Dynamic resource scheduling and energy consumption optimization strategy of government cloud platform
政务云平台资源动态调度与能耗优化策略

摘要

政务云平台在多部门协同与大数据驱动的背景下，对计算资源的高效调度与能耗控制提出了更高要求。本文针对政务云平台的资源动态调度问题，构建基于实时监测与预测的多维度优化模型，通过引入任务优先级、节点负载均衡与能耗反馈机制，实现计算、存储及网络资源的动态分配。结合多目标优化算法与能耗约束策略，提出一套兼顾性能与能效的调度方案。实验结果表明，该策略能够在保证服务质量的同时显著降低整体能耗，提高资源利用率与平台运行效率。

Abstract

In the context of multi-department collaboration and big data-driven operations, government cloud platforms face heightened demands for efficient computing resource scheduling and energy consumption control. This paper develops a real-time monitoring and predictive optimization model for dynamic resource scheduling in government cloud platforms. By incorporating task prioritization, node load balancing, and energy consumption feedback mechanisms, the proposed approach enables dynamic allocation of computing, storage, and network resources. Combining multi-objective optimization algorithms with energy consumption constraints, we present a scheduling strategy that balances performance and energy efficiency. Experimental results demonstrate that this strategy significantly reduces overall energy consumption while maintaining service quality, thereby improving resource utilization and platform operational efficiency.