International Business Machines Corporation

Kyndryl, Inc.

Vidhya Shankar Venkatesan

Dustin W Demetriou

Amol Bhaskar Mahamuni

Roger R Schmidt

Jinho Hwang

Larisa Shwartz

Rafal Bigaj

Frank Bagehorn

Marcos Vinicius L Paraiso

Dorothea W Wiesmann Rothuizen

Michael J Ellsworth, Jr

Milnes P David

Anand Haridass

Diyanesh B Chinnakkonda Vidyapoornachary

Arun Joseph

Sundaravelu Shanmugam

A trained classification model is executed, causing a classification of a first set of file system usage data into a set of categories comprising a trend category and a periodicity category. Responsive to the first set of file system usage data being classified into the trend category, a time series of the first set of file system usage data is generated. Responsive to the first set of file system usage data being classified into the periodicity category, using an anomaly detection model, an anomaly within the first set of file system usage data is detected. Responsive to predicting that the time series will exceed a threshold, a first reconfiguring of a file system resource is caused, altering a capacity of the file system. Responsive to detecting the anomaly, a second reconfiguring of the file system resource is caused, altering a capacity of the file system.

Technique selection for file system utilization prediction

Systems, computer-implemented methods, and computer program products that can facilitate refinement of a predicted event based on explainability data are provided. According to an embodiment, a system can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can comprise an interpreter component that identifies a probable cause of a predicted event based on explainability data. The computer executable components can further comprise an enrichment component that executes a diagnostic analysis based on the probable cause.

Refinement of a predicted event based on explainability data

Embodiments relate to monitoring computing hardware in a computing infrastructure facility. Image data and environmental data are received and a current operational status for a computing hardware component is determined from the image data. A hardware operational status tracking model and environment tracking model for the computing hardware component are updated. Embodiments can perform a root cause analysis if the current operational status is a fault status to determine if the fault status was caused by environmental conditions.

System to monitor computing hardware in a computing infrastructure facility

Automated managing of a data center installation is provided. The managing includes evaluating, at least in part by image processing analysis, a captured image of at least a portion of the data center installation to identify a component-related deficiency within the data center installation. One or more measurements within a data center are used to determine an energy penalty due to the identified component-related deficiency within the data center installation, and an action to correct the component-related deficiency within the data center installation is initiated based on the energy penalty exceeding a predefined threshold.

Automated managing of a data center installation

A system, method and computer program product for optimizing total cost of ownership (TCO) of a piece of IT equipment, e.g., a hard drive or server, using predictive analytics. The data center environment monitors and measures a number of environment variables, including temperature, Relative Humidity, and corrosion. For each piece of hardware, several pieces of data are assigned, including a criticality measure, an operational cost (function of environment), a static replacement cost, and a downtime cost (function of time). For each piece of hardware, if it has not yet failed, the system predicts a time-to-failure using the environment variables. If predicted time-to-failure exceeds an expected reference life criteria, real time TCO analytics is performed to minimize data center energy usage and/or maximize operational cost-efficiency.

Data center cost optimization using predictive analytics

Aspects include a method, system, and computer program product for determining a time to a threshold temperature of a device in a data center. A method includes measuring parameters for a device and the data center. A rate of change of temperature is determined based on the parameters. The change is compared to a change threshold. It is determined that a cooling system is operating below a threshold when the change is above the threshold. A first time is determined based on the rate of change of temperature and a machine learning model. The first and second time are compared, where the second time is a time to restore the cooling system above the threshold. A signal is transmitted when the first time is less than the second time. A cooling capacity is determined to have the temperature change of the device be equal to or less than the threshold.

Determining a time for corrective action in a data center

Aspects include a method, system, and computer program product for determining a time to a threshold temperature of an electronic device in a data center. A method includes measuring parameters for an electronic device and the data center. A rate of change of temperature is determined for the device based on the parameters. The rate of change is compared to a rate of change threshold. It is determined that a cooling system is operating below a threshold when the rate of change is above the threshold. A first time is determined, where the first time is based the rate of change of temperature and a machine learning model. The first time and second time are compared, where the second time is a time to restore the cooling system to above the threshold. A signal is transmitted when the first time is less than the second time.

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Bengaluru, India

Vidhya Shankar Venkatesan