MARS: Multi-Agent Deep Reinforcement Learning for Real-Time Workflow Scheduling in Hybrid Clouds with Privacy Protection

Long Cheng*, Haoyang He, Yan Gu, Qingzhi Liu, Zhiming Zhao, Fang Fang

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference paperAcademicpeer-review

Abstract

Scheduling workflows in hybrid cloud environments presents significant challenges due to the inherent complexity of workflows and the dynamic nature of cloud resources. This complexity is further increased when attempting to balance workflow performance with privacy protection. Recent efforts have leveraged deep reinforcement learning (DRL) to address these challenges. However, most of these approaches rely on single-agent models, which can lead to security issues and scalability problems due to their centralized processing. Specifically, the properties of workflows are transferred to the single agent, which risks leaking privacy information. Our paper addresses these issues by introducing MARS, a real-time workflow scheduling method that prioritizes privacy protection in hybrid clouds. MARS leverages multi-agent deep reinforcement learning (MADRL) to optimize the workflow scheduling of cloud virtual machines (VMs). The benefit of our solution is that it relies on the collaborative learning of multi-agents on multiple VMs, which could assign user data to specific cloud servers for privacy protection while sharing training experiences between agents. In our implementation, MARS aims to reduce workflow completion time and operational costs while complying with strict privacy protection guidelines. The experimental results demonstrate that MARS can significantly surpass existing methods, reducing makespan by an average of 53.18% and costs by 61.98% compared to basic techniques, and achieving 20.26% and 25.71% improvements over the latest advanced methods, respectively.

Original languageEnglish
Title of host publicationProceedings - 2024 IEEE 30th International Conference on Parallel and Distributed Systems, ICPADS 2024
PublisherIEEE
Pages657-666
ISBN (Electronic)9798331515966
ISBN (Print)9798331515973
DOIs
Publication statusPublished - 2024
Event30th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2024 - Belgrade, Serbia
Duration: 10 Oct 202414 Oct 2024

Publication series

NameProceedings of the International Conference on Parallel and Distributed Systems - ICPADS
ISSN (Print)1521-9097

Conference/symposium

Conference/symposium30th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2024
Country/TerritorySerbia
CityBelgrade
Period10/10/2414/10/24

Keywords

  • Cloud computing
  • deep reinforcement learning
  • hybrid cloud
  • multi-agent system
  • workflow scheduling

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