# Reinforcement Learning-driven Data-intensive Workflow Scheduling for Volunteer Edge-Cloud

arXiv: https://arxiv.org/abs/2407.01428 · HTML: https://mlsanity.com/paper/2407.01428/

## 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.

## Placed in

- Deep reinforcement learning methods / [RL for energy, networks and operations](https://mlsanity.com/deeprl/rl-for-energy-networks-and-operations/)

## Builds on (most cited first, 1 of 1)

- [Asynchronous Methods for Deep Reinforcement Learning](https://mlsanity.com/paper/1602.01783/) (2016)

## Built on by (most cited first, 5 of 5)

- [Deep Reinforcement Learning for Job Scheduling and Resource Management in Cloud Computing: An Algorithm-Level Review](https://mlsanity.com/paper/2501.01007/) (2025)
- [EdgeRL: Reinforcement Learning-driven Deep Learning Model Inference Optimization at Edge](https://mlsanity.com/paper/2410.12221/) (2024)
- [Reinforcement Learning-Driven Edge Management for Reliable Multi-view 3D Reconstruction](https://mlsanity.com/paper/2510.08839/) (2025)
- [Deep Reinforcement Learning-driven Edge Offloading for Latency-constrained XR pipelines](https://mlsanity.com/paper/2603.16823/) (2026)
- [Noise-Aware Misclassification Attack Detection in Collaborative DNN Inference](https://mlsanity.com/paper/2603.17914/) (2026)

## 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.