As cloud computing technologies and applications develop rapidly in recent years, the quantity and size of cloud datacenters have been ever-increasing, making the overconsumption of energy in datacenters become a widespread concern. To reduce the energy cost by servers, we must first build an accurate power model to achieve flexible, device-free power consumption measuring. However, most of the previous work related to server power modeling solely apply to the server and virtual machine levels, and the existing power models fail to take into account the heterogeneity in workload. Therefore, we first propose separate power consumption models based on the distinction of workload types including CPU-intensive, I/O-intensive, memory-intensive, and mixed workload. Then, we present an adaptive workload-aware power consumption measuring method (WSPM) for cloud servers. Our method proactively selects an appropriate power model for the upcoming workload through workload clustering, forecasting and classification, which are implemented using K-means, ARIMA, and threshold-based methods, respectively. We conducted several experiments to evaluate the performance of the key components of our method. The result shows: (1) the accuracy of our future workload forecasting on real traces of requests to our servers, (2) the accuracy of the power consumption measured by WSPM, and (3) the effectiveness of our workload-aware method in reducing real-time power estimation lag. Overall, the proposed method simplifies power modeling under diverse workloads without losing accuracy, making it a general and highly available solution for cloud data centers.

中文翻译：

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一种自适应工作负载感知的云数据中心服务器功耗测量方法

近年来，随着云计算技术和应用的快速发展，云数据中心的数量和规模不断增加，使得数据中心的能源过度消耗成为普遍关注的问题。为了降低服务器的能源成本，我们必须首先建立一个准确的功率模型，以实现灵活、无设备的功耗测量。然而，之前与服务器电源建模相关的大部分工作仅适用于服务器和虚拟机级别，现有的电源模型未能考虑到工作负载的异构性。因此，我们首先根据工作负载类型的区分提出单独的功耗模型，包括 CPU 密集型、I/O 密集型、内存密集型和混合工作负载。然后，我们提出了一种适用于云服务器的自适应工作负载感知功耗测量方法 (WSPM)。我们的方法通过工作负载聚类、预测和分类主动为即将到来的工作负载选择合适的功率模型，这些模型分别使用 K-means、ARIMA 和基于阈值的方法实现。我们进行了几次实验来评估我们方法的关键组件的性能。结果显示：(1) 我们未来工作负载预测对服务器请求的真实跟踪的准确性，(2) WSPM 测量的功耗的准确性，以及 (3) 我们的工作负载感知方法在减少实时功率估计滞后。总体而言，所提出的方法简化了不同工作负载下的功率建模，而不会损失准确性，