Cloud computing enables remote execution of users tasks. The pervasive adoption of cloud computing in smart cities services and applications requires timely execution of tasks adhering to Quality of Services (QoS). However, the increasing use of computing servers exacerbates the issues of high energy consumption, operating costs, and environmental pollution. Maximizing the performance and minimizing the energy in a cloud data center is challenging. In this paper, we propose a performance and energy optimization bi-objective algorithm to tradeoff the contradicting performance and energy objectives. An evolutionary algorithm-based multi-objective optimization is for the first time proposed using system performance counters. The performance of the proposed model is evaluated using a realistic cloud dataset in a cloud computing environment. Our experimental results achieve higher performance and lower energy consumption compared to a state of the art algorithm.

中文翻译：

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云数据中心的性能和能源感知双目标任务调度

云计算可实现用户任务的远程执行。智慧城市服务和应用中云计算的广泛采用要求及时执行符合服务质量（QoS）的任务。但是，计算服务器的日益使用加剧了高能耗，运营成本和环境污染的问题。在云数据中心中最大化性能和最小化能源是具有挑战性的。在本文中，我们提出了一种性能和能量优化的双目标算法，以权衡矛盾的性能和能量目标。首次提出使用系统性能计数器基于进化算法的多目标优化。在云计算环境中使用现实的云数据集评估了建议模型的性能。