Joint Resource Optimization, Computation Offloading and Resource Slicing for Multi-Edge Traffic-Cognitive Networks
This addresses efficient resource utilization and QoS in edge computing for platforms and edge servers, but it is incremental as it builds on existing game-theoretic and optimization methods.
The paper tackles the joint optimization of revenue, resource allocation, and task offloading in multi-edge traffic-cognitive networks by proposing a Stackelberg game-based framework with centralized and decentralized solutions, achieving superior performance in numerical evaluations compared to existing baselines.
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.