With the development of cloud computing and service architectures including software as a service (SaaS) and function as a service (FaaS), data centers, as the service provider, constantly face resource management. The quality of service (QoS) should be guaranteed, and the resource cost should be controlled. Therefore, a method to accurately measure computing power consumption becomes a key research issue for improving resource utilization and keeping the load pressure in the acceptable range. Due to mature virtualization technologies and developing parallel technologies, the traditional estimation metric CPU utilization fails to address interference caused by resource competition, thus leading to accuracy loss. However, the hyper-threading (HT) technology is employed as the main data center processor, which makes it urgent to estimate the computing power of HT processors. To address this estimation challenge, this study proposes the APU method to estimate the computing power consumption for HT processors based on the understanding of the HT running mechanism and thread behavior modeling. Considering that users with different authorities can access different system levels, two implementation schemes are put forward: one based on the hardware support and the other based on the operating system (OS). The proposed method adopts CPU utilization as the input without demands for other dimensions. Additionally, it reduces the development and deployment costs of new monitoring tools without the support of special hardware architectures, thereby making the method universal and easy to apply. Finally, SPEC benchmarks further prove the effectiveness of the method. The estimation errors of the three benchmarks are reduced from 20%, 50%, and 20% to less than 5%. For further proving the applicability, the APU method is leveraged to ByteDance clusters for showing its effects in case studies.