Utilizing green energy prediction to schedule mixed batch and service jobs in data centers
Utilizing green energy prediction to schedule mixed batch and service jobs in data centers
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DOI：
10.1145/2094091.2094105
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发表时间：
2011-10
期刊：
影响因子：
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通讯作者：
Baris Aksanli;J. Venkatesh;L. Zhang;Tajana Simunic
中科院分区：
文献类型：
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作者：
Baris Aksanli;J. Venkatesh;L. Zhang;Tajana Simunic
随着棕色能源成本的增长，可再生能源的使用变得更加广泛。以前的工作重点是使用立即可用的绿色能源来补充不可再生或棕色能源，但每当绿色能源可用性太低时，就会取消和重新安排工作[16]。在本文中，我们设计了一种自适应数据中心作业调度程序，它利用太阳能和风能生产的短期预测。这使我们能够将工作岗位数量扩大到预期的能源可用性，从而将取消的工作岗位数量减少 4 倍，并将绿色能源使用效率提高 3 倍，而仅利用立即可用的绿色能源。
As brown energy costs grow, renewable energy becomes more widely used. Previous work focused on using immediately available green energy to supplement the non-renewable, or brown energy at the cost of canceling and rescheduling jobs whenever the green energy availability is too low [16]. In this paper we design an adaptive data center job scheduler which utilizes short term prediction of solar and wind energy production. This enables us to scale the number of jobs to the expected energy availability, thus reducing the number of cancelled jobs by 4x and improving green energy usage efficiency by 3x over just utilizing the immediately available green energy.