Security computing resource allocation based on deep reinforcement learning in serverless multi-cloud edge computing

Handling computationally intensive tasks is challenging for user devices (UDs) with limited computing resources. Serverless cloud edge computing solves this problem and reduces maintenance and management. Its crucial function is to allocate computing resources reasonably. However, linking multiple computing resource nodes to perform computing resource allocation and ensure data security is a significant challenge. This study proposes an approach based on action-constrained deep reinforcement learning (DRL) to allocate computing resources securely. First, we consider a model of a serverless multi-cloud edge computing network with multiple computing resource nodes that possess various attribute characteristics. Then, we design a security mechanism to guarantee data security. Afterward, we formalize the network model and objectives and further transform them into a modeling process known as the Markov decision process. Finally, we propose DRL based on action constraints to provide an optimal resource allocation scheduling policy. Simulation results demonstrate that our approach can reduce system costs and improve working performance compared with the comparison schemes.