边缘网络缓存更新系统效用数据

《边缘网络缓存更新系统效用数据》：本数据集面向支持边缘网络智能重放置的多域融合的移动性控制机制和确定性达成技术，数据由边缘网络缓存更新模型运行过程中采集并导出，所设计算法模型的训练过程采用Python语言结合深度学习框架实现，通过持续的交互与训练获得缓存更新与资源分配的联合最优策略。使用Excel软件对数据进行了清洗、归档与分析，验证了策略的收敛性与有效性，确保数据具有良好的实用性与可复现性。该数据集包含了时间信息、策略迭代周期与系统效用数据。数据用于验证边缘网络资源管控机制的有效性，评估该机制下的系统效用性能，为多域融合的移动性控制机制和确定性达成技术提供数据支持。测试模拟了网络在不同边缘节点与不同系统参数下的运行场景，以检验模型在不同场景下的系统缓存更新策略的有效性与可靠性。每类场景在统一网络拓扑结构与软件平台下进行多轮实验采样，确保数据具有代表性与可对比性。测试过程中，仿真平台统一调用数据采集、预处理、模型训练与预测等关键组件。本数据集的数据类型为：文件，数据量为：6KB，共享方式为：完全共享，不设置保护期。

Utility Data of Edge Network Cache Update System: This dataset targets multi-domain integrated mobility control mechanisms and deterministic assurance technologies that support intelligent repositioning in edge networks. The data is collected and exported during the operation of the edge network cache update model. The training of the proposed algorithm model is implemented using Python and deep learning frameworks, and the joint optimal strategy for cache update and resource allocation is obtained through continuous interaction and training. The data was cleaned, archived and analyzed with Excel, and the convergence and effectiveness of the strategy were verified, ensuring that the data has excellent practicality and reproducibility. This dataset includes time information, strategy iteration cycles and system utility data. The data is utilized to validate the effectiveness of edge network resource management and control mechanisms, evaluate the system utility performance under such mechanisms, and provide data support for multi-domain integrated mobility control mechanisms and deterministic assurance technologies. The tests simulate network operating scenarios under different edge nodes and system parameters, to examine the effectiveness and reliability of the model's cache update strategy across diverse scenarios. For each scenario category, multiple rounds of experimental sampling are conducted under a unified network topology and software platform, ensuring the data is representative and comparable. During the testing phase, the simulation platform uniformly invokes core components including data collection, preprocessing, model training and prediction. The data type of this dataset is file, with a total size of 6KB, and it is fully shared without setting a protection period.