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Task scheduling using probabilistic ant colony heuristics
Joint Authors
Srikanth, Umarani
Maheswari, Uma
Palaniswami, Shanthi
Siromoney, Arul
Source
The International Arab Journal of Information Technology
Issue
Vol. 13, Issue 4 (31 Jul. 2016)5 p.
Publisher
Publication Date
2016-07-31
Country of Publication
Jordan
No. of Pages
5
Main Subjects
Information Technology and Computer Science
Abstract EN
The problem of determining whether a set of tasks can be assigned to a set of heterogeneous processors in general is NP-hard.
Generating an efficient schedule of tasks for a given application is critical for achieving high performance in a heterogeneous computing environment.
This paper presents a novel algorithm based on Ant Colony Optimization (ACO) for the scheduling problem.
An attempt is made to arrive at a feasible schedule for a task set on heterogeneous processors ensuring fair load balancing across the processors within a reasonable amount of time.
Three parameters: Average waiting time of tasks, utilization of individual processors and the scheduling time of tasks are computed.
The results are compared with those of the First Come First Served (FCFS) algorithm and it is found that ACO performs better than FCFS with respect to the waiting time and individual processor utilization.
On comparison with the FCFS approach, the ACO method balances the load fairly among the different processors with the standard deviation of processors utilization being 88.7 % less than that of FCFS.
The average waiting time of the tasks is also found to be 34.3 % less than that of the FCFS algorithm.
However, there is a 35.5 % increase in the scheduling time for the ACO algorithm.
American Psychological Association (APA)
Srikanth, Umarani& Maheswari, Uma& Palaniswami, Shanthi& Siromoney, Arul. 2016. Task scheduling using probabilistic ant colony heuristics. The International Arab Journal of Information Technology،Vol. 13, no. 4.
https://search.emarefa.net/detail/BIM-654996
Modern Language Association (MLA)
Srikanth, Umarani…[et al.]. Task scheduling using probabilistic ant colony heuristics. The International Arab Journal of Information Technology Vol. 13, no. 4 (Jul. 2016).
https://search.emarefa.net/detail/BIM-654996
American Medical Association (AMA)
Srikanth, Umarani& Maheswari, Uma& Palaniswami, Shanthi& Siromoney, Arul. Task scheduling using probabilistic ant colony heuristics. The International Arab Journal of Information Technology. 2016. Vol. 13, no. 4.
https://search.emarefa.net/detail/BIM-654996
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-654996