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September 12, 2025Open Access

A Novel Artificial Intelligence Based Dynamic Task Scheduling and Load Awareness

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Authors

HAHamed AltalhoniNANoraida Haji AliFYFarizah Yunus

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Overview

This framework enhances dynamic task scheduling and minimizes SLA violations in IoT workloads, suggesting improved energy efficiency.

Key Points

  • The framework achieves a 35% reduction in energy consumption by optimizing task scheduling across fog nodes.
  • Experimental results show up to a 40% improvement in throughput, enhancing overall performance in fog environments.
  • Using Deep Q-Networks for non-resource-intensive tasks optimizes latency while maintaining compliance with service agreements.
  • The Boosted Binary Owl Optimization algorithm focuses on optimal VM selection for efficient task forwarding and load awareness.

Cite This Study

Altalhoni et al. (2025) studied this question.

synapsesocial.com/papers/68d44f7331b076d99fa56b48https://doi.org/10.21203/rs.3.rs-7465771/v1
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