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Data center cost optimization using predictive analytics

  • US 10,152,394 B2
  • Filed: 09/27/2016
  • Issued: 12/11/2018
  • Est. Priority Date: 09/27/2016
  • Status: Active Grant
First Claim
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1. A computer-implemented method to manage environmental conditions of a data center comprising:

  • receiving, at a processor unit of a computer, sensor data from sensors monitoring environmental conditions at a data center, the data center housing operating hardware components that have not yet failed, and receiving reliability data of the hardware components; and

    for each hardware component;

    deriving, using an analytics model stored in a memory storage unit of the computer, an estimated time to failure of the hardware component, said analytics model being run on a processor unit and trained using machine learning, to correlate a component reliability using learned patterns of component failure, said received reliability data and said sensor data of monitored environmental conditions that the hardware component has been subject to at said data center;

    determining, at the processor unit, whether said estimated time to failure of the hardware component exceeds an expected reference life criteria time texp associated with that component, andfor each hardware component having a derived estimated time to failure that does not exceed the expected reference life criteria time texp for the respective component;

    computing, using the processor unit, a respective time for incurring a lowest cost to replace or repair the component;

    generating, using the processor unit, a candidate modification to one or more environmental conditions of said data center, wherein said candidate modification to said one or more environment conditions minimizes energy usage of operations at the data center and extends a life of the respective component while operating under said candidate modification to one or more environmental conditions at said data center to its respective lowest cost time to replace;

    computing, using the processor unit, an energy cost impact of letting the respective component operate under said candidate modified environment condition at said data center; and

    after generating a candidate modified environment condition associated with each hardware component having a derived estimated time to failure that does not exceed the expected reference life criteria time texp for the respective component;

    selecting, using the processor unit, an environmental condition modification from said generated candidate modified environment conditions, said selected environmental condition modification corresponding to a respective hardware component having a largest computed energy savings impact and running said analytics model on said processor to derive a new estimated time to failure of remaining hardware components having less than largest energy savings impact, said environmental condition modification selection ensuring that the new derived estimated time to failure of each remaining hardware component exceeds its respective said expected reference life criteria time if operating under the selected modification environment condition;

    generating, using the processor unit, an output signal for use in modifying said data center environment according to said selected environment condition modification;

    modifying said data center environment according to said selected environment condition modification, and scheduling a replacement of the hardware component corresponding to the selected environmental condition modification having the largest computed energy savings impact in the data center based on said computed time for incurring a lowest cost to replace or repair the component.

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