对分布式异质并行系统的计算任务调度方法的研究
Xianzhi Cao1, Chong Chen1, Shiwei Li1
1College of Computer and Control Engineering, Northeast Forestry University, Harbin, 150040, China.
Scientific reports
|March 16, 2025
概括
这项研究引入了一种用于不同设备上的并行任务流的新调度方法,显著减少了14.3%的能源消耗,同时满足了对更绿色计算的响应时间需求.
科学领域:
- 分布式系统和计算
- 绿色计算和能源效率的提高
- 人工智能用于调度时间表
背景情况:
- 终端设备的扩散需要在分布式平台上高效地安排大量并行任务流.
- 计算资源提供商在可靠性,响应时间,成本降低和绿色计算的能源效率方面面临挑战.
- 异质的并行任务流调度对于优化资源利用和最大限度地减少系统能耗至关重要.
研究的目的:
- 调查异质的并行任务流调度问题,目的是在严格的响应时间限制下最大限度地降低系统能耗.
- 开发一种动态调度方法,提高可靠性和服务质量,同时优化能源效率.
- 在不断增长的终端设备的背景下,解决计划大规模并行任务流的核心挑战.
主要方法:
- 模拟任务调度问题作为使用指向环形图的混合整数非线性编程问题.
- 提出一种集启发式和强化学习算法用于任务流管理的动态调度方法.
- 应用基于可靠性分析的动态冗余,以提高系统故障耐受性和服务质量.
主要成果:
- 与现有方法相比,拟议的方法实现了能源消耗的14.3%降低.
- 通过异质的并行任务流程安排,显著提高了能源效率.
- 在实际的工作流实例上验证了动态调度和冗余策略的有效性.
结论:
- 开发的动态调度方法有效地将系统能耗降至最低,同时遵守响应时间限制.
- 将启发式学习和强化学习与动态冗余结合在一起,为可靠和节能任务调度提供了强大的解决方案.
- 这些发现有助于通过在分布式环境中优化调度实现绿色计算.
相关概念视频
01:21Multiple Pipe Systems
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Multipipe systems consist of complex configurations of interconnected pipes designed to transport fluids efficiently across intricate networks. They are essential in engineering applications requiring precise control over flow distribution, pressure, and head loss. They are categorized into series, parallel, loop, and network configurations, each distinguished by unique flow characteristics and applications.
Series Configuration
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Series Configuration
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01:29Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving
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Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
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