多模态网络调度算法仿真数据集

多模态网络调度算法仿真数据集主要面向算网协同调度算法的研究而建设。数据集记录了在不同网络拓扑、不同业务量条件下，通过Cloudism仿真框架仿真得到的不同的调度算法在不同网络拓扑中的调度结果，包含数据主要有：业务处理时间、业务处理情况等。数据集同时包含多模态算网资源协同调度算法研究过程中产生的一系列研究报告，如：算网一体化协同调度技术研究报告、算网资源协同与自动化模态与加载技术研究报告以及跨模态资源协同编排技术研究报告。研究报告主要聚焦如何对多模态网络中的计算资源、存储资源和网络资源进行统一协同编排，以及如何根据业务的资源需求、全局负载均衡度进行调度等问题。研究报告对以上问题展开研究，提出了一系列创新理论和可行的技术方案。数据量为35.2MB。

This multimodal network scheduling algorithm simulation dataset is developed specifically for research on computing-network collaborative scheduling algorithms. The dataset records the scheduling outcomes of various scheduling algorithms across different network topologies, which are generated through simulations using the Cloudism framework under varying traffic load conditions. The core collected data includes service processing time, service execution status, and other relevant metrics.
Additionally, the dataset contains a series of research reports produced during the study of multimodal computing-network resource collaborative scheduling algorithms, including the research report on integrated computing-network collaborative scheduling technology, the research report on computing-network resource collaboration, automated modal and loading technology, and the research report on cross-modal resource collaborative orchestration technology.
These research reports primarily focus on two key issues: how to implement unified collaborative orchestration of computing, storage and network resources in multimodal networks, and how to perform scheduling based on service resource demands and global load balancing levels. The reports conduct in-depth investigations into these topics and propose a series of innovative theories and feasible technical solutions. The total size of the dataset is 35.2 MB.

搜集汇总

背景与挑战

背景概述

该数据集面向算网协同调度算法研究，通过Cloudism仿真框架记录了不同网络拓扑和业务量条件下的调度结果，包括业务处理时间和处理情况等仿真数据。数据集还包含多模态算网资源协同调度算法的系列研究报告，聚焦计算、存储和网络资源的统一协同编排问题，数据量为35.2MB，格式为png和docx。