mlsanityData to 14 Oct 2026

Energy-Efficient Computation with DVFS using Deep Reinforcement Learning for Multi-Task Systems in Edge Computing

arXiv 2409.19434 ↗First posted 28 Sep 2024cs.OS cs.LGcited by 1

On the map: Deep reinforcement learning methods → RL for energy, networks and operations

Abstract (arXiv)

Finding an optimal energy-efficient policy that is adaptable to underlying edge devices while meeting deadlines for tasks has always been challenging. This research studies generalized systems with multi-task, multi-deadline scenarios with reinforcement learning-based DVFS for energy saving for periodic soft real-time applications on edge devices. This work addresses the limitation of previous work that models a periodic system as a single task and single-deadline scenario, which is too simplified to cope with complex situations. The method encodes time series data in the Linux kernel into information that is easy to interpret for reinforcement learning, allowing the system to generate DVFS policies to adapt system patterns based on the general workload. For encoding, we present two different methods for comparison. Both methods use only one performance counter: system utilization, and the kernel only needs minimal information from the userspace. Our method is implemented on Jetson Nano Board (2GB) and is tested with three fixed multitask workloads, which are three, five, and eight tasks in the workload, respectively. For randomness and generalization, we also designed a random workload generator to build different multitask workloads to test. Based on the test results, our method could save 3%-10% power compared to Linux built-in governors.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods. Builds on 5 of its 46 references that are in the map. First posted on arXiv 28 Sep 2024.

Builds on

5 of 46 references.

References · 5

  1. 2015Deep Reinforcement Learning with Double Q-learningcited by 1,745
  2. 2021DeepMTL Pro: Deep Learning Based MultipleTransmitter Localization and Power Estimationcited by 10
  3. 2024MalleTrain: Deep Neural Network Training on Unfillable Supercomputer Nodescited by 5
  4. 2022InfiniStore: Elastic Serverless Cloud Storagecited by 2
  5. 2023CPU frequency scheduling of real-time applications on embedded devices with temporal encoding-based deep reinforcement learningcited by 1

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Sustainable Edge Vision via Empirically Calibrated DVFS: Eliminating Thermal Throttling on Passively Cooled Hardwarecited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2023Multi-objective Deep Reinforcement Learning for Mobile Edge Computing
  2. 2025Metadata-Guided Adaptable Frequency Scaling across Heterogeneous Applications and Devices
  3. 2025A Novel Deep Reinforcement Learning Method for Computation Offloading in Multi-User Mobile Edge Computing with Decentralization
  4. 2026ZeroDVFS: Zero-Shot LLM-Guided Core and Frequency Allocation for Embedded Platforms
  5. 2026Decentralized Task Scheduling in Distributed Systems: A Deep Reinforcement Learning Approach
  6. 2025Cooperative Task Offloading through Asynchronous Deep Reinforcement Learning in Mobile Edge Computing for Future Networks
  7. 2025Research on Edge Computing and Cloud Collaborative Resource Scheduling Optimization Based on Deep Reinforcement Learning
  8. 2025Generalizable Pareto-Optimal Offloading with Reinforcement Learning in Mobile Edge Computing