Secure Resource Allocation via Constrained Deep Reinforcement Learning

2025-01-20 · Jianfei Sun, Qiang Gao, Cong Wu, Yuxian Li, Jiacheng Wang, Dusit Niyato

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.

📄 PDF Abstract BibTeX arXiv:2501.11557

Code (0)

등록된 구현이 없습니다.

Tasks

Deep Reinforcement LearningDistributed ComputingEdge-computingreinforcement-learning Reinforcement Learning

Secure Deep Reinforcement Learning for Dynamic Resource Allocation in Wireless MEC Networks

2023-12-13 · Xin Hao, Phee Lep Yeoh, Changyang She, Branka Vucetic 외

This paper proposes a blockchain-secured deep reinforcement learning (BC-DRL) optimization framework for {data management and} resource allocation in decentralized {wireless mobile edge computing (MEC)} networks. In our …

Deep Reinforcement Learning Edge-computing Management

CLARA: A Constrained Reinforcement Learning Based Resource Allocation Framework for Network Slicing

2021-11-16 · Yongshuai Liu, Jiaxin Ding, Zhi-Li Zhang, Xin Liu

As mobile networks proliferate, we are experiencing a strong diversification of services, which requires greater flexibility from the existing network. Network slicing is proposed as a promising solution for resource uti…

Management reinforcement-learning Reinforcement Learning (RL)

Modelling resource allocation in uncertain system environment through deep reinforcement learning

2021-06-17 · Neel Gandhi, Shakti Mishra

Reinforcement Learning has applications in field of mechatronics, robotics, and other resource-constrained control system. Problem of resource allocation is primarily solved using traditional predefined techniques and mo…

Deep Reinforcement Learning reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

T-s3ra: traffic-aware scheduling for secure slicing and resource allocation in sdn/nfv enabled 5g networks

2021-07-11 · Ali J. Ramadhan

Network slicing and resource allocation play pivotal roles in software-defined network (SDN)/network function virtualization (NFV)-assisted 5G networks. In 5G communications, the traffic rate is high, necessitating high …

Scheduling

Situational-Constrained Sequential Resources Allocation via Reinforcement Learning

2025-06-17 · Libo Zhang, Yang Chen, Toru Takisaka, Kaiqi Zhao 외

Sequential Resource Allocation with situational constraints presents a significant challenge in real-world applications, where resource demands and priorities are context-dependent. This paper introduces a novel framewor…

Decision Making reinforcement-learning Reinforcement Learning