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Task scheduling with ANN-based temperature prediction in a data center: a simulation-based study
2011
Engineering with Computers
High temperatures within a data center can cause a number of problems, such as increased cooling costs and increased hardware failure rates. To overcome this problem, researchers have shown that workload management, focused on a data center's thermal properties, effectively reduces temperatures within a data center. In this paper, we propose a method to predict a workload's thermal effect on a data center, which will be suitable for real-time scenarios. We use machine learning techniques, such
doi:10.1007/s00366-011-0211-4
fatcat:pyalssklbfekpg5pfgve2jdrkq