Original scientific paper
https://doi.org/10.1080/00051144.2023.2288484
Effective task scheduling based on interactive autodidactic school algorithm for cloud computing
G. Senthilkumar
; Department of Computer Science and Engineering, Panimalar Engineering College, Chennai, India
*
B. Suvarnamukhi
; Department of CSE, Neil Gogte Institute of Technology, Hyderabad, India
S. Lekashri
; Department of ECE, Kings Engineering College, Sriperumbudur, India
M. Mohammed Thaha
; Department of Computer Science and Engineering, B.S. Abdur Rahman Crescent Institute of Science and Technology, Vandalur, India
* Corresponding author.
Abstract
The topic of load balanced task scheduling has emerged as a prominent and intricate area of
study within the realm of Cloud computing. Swarm intelligence-based meta-heuristic algorithms
are commonly considered more suitable for the purposes of Cloud scheduling and load balancing. These algorithms employ a combination of local and global search strategies in order
to ascertain the ideal location. To achieve an optimal mapping strategy for task allocation to
resources, it is imperative to find a suitable equilibrium between local and global search techniques, since this approach has demonstrated significant efficacy. This research introduces a new
approach to task scheduling using the Autodidactic Interactive School Optimization Algorithm
(IASOA). The objective of this method is to decrease the time required for job execution while
also enhancing throughput. The assessment of the suggested methodology has been executed,
and a comparative analysis has been performed with five established algorithms in relation to
makespan and throughput. The tests were subsequently extended to encompass a comparative
analysis of the suggested methodology alongside four other established meta-heuristic scheduling methodologies. The study of the simulated experimentation reveals that the proposed
approach yielded noteworthy advantages in makespan and throughput, with improvements of
up to 10% and 60% respectively.
Keywords
Task scheduling; Makespan; cloud computing; optimization algorithm
Hrčak ID:
322958
URI
Publication date:
10.12.2023.
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