Advertisement

Cluster Computing

, Volume 22, Supplement 6, pp 13761–13771 | Cite as

A novel algorithm for real time task scheduling in multiprocessor environment

  • Joel Josephson
  • R. Ramesh
Article

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.

Keywords

Real-time scheduling Task scheduling PSO Cuckoo search Fuzzy 

References

  1. 1.
    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 CrossRefGoogle Scholar
  2. 2.
    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 CrossRefGoogle Scholar
  3. 3.
    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 CrossRefGoogle Scholar
  4. 4.
    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)Google Scholar
  5. 5.
    Gujarati, A., Cerqueira, F., Brandenburg, B.B.: Multiprocessor real-Time Scheduling with Arbitrary Processor Affinities: From Practice to Theory. Germany (2014)Google Scholar
  6. 6.
    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 CrossRefGoogle Scholar
  7. 7.
    Boveiri, H.R.: Multiprocessor task graph scheduling using a novel graph-like learning Automata. Int. J. Grid. Distrib. Comput. 8, 41–54 (2015)CrossRefGoogle Scholar
  8. 8.
    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)CrossRefGoogle Scholar
  9. 9.
    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)Google Scholar
  10. 10.
    Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of ICNN’95 - International Conference on Neural Networks, IEEE, pp 1942–1948. (1995)Google Scholar
  11. 11.
    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 MathSciNetCrossRefzbMATHGoogle Scholar
  12. 12.
    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 CrossRefGoogle Scholar
  13. 13.
    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 MathSciNetCrossRefzbMATHGoogle Scholar
  14. 14.
    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 MathSciNetCrossRefzbMATHGoogle Scholar
  15. 15.
    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)Google Scholar
  16. 16.
    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)Google Scholar
  17. 17.
    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)Google Scholar
  18. 18.
    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 CrossRefGoogle Scholar
  19. 19.
    Rusli, E., Ang, S., Braatz, R.D.: Quadruple tank process control experiment. J. Chem. Eng. Educ. 38, 1–25 (2004)Google Scholar
  20. 20.
    Liutkeviius, R., Dainys, S.: Hybrid fuzzy model of a nonlinear plant. Inf. Technol. Control 34, 51–56 (2005)Google Scholar
  21. 21.
    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)Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Joel Josephson
    • 1
  • R. Ramesh
    • 1
  1. 1.Electrical and Electronics Engineering, College of Engineering GuindyAnna UniversityChennaiIndia

Personalised recommendations