Data centres constitute the engine of the Internet, and run a major portion of large web and mobile applications, content delivery and sharing platforms, and Cloud-computing business models. The high performance of such infrastructures is therefore critical for their correct functioning. This work focuses on the improvement of data-centre performance by dynamically switching the main data-centre governance software system: the resource manager. Instead of focusing on the development of new resource-managing models as soon as new workloads and patterns appear, we propose DISCERNER, a decision-theory model that can learn from numerous data-centre execution logs to determine which existing resource-managing model may optimise the overall performance for a given time period. Such a decision-theory system employs a classic machine-learning classifier to make real-time decisions based on past execution logs and on the current data-centre operational situation. A set of extensive and industry-guided experiments has been simulated by a validated data-centre simulation tool. The results obtained show that the values of key performance indicators may be improved by at least 20% in realistic scenarios.

数据中心构成引擎并运行大型Web和移动应用程序，内容交付和共享平台以及云计算业务模型的主要部分。因此，此类基础架构的高性能对其正确运行至关重要。这项工作的重点是通过动态切换主要的数据中心管理软件系统：资源管理器来改善数据中心的性能。我们提出了DISCERNER（决策理论模型），该决策理论模型可以从大量数据中心执行日志中学习，以确定哪些现有资源管理模型可以优化，而不是着眼于出现新的工作负载和模式后立即着手开发新的资源管理模型。给定时间段内的整体效果。这种决策理论系统采用经典的机器学习分类器，根据过去的执行日志和当前数据中心的运行状况做出实时决策。已通过验证的数据中心模拟工具对一组广泛的行业指导实验进行了模拟。获得的结果表明，在实际情况下，关键绩效指标的值可以提高至少20％。