ACO Based Scheduling Method for Soft RTOS with Simulation and Mathematical Proofs
Jay Teraiya1, Apurva Shah2, Ketan Kotecha3
1Jay Teraiya*, Computer Engineering Department, Marwadi University, Rajkot, India.
2Apurva Shah, Computer Science and Engineering Department, The Maharaja Sayajirao University of Baroda, Baroda, India. Ketan Kotecha, The Symbiosis Institute of Technology, Pune, India.
Manuscript received on September 16, 2019. | Revised Manuscript received on 24 September, 2019. | Manuscript published on October 10, 2019. | PP: 4736-4740 | Volume-8 Issue-12, October 2019. | Retrieval Number: L3606081219/2019©BEIESP | DOI: 10.35940/ijitee.L3606.1081219
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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: The Ant Colony Optimization (ACO) algorithm is a mathematical model enlivened by the system searching conduct of ants. By taking a gander at the qualities of ACO, it is most suitable for scheduling of tasks in soft real-time systems. In this paper, the ACO based scheduling method for the soft real-time operating system (RTOS) has been profound with mathematical and practical proof. In Mathematical proof, three different Propositions and two Theorems have been given, which prove the correctness of the proposed algorithm. Practical experiments also support mathematical proofs. During the investigation, observations are gathered with different periodic task set. Algorithms have been observed regarding Success Ratio (SR) and Effective CPU utilization (ECU). ACO based scheduling algorithm has been compared with the Earliest Deadline First (EDF) algorithm with parameter SR and ECU. The EDF is dynamic scheduling algorithm and it is most suitable in RTOS when task set is preemptable. It is noted that the new algorithm is equally efficient during under loaded conditions when CPU load is less than one. ACO based scheduling algorithm performs superior during the overloaded conditions when CPU load is more than one where as EDF algorithm performance degraded in overload condition. Empirical study has been executed with a hefty Dataset consist of more than 7500 task set, and a set contains different one to nine processes where CPU load is dynamic for each process set and differ from 0.5 to 5. Algorithms have been executed on five-hundred-time unit for each process set to authenticate the accuracy of both algorithms.
Keywords: ACO, EDF, ACO, Real-Time Systems, RTOS
Scope of the Article: Computer Graphics, Simulation, and Modelling