processor and is destined for final execution. For performance evaluation purposes, a relative graph between fault and failure rates, failure and total processor utilization along with other averages have been evaluated. Furthermore, these archived results are compared with fault-tolerant Earliest Deadline First (EDF) and Rate Monotonic Scheduling (RMS) algorithms independently in relatively similar conditions. These comparisons show better performance against overloading conditions.

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Keywords

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