Abstract
The objective of the study is to establish task scheduling process by examining the various real times scheduling algorithm. Subsequently, the research attempted to propose a new algorithm for task scheduling in a multiprocessor environment. In addition, the study planned to implement the new algorithm for the security issues, hardware and software implementation. For developing real-time scheduling, TORSCHE toolbox is used. A novel algorithm was developed using features of particle swarm optimization, Cuckoo search, and fuzzy concepts. The findings showed that the proposed algorithm executes a maximum number of the process at a minimum time.
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Singh, J., Singh, G.: Improved task scheduling on parallel system using genetic algorithm. Int. J. Comput. Appl. 39, 17–22 (2012). https://doi.org/10.5120/4912-7449
Kwok, Y.-K., Ahmad, I.: Static scheduling algorithms for allocating directed task graphs to multiprocessors. ACM Comput. Surv. 31, 406–471 (1999). https://doi.org/10.1145/344588.344618
Rajak, R., Katti, C.: Static task scheduling algorithm with minimum distance for multiprocessor system (STMD). Smart Comput. Rev. (2015). https://doi.org/10.6029/smartcr.2015.02.004
Kaur, R., Kaur, R.: Multiprocessor scheduling using task duplication based scheduling algorithms: a review paper. Int. J. Appl. Innov. Eng. Manag. 2, 311–317 (2013)
Gujarati, A., Cerqueira, F., Brandenburg, B.B.: Multiprocessor real-Time Scheduling with Arbitrary Processor Affinities: From Practice to Theory. Germany (2014)
Rajak, N., Dixit, A.: Classification of list task scheduling algorithms: a short review paper. J. Ind. Intell. Inf. 2, 320–323 (2014). https://doi.org/10.12720/jiii.2.4.320-323
Boveiri, H.R.: Multiprocessor task graph scheduling using a novel graph-like learning Automata. Int. J. Grid. Distrib. Comput. 8, 41–54 (2015)
Sharma, A., Kaur, M.: An efficient task scheduling of multiprocessor using genetic algorithm based on task height. Int. J. Hybrid. Inf. Technol. 8, 83–90 (2015)
Kutil, M., Sucha, P., Capek, R., Hanzalek, Z.: Optimization and scheduling toolbox. In: Leite EP (ed) Matlab - Modelling, Programming and Simulations, pp 239–276. (2010)
Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of ICNN’95 - International Conference on Neural Networks, IEEE, pp 1942–1948. (1995)
Parsopoulos, K.E., Vrahatis, M.N.: Recent approaches to global optimization problems through particle swarm optimization. Nat. Comput. 1, 235–306 (2002). https://doi.org/10.1023/A:1016568309421
Clerc, M., Kennedy, J.: The particle swarm - explosion, stability, and convergence in a multidimensional complex space. IEEE Trans. Evol. Comput. 6, 58–73 (2002). https://doi.org/10.1109/4235.985692
Van Den, Bergh F., Engelbrecht, A.P.: A study of particle swarm optimization particle trajectories. Inf Sci (Ny) 176, 937–971 (2006). https://doi.org/10.1016/j.ins.2005.02.003
Tripathi, P.K., Bandyopadhyay, S., Pal, S.K.: Multi-objective particle swarm optimization with time variant inertia and acceleration coefficients. Inf. Sci. (Ny) 177, 5033–5049 (2007). https://doi.org/10.1016/j.ins.2007.06.018
Kamat, S., Karegowda, A.G.: A brief survey on cuckoo search applications. International Conference on Advances in Computer & Communication Engineering (ACCE - 2014), pp. 7–14. Department of CSE & ISE, Vemana Institute of Technology (2014)
Nour, M., Ooi, J., Chan, K.Y.: Fuzzy logic control vs. conventional PID control of an inverted pendulum robot. In: 2007 International Conference on Intelligent and Advanced Systems, IEEE, pp 209–214. (2007)
Shingare, P., Joshi, M.A.: Modeling and robust control of level in hybrid tanks. Proceedings of the world academy of science, engineering and technology, pp. 279–283. The Pennsylvania State University, State College (2007)
Vadigepalli, R., Gatzke, E., Doyle, F.: Robust control of a multivariable experimental four-tank system. Ind. Eng. 40, 1916–1927 (2001). https://doi.org/10.1021/ie000381p
Rusli, E., Ang, S., Braatz, R.D.: Quadruple tank process control experiment. J. Chem. Eng. Educ. 38, 1–25 (2004)
Liutkeviius, R., Dainys, S.: Hybrid fuzzy model of a nonlinear plant. Inf. Technol. Control 34, 51–56 (2005)
Duan, Y., Boulet, B., Michalska, H.: Application of IMC-based robust tunable controller design to water tank level regulation. Proceeding MIC’07 Proceedings of the 26th IASTED International Conference on Modelling, Identification, and Control, pp. 285–290. ACTA Press Anaheim, CA (2007)
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Josephson, J., Ramesh, R. A novel algorithm for real time task scheduling in multiprocessor environment. Cluster Comput 22 (Suppl 6), 13761–13771 (2019). https://doi.org/10.1007/s10586-018-2083-5
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DOI: https://doi.org/10.1007/s10586-018-2083-5