解决边缘计算中资源分配与任务卸载的协同优化问题。
Joint Resource Optimization, Computation Offloading and Resource Slicing for Multi-Edge Traffic-Cognitive Networks
- 构建斯塔克尔伯格博弈模型，协调平台与边缘服务器利益
- 中心化算法提升收益，去中心化方案保护隐私且性能优越
- 适合研究边缘计算资源调度与激励机制的开发者
边缘计算正发展为应用提供商与边缘服务器（ESs）之间的动态中介平台，任务卸载与计算服务支付紧密耦合。为实现高效资源利用并满足严格服务质量（QoS）要求，需在优化平台运营目标的同时激励边缘服务器。本文研究一个多方参与的自利主体系统，联合优化收益最大化、资源分配与任务卸载。提出基于斯塔克尔伯格博弈的框架，采用贝叶斯优化的集中式算法求解；针对信息收集中的隐私挑战，进一步设计基于神经网络优化与隐私保护信息交换协议的分布式方案。大量数值实验表明，所提机制相较现有基线显著提升性能。
原文摘要 · Abstract (English)
The evolving landscape of edge computing envisions platforms operating as dynamic intermediaries between application providers and edge servers (ESs), where task offloading is coupled with payments for computational services. Ensuring efficient resource utilization and meeting stringent Quality of Service (QoS) requirements necessitates incentivizing ESs while optimizing the platforms operational objectives. This paper investigates a multi-agent system where both the platform and ESs are self-interested entities, addressing the joint optimization of revenue maximization, resource allocation, and task offloading. We propose a novel Stackelberg game-based framework to model interactions between stakeholders and solve the optimization problem using a Bayesian Optimization-based centralized algorithm. Recognizing practical challenges in information collection due to privacy concerns, we further design a decentralized solution leveraging neural network optimization and a privacy-preserving information exchange protocol. Extensive numerical evaluations demonstrate the effectiveness of the proposed mechanisms in achieving superior performance compared to existing baselines.
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