Reinforcement Learning-driven Data-intensive Workflow Scheduling for Volunteer Edge-Cloud
On the map: Deep reinforcement learning methods → RL for energy, networks and operations
Abstract (arXiv)
In recent times, Volunteer Edge-Cloud (VEC) has gained traction as a cost-effective, community computing paradigm to support data-intensive scientific workflows. However, due to the highly distributed and heterogeneous nature of VEC resources, centralized workflow task scheduling remains a challenge. In this paper, we propose a Reinforcement Learning (RL)-driven data-intensive scientific workflow scheduling approach that takes into consideration: i) workflow requirements, ii) VEC resources' preference on workflows, and iii) diverse VEC resource policies, to ensure robust resource allocation. We formulate the long-term average performance optimization problem as a Markov Decision Process, which is solved using an event-based Asynchronous Advantage Actor-Critic RL approach. Our extensive simulations and testbed implementations demonstrate our approach's benefits over popular baseline strategies in terms of workflow requirement satisfaction, VEC preference satisfaction, and available VEC resource utilization.
Key facts. Cited by 5 papers 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 1 of its 32 references that are in the map. First posted on arXiv 1 Jul 2024.
Builds on
1 of 32 references.
References · 1
- 2016Asynchronous Methods for Deep Reinforcement Learningcited by 2,258
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Citing papers per year: 2024: 1; 2025: 2; 2026: 2.
5 papers.
- 2025Deep Reinforcement Learning for Job Scheduling and Resource Management in Cloud Computing: An Algorithm-Level Reviewcited by 4
- 2024EdgeRL: Reinforcement Learning-driven Deep Learning Model Inference Optimization at Edgecited by 2
- 2025Reinforcement Learning-Driven Edge Management for Reliable Multi-view 3D Reconstructioncited by 0
- 2026Deep Reinforcement Learning-driven Edge Offloading for Latency-constrained XR pipelinescited by 0
- 2026Noise-Aware Misclassification Attack Detection in Collaborative DNN Inferencecited by 0
- 2026Noise-Aware Misclassification Attack Detection in Collaborative DNN Inference18 Mar 2026
- 2026Deep Reinforcement Learning-driven Edge Offloading for Latency-constrained XR pipelines17 Mar 2026
- 2025Reinforcement Learning-Driven Edge Management for Reliable Multi-view 3D Reconstruction9 Oct 2025
- 2025Deep Reinforcement Learning for Job Scheduling and Resource Management in Cloud Computing: An Algorithm-Level Review2 Jan 2025
- 2024EdgeRL: Reinforcement Learning-driven Deep Learning Model Inference Optimization at Edge16 Oct 2024
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