Abstract/Summary

Hybrid clouds offer more capacity and flexibility than public or private clouds alone, making them popular in business settings. However, they also complicate the scheduling of workflow applications due to their complex computing resources. Furthermore, significant concerns about exposing sensitive data on public clouds and securing data transmission add additional challenges to the scheduling process. Although various workflow scheduling strategies for hybrid clouds that address privacy and security concerns have been proposed, they have inherent limitations. Many of these methods, including heuristic and metaheuristic approaches, are tailored for batch processing and are not suitable for real-time scenarios where workflows can arrive unpredictably. Additionally, they often prioritize security over reducing execution time and costs, compromising overall efficiency. To tackle these challenges, we introduce a scheduling system in Hybrid clOuds that considers Privacy and Security constraints, called HOPS. HOPS utilizes Deep Reinforcement Learning to dynamically assign workflows to virtual machines in real-time, and aims to minimize both makespan and operational costs while adhering to privacy and security standards. We provide a comprehensive overview of the design of HOPS, and our experimental results clearly show its advantages over existing methods in terms of makespan, cost efficiency, and the success rate, with privacy and security compliance.