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Task scheduling optimisation for wireless sensor modules in cloud-based systems
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DOI：10.1504/IJIPT.2026.152211.png)
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Abstract
En 中文
Scheduling algorithms demonstrate the critical function they play in the cloud data centres in determining a potential time table for the work. Since the goal is to achieve the shortest total execution time, existing research has demonstrated that the job scheduling issue is NP-complete. We address the problem of minimising total layover time by scheduling a set of l jobs across a set of |G| groups and m cloud resources. Here, we describe a pair-based work scheduling method based on the well-known optimisation process known as the Hungarian algorithm for cloud data centres. Two performance indicators, specifically makespan and averaged cloud utilisation, are used to assess their performances via simulation. The method will decrease task waiting times and increase resource efficiency as a consequence. The results of the experiment demonstrate that the suggested technique enhances resource consumption while decreasing task execution times.
Keywords:
task management
Hungarian method
normalisation
cloud computing
makespan cloud use
node weight method
Hopfield neural network
Journal
I
IF:
0.2
Papers:
7
Citations:
53
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