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A Smart Algorithm for Dynamic Task Allocation for Distributed Processing Environment

by Dr. Kapil Govil
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 28 - Number 2
Year of Publication: 2011
Authors: Dr. Kapil Govil
10.5120/3362-4641

Dr. Kapil Govil . A Smart Algorithm for Dynamic Task Allocation for Distributed Processing Environment. International Journal of Computer Applications. 28, 2 ( August 2011), 13-19. DOI=10.5120/3362-4641

@article{ 10.5120/3362-4641,
author = { Dr. Kapil Govil },
title = { A Smart Algorithm for Dynamic Task Allocation for Distributed Processing Environment },
journal = { International Journal of Computer Applications },
issue_date = { August 2011 },
volume = { 28 },
number = { 2 },
month = { August },
year = { 2011 },
issn = { 0975-8887 },
pages = { 13-19 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume28/number2/3362-4641/ },
doi = { 10.5120/3362-4641 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:13:42.136518+05:30
%A Dr. Kapil Govil
%T A Smart Algorithm for Dynamic Task Allocation for Distributed Processing Environment
%J International Journal of Computer Applications
%@ 0975-8887
%V 28
%N 2
%P 13-19
%D 2011
%I Foundation of Computer Science (FCS), NY, USA
Abstract

A Distributed Processing Environment (DPE) consists of multiple autonomous computers that communicate through a communication media. In DPE a task is divided into many fractions and each of which is to be get processed. The task allocation problem can be explained in terms of number of tasks and number of processors available. In the present method propose a dynamic model for task allocation in DPE. Present method describes the allocation of m tasks in the environment of distributed processing with n processors (m>n) that completes in k number of phases. This method allocates the tasks to the processor to increases the performance of the DPE; and based on the inter task communication cost between executing task and another tasks. Residing cost and reallocation cost in various phases has also taken in consideration. On implemented the suggested algorithm we have obtained the phase wise optimal allocation and overall optimal cost. The run time complexity has been computed and compared with existing approaches. It is found that suggested algorithm is much better as compared to others.

References
  1. Dr. Kapil Govil and Dr. Avanish Kumar. 2011. A modified and efficient algorithm for Static task assignment in Distributed Processing Environment. International Journal of Computer Applications, Vol. 23, Number 8, Article 1, 1 – 5, ISBN: 978-93-80752-82-3, ISSN: 0975 – 8887.
  2. Kok Fu Ng, Norhashidah Hj. Mohd Ali. 2008. Performance analysis of explicit group parallel algorithms for distributed memory multicomputer. Elsevier Inc. Vol. 34, Issue 6,7,8. 427 – 440.
  3. Kumar, V. Singh, M. P. and Yadav, P.K. 1995. An Efficient Algorithm for Allocating Tasks to Processors in a Distributed System, In proceedings of the 19th National system conference, SSI, Coimbatore, 82 – 87.
  4. Kumar, V. Singh, M.P. and Yadav, P.K. 1995. A Fast Algorithm for Allocating Tasks in Distributed Processing System, In proceedings of the 30th Annual Convention of CSI, Hyderabad, 347 – 358.
  5. Richard R. Y., Lee, E. Y. S. and Tsuchiya, M. 1982. A Task Allocation Model for Distributed Computer System, IEEE Transactions on Computer, Vol. 31, 41 – 47.
  6. J. Sum, J. Wu, and C. S. Leung. 2007. On profit density based greedy algorithm for a resource allocation problem in web services. International Journal of Computers and Applications.
  7. Suresh Behara, Sanjay Mittal. 2009. Parallel finite element computation of incompressible flows. Elsevier Inc. Vol. 35, Issue 4, 195 – 212.
  8. Ucar, Bora, Aykanat, Cevdet, Kaya, Kamer and Ikinci, Murat. 2005. Task assignment in heterogeneous computing systems. Journal of Parallel and Distributed Computing, Elsevier Inc., Vol. 66, Issue 1, 32 – 46.
  9. Wei – Ming Lin. 2008. Performance modeling and analysis of correlated parallel computations. Elsevier Inc. Vol. 34, Issue 9, 521 – 538.
  10. N. Beaumont. 2009. Using dynamic programming to determine an optimal strategy in a contract bridge tournament. Journal of the Operational Research Society.
  11. Kumar, Avanish, 1999. Optimizing for the Dynamic Task Allocation, in proceedings of the ‘III Conference of the International Academy of Physical Sciences, 1999 Allahabad, 281 – 294.
  12. Palmer, J. and Mitrani, I. 2005. Optimal and heuristic policies for dynamic server allocation. Journal of Parallel and Distributed Computing, Vol. 65, Issue 10, 1204 – 1211.
  13. M. Vanneschi, L. Veraldi. 2007. Dynamicity in distributed applications: issues, problems and the ASSIST approach. Elsevier Inc. Vol. 33, Issue 12, 822 – 845.
  14. Pradeep Kumar Yadav, M. P. Singh and Harendra Kumar. 2008. Scheduling Algorithm: Tasks scheduling Algorithm for Multiple Processors with Dynamic Reassignment. Journal of Computer Systems, Networks and Communications, Vol. 2008, Article ID 578180, 9 pages.
  15. C Alves and J M Valerio de Carvalho. 2008. New integer programming formulations and an exact algorithm for the ordered cutting stock problem. Journal of the Operational Research Society. Vol. 59, 1520 – 1531.
  16. Baz D. El, and Elkihel M. 2005. Load balancing methods and parallel dynamic programming algorithm using dominance technique applied to the 0 – 1 knapsack problem, Elsevier Inc., Vol. 65, Issue 1, 74 – 84.
  17. Iqbal, Saeed and Carey, Graham F. 2005. Performance analysis of dynamic load balancing algorithms with variable number of processors. Journal of Parallel and Distributed Computing, Elsevier Inc., Vol. 65, Issue 8, 934 – 948.
  18. Bahi, Jacques, Couturier, Raphael and Vernier, Flavien. 2005. Synchronous distributed load balancing on dynamic networks, Journal of Parallel and Distributed Computing, Elsevier Inc., Vol. 65, Issue 11, 1397 – 1405.
  19. Jan, Gene Eu and Lin, Ming – Bo. 2005. Concentration, load balancing, partial permutation routing, and super concentration on cube – connected cycles parallel computers. Journal of Parallel and Distributed Computing, Elsevier Inc., Vol. 65, Issue 12, 1471 – 1482.
  20. C. Muller, M. Strengert, T. Ertl. 2007. Adaptive load balancing for raycasting of non-uniformly bricked volumes. Elsevier Inc. Vol. 33, Issue 6, 406 – 419.
  21. Wong, Han Min, Bharadwaj, Veeravalli and Gerassimos, Barlas. 2005. Design and performance evaluation of load distribution strategies for multiple divisible loads on heterogeneous linear daisy chain networks, Elsevier Inc., Vol.65, No.12, 1558-1577.
  22. Zeng, Zeng and Bharadwaj, Veeravalli. 2006. Distributed scheduling strategy for divisible loads on arbitrarily configured distributed networks using load balancing via virtual routing. Journal of Parallel and Distributed Computing, Elsevier Inc. Vol. 66, Issue 11, 1404 – 1418.
  23. Grosu, Daniel and Chronopoulos, Anthony T. 2005. Noncooperative load balancing in distributed systems. Journal of Parallel and Distributed Computing, Elsevier Inc., Vol. 65, Issue 9, 1022 – 1034.
  24. Yeon – Koo Che, Kathryn E. Spier. 2007. Exploiting Plaintiffs Through Settlement: Divide and Conquer. Journal of Institutional and Theoretical Economics (JITE), Vol. 164, Issue I, 4 – 23.
  25. Yi – mu Ji and Ru – chuan Wang. 2006. A Solution of Grid Computing Flow Using MDA Methodology. The Journal of China Universities of Posts and Telecommunications. Vol. 13, Issue 1, 29 – 33.
  26. Giovanni Righini. 2008. A branch – and – bound algorithm for the linear ordering problem with cumulative costs. European Journal of Operational Research Vol. 186, Issue 3, 965 – 971.
  27. Yanai Shuzo, Fujie Tetsuya. 2005. An Improved Branch – and – Bound Algorithm for a Two-machine Flowshop Problem with Minimum Makespan. Journal of Japan Industrial Management Association. Vol. 56, Issue 4, 284 – 293.
Index Terms

Computer Science
Information Sciences

Keywords

Distributed Processing Environment Task Allocation Residing Cost Reallocation Cost

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