Advertisement

A Review of Dynamic Scheduling Algorithms for Homogeneous and Heterogeneous Systems

  • Mahfooz Alam
  • Asif Khan
  • Ankur K. Varshney
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 732)

Abstract

The dynamic scheduling algorithms are widely used to evaluate the performance of homogeneous and heterogeneous systems in terms of QoS parameters such as scheduling length, execution time, load imbalance factor and many more. Over the time, many dynamic scheduling policies were introduced which are designed to achieve their goal such as efficient utilization of process elements, minimization of resources idleness, or determining the total execution time. In this paper, we analyzed different aspects in dynamic scheduling algorithm and numerous issues in various levels of the homogeneous and heterogeneous systems.

Keywords

Parallel processing Multiprocessor system Static and dynamic scheduling Heterogeneous and homogeneous systems 

References

  1. 1.
    Kwok, Y.K., Ahmad, I.: Static scheduling algorithms for allocating directed task graphs to multiprocessors. ACM Comput. Surv. 31(4), 406–471 (1999)CrossRefGoogle Scholar
  2. 2.
    Singh, K., Alam, M., Sharma, S.K.: A survey of static scheduling algorithm for distributed computing system. Int. J. Comput. Appl. 129(2), 25–30 (2015)CrossRefGoogle Scholar
  3. 3.
    Singh, M.K., Tiwari, R.: A survey on scheduling of parallel program in heterogeneous system. Int. J. Advanced Research in Computer Engineering & Technology. 1(8), 357 (2012)Google Scholar
  4. 4.
    He, Y., Liu, J., Sun, H.: Scheduling functionally heterogeneous systems with utilization balancing. In: IEEE International Parallel and Distributed Processing Symposium, pp. 1187–1198 (2011)Google Scholar
  5. 5.
    Alam, M., Varshney, A.K.: A comparative study of interconnection network. Int. J. Comput. Appl. 127(4), 37–43 (2015)CrossRefGoogle Scholar
  6. 6.
    Choudhury, P.: Online scheduling of dynamic task graphs with communication and contention for multiprocessors. IEEE Trans. Parallel Distrib. Syst. 23(1), 126–133 (2012)CrossRefGoogle Scholar
  7. 7.
    Amalarethinam, D.I.G., Joyce Mary, G.J.: A new DAG based dynamic task scheduling algorithm (DYTAS) for multiprocessor systems. Int. J. Comput. Appl. 19(8), 24–28 (2011)Google Scholar
  8. 8.
    Kaur, P., Kaur, A.: Implementation of Dynamic Level Scheduling Algorithm Using Genetic Operators. Int. J. of Appl. or Innovation in Eng. & Manag. 2(7), 2319–4847 (2013)Google Scholar
  9. 9.
    Khan, Z.A., Siddiqui, J., Samad, A.: Linear crossed cube (LCQ): a new interconnection network topology for massively parallel system. Int. J. Comput. Netw. Inf. Secur. 7(3), 18–25 (2015)Google Scholar
  10. 10.
    Kurt, M.C., Krishnamoorthy, S., Agrawal, K., Agrawal, G.: Fault-tolerant dynamic task graph scheduling. In: SC14: International Conference for High Performance Computing, Networking, Storage and Analysis (2014)Google Scholar
  11. 11.
    Visalakshi, P., Sivanandam, S.N.: Dynamic Task Scheduling with Load Balancing using Hybrid Practical Swarm Optimization. Int. J. Open Problems Compt. Math. 2(3), 475–488 (2009)Google Scholar
  12. 12.
    Khan, Z.A., Siddiqui, J., Samad, A.: A novel multiprocessor architecture for massively parallel system. Int. Conf. Parallel Distrib. Grid Comput. 466–471 (2015)Google Scholar
  13. 13.
    Kwok, Y.K., Ahmad, I.: Static scheduling algorithms for allocating directed task graphs to multiprocessors. ACM Comput. Surv. (CSUR) 31(4) (1999)CrossRefGoogle Scholar
  14. 14.
    Evans, D.J., Butt, W.U.N.: Dynamic load balancing using task-transfer probabilities. Parallel Comput. Elsevier North-Holland 19, 897–916 (1993)CrossRefGoogle Scholar
  15. 15.
    Zomaya, A.Y., Hwei, Y.: Observations on using genetic algorithms for dynamic load-balancing. Parallel Distrib. Syst. IEEE 12(9) (2001)CrossRefGoogle Scholar
  16. 16.
    Munetomo, M., Takai, Y., Sato, Y.: A genetic approach to dynamic load-balancing in a distributed computing system. In: Proceedings of First International Conference on Evolutionary Computation, IEEE World Congress Computational Intelligence, vol. 1, pp. 418–421 (1994)Google Scholar
  17. 17.
    Pico, C.A.G., Wainwright, R.L.: Dynamic scheduling of computer tasks using genetic algorithms. In: Proceedings of First IEEE Conference Evolutionary Computation, IEEE World Congress Computational Intelligence, vol. 2, pp. 829–833 (1994)Google Scholar
  18. 18.
    Kelly, O.R., Aydin, H.: Fixed—priority global scheduling for mixed-critically real-time system. Int. J. Embedded Syst. 6(2/3) (2014)Google Scholar
  19. 19.
    Sun, Y., Lipariy, G., Guanzx, N., Yix, W.: Improving the Response Time Analysis of Global Fixed-Priority Multiprocessor Scheduling. Embedded and Real-Time IEEE Xplore (2014)Google Scholar
  20. 20.
    Davis, R.I., Burns, A.: Improved priority assignment for global fixed priority pre-emptive scheduling in multiprocessor real-time systems. Real-Time Syst. 47(1), 1–40 (2011)CrossRefGoogle Scholar
  21. 21.
    Guan, N., Stigge, M., Yi, W., Yu, G.: New response time bounds for fixed priority multiprocessor scheduling. In: 30th IEEE on Real-Time Systems Symposium, 2009, RTSS 2009, pp. 387–397. IEEE (2009)Google Scholar
  22. 22.
    Guan, N., Stigge, M., Yi, W., Yu, G.: New Response Time Bounds for Fixed Priority Multiprocessor Scheduling. In Real-Time Systems Symposium, RTSS 2009. 387–397 IEEE, (2009)Google Scholar
  23. 23.
    Li, H., Baruah, S.: Load-based schedulability analysis of certifiable mixed-criticality systems. In: Proceedings of the 10th ACM International Conference on Embedded Software (2010)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Department of Computer ScienceAl-Barkaat College of Graduate StudiesAligarhIndia
  2. 2.University of Electronic Science and Technology of ChinaChengduChina
  3. 3.Institute of Technology & ManagementAligarhIndia

Personalised recommendations