Abstract

By spreading out computing workloads over multiple levels, the Edge-to-Cloud continuum paradigm improves the performance of applications that are sensitive to latency. However, real-time scheduling is difficult on the Edge computing layer since it consists of a variety of nodes with varying uptime. In this paper, we address this issue by proposing an online and adaptive scheduling algorithm based on a continuous learning Reinforcement Learning. Our algorithm determines each work request individually, optimizing scheduling policies to meet realtime application requirements while taking environmental energy and battery limits into account. We validate the efficacy of our approach in dynamically assigning tasks, particularly in scenarios where Edge nodes exhibit variable speeds and unpredictable failures, while efficiently managing energy resources and battery constraints through extensive simulations and comparisons with static scheduling strategies.

Citation

Panceri, A., Proietti Mattia, G., & Beraldi, R. (2024). Real-time and Energy-aware Scheduling for Edge-to-Cloud Continuum based on Reinforcement Learning. 2024 28th International Symposium on Distributed Simulation and Real Time Applications (DS-RT), 166–173. https://doi.org/10.1109/DS-RT62209.2024.00038

@inproceedings{2024PanceriRealTime,
  author = {Panceri, Andrea and {Proietti Mattia}, Gabriele and Beraldi, Roberto},
  booktitle = {2024 28th International Symposium on Distributed Simulation and Real Time Applications (DS-RT)},
  title = {Real-time and Energy-aware Scheduling for Edge-to-Cloud Continuum based on Reinforcement Learning},
  year = {2024},
  volume = {},
  number = {},
  pages = {166-173},
  keywords = {Processor scheduling;Heuristic algorithms;Computational modeling;Reinforcement learning;Quality of service;Dynamic scheduling;Real-time systems;Batteries;Testing;Load modeling;n/a},
  doi = {10.1109/DS-RT62209.2024.00038}
}