Advertisement

Level-Wise Scheduling Algorithm for Linearly Extensible Multiprocessor Systems

  • Abdus Samad
  • Savita Gautam
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 524)

Abstract

The valuable treating of parallelism on an interconnection network entails optimizing inconsistent performance indices, such as the reduction of communication and scheduling overheads and also uniform distribution of load among the nodes. In this kind of a system a number of nodes process the numerous jobs concurrently. A novel dynamic scheduling scheme that supports task unbiased structure approach is proposed for a particular class of multiprocessor networks known as linearly extensible multiprocessor networks. The significance of proposed scheduling scheme is remedying the communication overhead, delay in task execution and efficient processor utilization, which ultimately improves the total execution time. The proposed algorithm is implemented on a set of processors known as nodes which are linked through certain interconnection network. In particular, the performance is evaluated for linear type of multiprocessor architectures. In addition, a comparison is also made by implementing standard scheduling algorithm on same architectures with same number of nodes. The metrics used for comparison are Load Imbalance Factor (LIF), which represents the deviation of load among processors after achieving load balancing and execution time. The comparative simulation study shows that the proposed scheme gives better performance in terms of task scheduling and execution time when implemented on various linearly extensible multiprocessor networks.

Keywords

Linearly extensible network Load imbalance factor Level scheduling algorithm Interconnection network Execution time 

References

  1. 1.
    Birmpilis, S., & Aslanidis, T. (2017). A critical improvement on open shop scheduling algorithm for routing in interconnection networks. International Journal of Computer Networks & Communications (IJCNC), 9(1), 1–19.CrossRefGoogle Scholar
  2. 2.
    Barbosa, G., & Moreira, B. (2011). Dynamic scheduling of batch of parallel task jobs on heterogeneous clusters. Journal of Parallel Computing, 37, 428–438.CrossRefGoogle Scholar
  3. 3.
    Prasad, N., Mukkherjee, P., Chattopadhyay, S., Chakrabarti, I. (2018). Design and evaluation of ZMesh topology for on-chip interconnection networks. Journal of Parallel and Distributed Computing, 17–36.CrossRefGoogle Scholar
  4. 4.
    Samad, A., Rafiq, M. Q., & Farooq, O. (2012). Two round scheduling (TRS) scheme for linearly extensible multiprocessor systems. International Journal of Computer Applications, 38(10), 34–40.CrossRefGoogle Scholar
  5. 5.
    Khan, Z. A., Siddiqui, J., & Samad, A. (2013). Performance analysis of massively parallel architectures. BVICAM’s International Journal of Information Technology (IJIT), 5(1), 563–568.Google Scholar
  6. 6.
    Manullah, (2013). A Δ-based linearly extensible multiprocessor network. International Journal of Computer Science and Information Technology, 4(5), 700–707.Google Scholar
  7. 7.
    Khan, Z. A., Siddiqui, J., & Samad, A. (2016). Properties and performance of cube-based mutiprocessor architectures. International Journal of Applied Evolutionary Computation (IJCNIS), 7(1), 67–82.Google Scholar
  8. 8.
    Khan, Z. A., Siddiqui, J., Samad, A. (2015). Linear Crossed Cube (LCQ): A new interconnection network topology for massively parallel architectures. International Journal of Computer Network and Information Science (IJCNIS), 7(3), 18–25.CrossRefGoogle Scholar
  9. 9.
    Seth, A., & Singh, V. (2016). Types of scheduling in parallel computing. International Research Journal of Engineering and Technology (IRJET), 3(5), 521–526.Google Scholar
  10. 10.
    Mahafzah, B. A., & Jaradat, B. A. (2010). The hybrid dynamic parallel scheduling algorithm for load balancing on chained-cubic tree. The Journal of Supercomputing, 52, 224–252.CrossRefGoogle Scholar
  11. 11.
    Ding, Z., Hoare, R. R., Jones, K. A. (2006). Level-wise scheduling algorithm for fat tree interconnection networks. In Proceedings of the 2006 ACM/IEEE SC|06 Conference (SC’06) (pp. 9–17).Google Scholar
  12. 12.
    Alebrahim, S., & Ahmad, I. (2017). Task scheduling for heterogeneous computing systems. The Journal of Supercomputing, 73(6), 2313–2338.CrossRefGoogle Scholar
  13. 13.
    Abdelkader, D. M., & Omara, F. (2012). Dynamic task scheduling algorithm with load balancing. Egyptian Informatics Journal, 13, 135–145.CrossRefGoogle Scholar
  14. 14.
    Nayak, K., Padhy, S. K., & Panigrahi, S. P. (2012). A novel algorithm for dynamic task scheduling. Future Generation Computer System, 28, 709–717.CrossRefGoogle Scholar
  15. 15.
    Samad, A., Khan, Z. A., & Siddiqui, J. (2016). Optimal dynamic scheduling algorithm for cube based multiprocessor interconnection networks. International Journal of Control Theory and Applications, 9(40), 485–490.Google Scholar
  16. 16.
    Mohammad, S. B., Ababneh, I. (2108). Improving system perfromance in non-contiguous processor allocation for mesh interconnection networks. Journal of Simulation Modeling and Practice. 80, 19–31.Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.University Women’s Polytechnic, F/O Engineering & Technology, Aligarh Muslim UniversityAligarhIndia

Personalised recommendations