在云环境中使用深度学习进行节能动态工作流程调度
Sunera Chandrasiri1, Dulani Meedeniya1
1Department of Computer Science and Engineering, University of Moratuwa, Moratuwa 10400, Sri Lanka.
Sensors (Basel, Switzerland)
|March 17, 2025
概括
本研究介绍了一种使用图形神经网络和深度强化学习的新云调度框架,以尽量减少任务完成时间和能源使用. 这种方法比传统方法显著提高了效率.
科学领域:
- 云计算 云计算 云计算 云计算
- 人工智能的人工智能
- 运营研究 运营研究
背景情况:
- 云环境中的动态工作流调度是复杂的,因为依赖,可变的工作负载和资源波动.
- 在云资源管理中,平衡 makespan (总完成时间) 和能源消耗是一个关键的挑战.
研究的目的:
- 提出一个新的调度框架,集成图形神经网络 (GNN) 和深度强化学习 (DRL) 以实现多目标优化.
- 为了最大限度地减少制作时间,并减少云工作流中的能源消耗.
主要方法:
- 利用GNN来建模适应性资源分配的任务依赖性.
- 使用近接政策优化 (PPO) 算法进行深度强化学习.
- 在基于CloudSim的模拟环境中使用合成数据集评估框架.
主要成果:
- 拟议的框架实现了689.22s的最低制程,比基准方法高出13.92%.
- 与HEFT,Min-Min和Max-Min等传统启发式方法相比,在制造量和能源消耗方面取得了持续的改进.
- 保持了10964.45 J的竞争性能源消耗.
结论:
- 集成GNN和DRL为云环境中的动态任务调度提供了强大的方法.
- 该框架有效地平衡了多个目标,包括制造商减少和能源效率.
- 研究结果强调了先进的人工智能技术的潜力,以优化云资源管理.
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