What type of study is this?

This is a Experimental Study study (also classified as: Quantitative Study).

September 16, 2025SensorsOpen Access

Priority-Aware Multi-Objective Task Scheduling in Fog Computing Using Simulated Annealing

View Full Paper

Ask AI

Bookmark

Authors

This approach optimizes multiple objectives in fog computing, reducing lateness and energy use, while handling prioritized tasks.

The proposed scheduling method significantly reduces makespan and energy consumption while improving task responsiveness.
Experimental analyses indicate that the algorithm outperforms other scheduling methods like ACO and PSO across varying task volumes.
This multi-objective scheduling approach addresses the NP-hard problem in resource-limited fog environments effectively.
Implementing the simulated annealing algorithm enhances optimization, adapting configurations through an initially sub-optimal search.

Mangalampalli et al. (2025) studied this question.

View Full Paper

Ask AI

Bookmark

September 16, 2025SensorsOpen Access

View Full Paper

Ask AI

Bookmark

Authors

This approach optimizes multiple objectives in fog computing, reducing lateness and energy use, while handling prioritized tasks.

The proposed scheduling method significantly reduces makespan and energy consumption while improving task responsiveness.
Experimental analyses indicate that the algorithm outperforms other scheduling methods like ACO and PSO across varying task volumes.
This multi-objective scheduling approach addresses the NP-hard problem in resource-limited fog environments effectively.
Implementing the simulated annealing algorithm enhances optimization, adapting configurations through an initially sub-optimal search.

Mangalampalli et al. (2025) studied this question.

View Full Paper

Ask AI

Bookmark