Focal Interest
Focal Interest

Edge computing

  1. Adaptive CPU frequency scaling for energy-efficient and sustainable edge computing under renewable energy uncertainty
    Deep reinforcement learning improves CPU frequency scaling for edge computing systems powered by renewable energy, reducing prediction error by 35% and optimizing the energy-latency tradeoff.
  2. Hybrid deep learning improved edge-cloud task scheduling in simulation
    Deep reinforcement learning framework for adaptive task scheduling in edge-cloud computing with improved SLA compliance, reduced operational costs, and lower task rejection rates.