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

TL;DR

This paper introduces a reinforcement learning-based scheduling method for data-intensive workflows in Volunteer Edge-Cloud environments, optimizing resource allocation amidst heterogeneity and distribution.

Contribution

It presents a novel RL-driven scheduling framework that considers workflow needs, resource preferences, and policies, formulated as a Markov Decision Process for robust optimization.

Findings

Outperforms baseline strategies in workflow requirement satisfaction.

Achieves higher resource utilization in simulations and testbeds.

Enhances robustness and adaptability of scheduling in VEC environments.

Abstract

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.