摘 要：
随着5G通信技术的快速发展，网络切片作为其核心关键技术之一，为多样化业务需求提供了灵活高效的资源分配方案，但如何在多租户环境下实现高效资源调度与性能优化仍面临诸多挑战。本研究以提升网络切片资源利用率和用户体验为目标，深入探讨了基于网络切片的资源调度策略及性能优化方法。通过引入强化学习算法，设计了一种动态自适应资源分配机制，能够根据实时流量负载和业务优先级调整资源分配比例，从而有效降低时延并提高吞吐量。同时，提出了一种基于博弈论的多目标优化模型，综合考虑了公平性、能耗和成本等多维约束条件，实现了资源的全局最优配置。实验结果表明，所提方法在复杂网络环境下表现出显著优势，不仅提升了系统整体性能，还满足了不同业务场景下的个性化需求。此外，本研究创新性地将边缘计算与网络切片相结合，进一步增强了本地化处理能力，降低了核心网负担。总体而言，该研究为未来网络切片技术的实际部署和应用提供了理论支持和技术参考，具有重要的学术价值和实践意义。
关键词：网络切片；资源调度；强化学习；博弈论；边缘计算
Research on Resource Scheduling and Performance Optimization Based on Network Slicing
Abstract: With the rapid development of 5G communication technology, network slicing, as one of its core key technologies, provides a flexible and efficient resource allocation solution for diverse service requirements. However, achieving effective resource scheduling and performance optimization in a multi-tenant environment remains challenging. This study aims to improve the resource utilization of network slices and enhance user experience by thoroughly investigating resource scheduling strategies and performance optimization methods based on network slicing. By introducing reinforcement learning algorithms, a dynamic adaptive resource allocation mechanism is designed, which can adjust resource allocation ratios according to real-time traffic loads and service priorities, thereby effectively reducing latency and improving throughput. Additionally, a multi-ob jective optimization model based on game theory is proposed, comprehensively considering multiple constraints such as fairness, energy consumption, and cost, to achieve globally optimal resource configuration. Experimental results demonstrate that the proposed methods exhibit significant advantages in complex network environments, not only enhancing overall system performance but also meeting personalized demands under various service scenarios. Furthermore, this research innovatively integrates edge computing with network slicing, further strengthening localized processing capabilities and reducing the burden on the core network. Overall, this study provides theoretical support and technical references for the practical deployment and application of future network slicing technology, possessing important academic value and practical significance.
Keywords: Network Slicing; Resource Scheduling; Reinforcement Learning; Game Theory; Edge Computing
目 录
1绪论 1
1.1网络切片技术的研究背景与意义 1
1.2国内外研究现状分析 1
1.3本文研究方法与技术路线 2
2网络切片资源调度的基础理论 2
2.1网络切片的基本概念与特性 2
2.2资源调度的核心挑战与需求 3
2.3常见资源调度算法的比较分析 3
2.4网络切片性能评估的关键指标 4
2.5理论框架对实际应用的指导意义 4
3基于网络切片的资源调度优化策略 5
3.1动态资源分配机制的设计原则 5
3.2面向多业务场景的调度模型构建 5
3.3基于机器学习的智能调度算法研究 6
3.4资源冲突与优先级管理的解决方案 6
3.5实验验证与结果分析 7
4网络切片性能优化的技术实现 7
结论 9
参考文献 10
致 谢 11
