# Joint Resource Optimization, Computation Offloading and Resource Slicing for Multi-Edge Traffic-Cognitive Networks

arXiv: https://arxiv.org/abs/2411.17782 · HTML: https://mlsanity.com/paper/2411.17782/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 42 references that are in the map.
- First posted on arXiv 26 Nov 2024.

## Builds on (most cited first, 5 of 5)

- [Low-latency Federated Learning and Blockchain for Edge Association in Digital Twin empowered 6G Networks](https://mlsanity.com/paper/2011.09902/) (2020)
- [FlexEdge: Digital Twin-Enabled Task Offloading for UAV-Aided Vehicular Edge Computing](https://mlsanity.com/paper/2305.01536/) (2023)
- [An Optimal Load Balanced Resource Allocation Scheme for Heterogeneous Wireless Networks based on Big Data Technology](https://mlsanity.com/paper/2101.02666/) (2021)
- [An Optimized Multi-Layer Resource Management in Mobile Edge Computing Networks: A Joint Computation Offloading and Caching Solution](https://mlsanity.com/paper/2211.15487/) (2022)
- [A Novel Approach for Establishing Connectivity in Partitioned Mobile Sensor Networks Using Beamforming Techniques](https://mlsanity.com/paper/2308.04797/) (2023)

## Abstract (arXiv)

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.