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Relative Deadline Analysis in Multitasking RTS using RM & EDF Scheduling
Sandeep S Chapalkar1, K. Karibasappa2

1Sandeep S Chapalkar*, Assistant Professor, Department of Electronics and Communication Engineering, Dayananda Sagar College of Engineering, Bengaluru (Karnataka), India.
2K. Karibasappa, Professor, Department of Electronics and Communication Engineering, Dayananda Sagar Academy of Technology and Management, Bengaluru (Karnataka), India.

Manuscript received on May 17, 2021. | Revised Manuscript received on May 24, 2021. | Manuscript published on May 30, 2021. | PP: 246-251 | Volume-10 Issue-1, May 2021. | Retrieval Number: 100.1/ijrte.A59000510121 | DOI: 10.35940/ijrte.A5900.0510121
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: In embedded systems the time required for any process to complete its execution in multitasking environment is an important factor to understand the performance of Real Time System (RTS) and its ability fulfill the deadline requirement of each process under different process load conditions. Even though some non-critical systems provide flexibility over deadlines, the hard real time systems are to be designed to meet the deadline requirement of all processes under peak process load condition. The number of processes available in scheduling queue may vary with time, the dynamic load on processing unit also changes proportionately which in turn affects the relative deadlines of each process. The scheduling policies considered are widely used scheduling policies like Rate Monotonic (RM) and Earliest Deadline First (EDF) to analyze and understand the impact on relative deadline with respect to number of scheduled processes. The real time execution timings of each process is observed on Raspberry Pi 3b+ processing unit operating at standard frequency of 700 MHz in multitasking mode of operation. The results obtained will decisively conclude the suitable scheduling policy for a set of processes under different process load conditions. 
Keywords: Embedded, Dynamic load, load estimation, Multitasking, Optimization, Rate monotonic, Earliest Deadline First, Real time systems, Absolute deadline, Relative deadline