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Green-aware workload scheduling in geographically distributed data centers

机译:地理分布的数据中心中的绿色感知工作负载调度

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Renewable (or green) energy, such as solar or wind, has at least partially powered data centers to reduce the environmental impact of traditional energy sources (brown energy with high carbon footprint). In this paper, we propose a holistic workload scheduling algorithm to minimize the brown energy consumption across multiple geographically distributed data centers with renewable energy sources. While green energy supply for a single data center is intermittent due to daily/seasonal effects, our workload scheduling algorithm is aware of different amounts of green energy supply and dynamically schedules the workload across data centers. The scheduling decision adapts to workload and data center cooling dynamics. Our experiments with real workload traces demonstrate that our scheduling algorithm greatly reduces brown energy consumption by up to 40% in comparison with other scheduling policies.
机译:可再生(或绿色)能源,例如太阳能或风能,至少具有部分供电的数据中心,以减少传统能源(碳足迹高的棕色能源)对环境的影响。在本文中,我们提出了一种整体工作量调度算法,以最小化具有可再生能源的多个地理分布的数据中心之间的棕色能源消耗。由于日常/季节影响,单个数据中心的绿色能源供应是间歇性的,但我们的工作负载调度算法可以识别不同数量的绿色能源供应,并可以动态地调度整个数据中心的工作负载。调度决策可适应工作负载和数据中心散热动态。我们的实际工作负载跟踪实验表明,与其他调度策略相比,我们的调度算法可最大程度地减少棕色能源消耗,最多可减少40%。

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