IMPROVING DATA MONITORING AND QUALITY USING AI AND MACHINE LEARNING

    公开(公告)号:US20220404235A1

    公开(公告)日:2022-12-22

    申请号:US17351085

    申请日:2021-06-17

    Abstract: Systems and methods are provided for improving statistical and machine learning drift detection models that monitor computing health of a data center environment. For example, the system can receive streams of sensor data from a plurality of sensors in a data center; clean the streams of sensor data; generate, using a machine learning (ML) model, an anomaly score and a dynamic threshold value based on the cleaned streams of sensor data; determine, using the ML model and based on the anomaly score and the dynamic threshold value, a correctness indicator for a first sensor in the plurality of sensors; and using the correctness indicator, correct the first sensor.

    POWER CONSUMPTION MANAGEMENT THROUGH APPLYING OF A SYSTEM POWER CAP ON HETEROGENOUS SYSTEMS

    公开(公告)号:US20240353911A1

    公开(公告)日:2024-10-24

    申请号:US18303403

    申请日:2023-04-19

    CPC classification number: G06F1/3234

    Abstract: Systems and methods are provided for distributing a system power cap amongst system equipment for efficient utilization of power cap ranges without requiring an understanding of intricacies of the system architecture. An example of the system and methods obtain power cap ranges for controllable system equipment and power cap values for non-controllable system equipment; calculate a settable power cap range for the system based on the power cap ranges and power cap values; based on a requested power cap, determine power caps for controllable system equipment from a comparison of the requested power cap against the settable power cap range; and provide the determined power caps to the system for application to each of the controllable system equipment. In various examples, the power caps for the controllable system equipment can be determined through application of one or more distribution schemes.

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