It is suggested that neuromorphic, event-driven decision mechanisms offer an effective and scalable alternative for real-time orchestration in dynamic and resource-constrained edge-IoT environments, particularly under congestion-prone conditions anticipated in next-generation large-scale deployments.
MPL-schedule is proposed, a multi-preemptive, priority-aware scheduling scheme that dynamically coordinates heterogeneous SNN tasks on GPUs that improves throughput by up to 15.0% and energy efficiency by up to 20.3%, while sustaining over 95.8% GPU utilization.
Xiaofang Zhao, Xin Du, Di Yu et al.· Science China Information Sc...· 0 citations
The key idea is to exploit the latency budget of tasks to control their execution timing, enabling a more balanced distribution of workload over time and reducing peak congestion across the continuum.
Keyvan Aghababaiyan, B. Coll-Perales, Javier Gozálvez· 0 citations
Fog computing brings computations closer to edge devices, which reduces the latency and energy consumption of tasks. However, when operating in a fog environment, task offloading decisions are exacerbated by the dynamic nature of network conditions and the diversity of available resources. In this paper, we propose an...
Gayathri Tippani, Odapalli Keerthana, Tadivaka Hasmita et al.· International Conference on...· 0 citations
LEAOT is proposed, a lightweight load- and deadline-aware task offloading method for IoT edge–cloud systems that reduces deadline violation to 0.69% and maintains low energy under the reference setting, while remaining competitive with delay-resource and drift-plus-penalty baselines.
Ayman Noor· International Journal of Adv...· 0 citations
These findings validate that embedding hierarchical micro-cooperative intelligence directly inside the communication mesh significantly enhances adaptability without increasing computational load.
A. Shenbagarajan, G. Shenbagalakshmi· International Journal of Com...· 0 citations
A Variational Mode Decomposition-Convolutional Neural Network-Attention-Bidirectional Long Short-Term Memory-CNN-Attention-BiLSTM model is constructed to filter environmental noise and accurately capture the spatio-temporal features of bursty traffic.
Shizhen Bai, Rong-Hua Chen, Yongbo Tan et al.· Applied Sciences· 0 citations
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