ClusterSense-SS: A Large-Scale Smart Scheduling Dataset for Intelligent Cluster Resource Management

The ClusterSense-SS dataset provides a large-scale collection of job scheduling and resource allocation records from CPU cluster computing systems. The dataset includes information related to job characteristics, resource requirements, execution times, queue waiting times, priority levels, CPU and memory allocations, and scheduling outcomes. It is intended to facilitate research in intelligent workload scheduling, resource optimization, and AI-driven cluster management. This dataset can be used to develop and evaluate machine learning models for efficient task scheduling, reduced execution latency, balanced resource utilization, and improved overall system performance in high-performance computing environments.

ClusterSense-SS数据集（ClusterSense-SS）收录了来自CPU集群计算系统的大规模作业调度与资源分配记录。该数据集涵盖作业特征、资源需求、执行时长、队列等待时长、优先级、CPU与内存分配情况以及调度结果等相关信息，旨在助力智能工作负载调度、资源优化及AI驱动的集群管理领域的研究工作。本数据集可用于开发并评估适用于高性能计算环境的机器学习模型，以实现高效任务调度、降低执行延迟、均衡资源利用率并提升系统整体性能。