Secure Resource Allocation via Constrained Deep Reinforcement Learning

TL;DR

This paper introduces SARMTO, a constrained deep reinforcement learning framework for secure and efficient resource allocation in multi-cloud edge computing, significantly reducing costs and improving energy efficiency.

Contribution

The paper presents a novel DRL-based framework that effectively manages resources, security, and performance constraints in complex distributed computing environments.

Findings

Achieves up to 40% reduction in system costs

Improves energy efficiency by 41.5%

Outperforms five baseline approaches

Abstract

The proliferation of Internet of Things (IoT) devices and the advent of 6G technologies have introduced computationally intensive tasks that often surpass the processing capabilities of user devices. Efficient and secure resource allocation in serverless multi-cloud edge computing environments is essential for supporting these demands and advancing distributed computing. However, existing solutions frequently struggle with the complexity of multi-cloud infrastructures, robust security integration, and effective application of traditional deep reinforcement learning (DRL) techniques under system constraints. To address these challenges, we present SARMTO, a novel framework that integrates an action-constrained DRL model. SARMTO dynamically balances resource allocation, task offloading, security, and performance by utilizing a Markov decision process formulation, an adaptive security mechanism, and sophisticated optimization techniques. Extensive simulations across varying scenarios, including different task loads, data sizes, and MEC capacities, show that SARMTO consistently outperforms five baseline approaches, achieving up to a 40% reduction in system costs and a 41.5% improvement in energy efficiency over state-of-the-art methods. These enhancements highlight SARMTO's potential to revolutionize resource management in intricate distributed computing environments, opening the door to more efficient and secure IoT and edge computing applications.