A Model-Free Scheduling Method for Heterogeneous Electric Vehicle Cryptographic Task Processing Architecture

Authors:
Publisher website
Google Scholar
Abstract
In recent years, Electric Vehicles (EVs) have be-come widely used due to their environmental friendliness. How-ever, the increase in essential supporting Electric Vehicle Supply Equipments (EVSEs) has led to a large and diverse cryptograph-ic demand for charging aggregators. The growing demand for computational power cannot be met by local cryptographic com-puting clusters, while cryptographic computing services on the cloud bring risks such as cross-VM (cross-Virtual-Machine) at-tacks. Meeting the requirements of security and timeliness simul-taneously poses challenges for cryptographic service architecture and scheduling algorithms. To address these challenges, a heter-ogeneous task processing architecture is proposed to integrate local and cloud computing resources. Next, a task queue is con-structed to transform the dynamic scheduling problem of multi-ple cryptographic tasks into a queue-jumping sequential deci-sion-making problem. By modeling security requirements as con-straints, the problem is modeled as a Constrained Markov Deci-sion Process (CMDP) formulation. A Deep Reinforcement Learn-ing (DRL) method with recurrent networks and a safe action mechanism are proposed to solve the formulation, which utilizes the temporal characteristics of cryptographic tasks to achieve better scheduling performance under constraints. Finally, com-pared with the state-of-the-art method, our proposed approach is validated through simulation experiments, demonstrating supe-rior security and enhanced processing efficiency across various scenarios.
Funding Information
  • National Key Research and Development Program of China (2022YFB2403900)

This publication has 25 references indexed in Scilit: