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.

我们提出一种基于强化学习的数据密集型科学工作流调度方法，考虑到 Volunteer Edge-Cloud 资源的分布和异构性，以确保鲁棒的资源分配。通过将问题建模为马尔可夫决策过程，并使用基于事件的异步优势演员 - 评论家强化学习方法进行求解，我们在大量模拟和实验中证明了我们的方法在满足工作流需求、满足 Volunteer Edge-Cloud 资源偏好以及有效利用资源方面的优势。