Abstract: A method for directly estimating PUE of one or more data handling facilities of a communication system. The method includes receiving a facility power dataset generated by a plurality of power monitors connected between electrically powered equipment of data handling facility of the one or more data handling facilities, and classifying data as belonging to either a productive power consumption group or a nonproductive power consumption group to provide a labeled facility power dataset. The method additionally includes creating by a PUE profile engine a PUE profile of the data handling facility using at least the labeled facility power dataset, the PUE profile containing an estimated PUE of the data handling facility based on the labeled facility power dataset, and continually and automatically updating by the PUE profile engine the estimated PUE of the PUE profile as the facility power dataset is received and provide the labeled facility power dataset.

Abstract: A simulator extracts sensor data from multiple systems. The sensor data includes measurements taken by sensors disposed at the multiple systems. The simulator standardizes the sensor data into a common format and classifies the sensor data according to a performance metric. A model of a target system for the performance metric is generated based on the standardized sensor data. The simulator can simulate the impact on the performance metric for a target system based on a simulated change to the multiple systems. The simulator can generate a network interface including a tool that enables end users to interact with the simulation and to determine procedures for mitigating the impact.

Abstract: A method for directly estimating PUE of one or more data handling facilities of a communication system. The method includes receiving a facility power dataset generated by a plurality of power monitors connected between electrically powered equipment of data handling facility of the one or more data handling facilities, and classifying data as belonging to either a productive power consumption group or a nonproductive power consumption group to provide a labeled facility power dataset. The method additionally includes creating by a PUE profile engine a PUE profile of the data handling facility using at least the labeled facility power dataset, the PUE profile containing an estimated PUE of the data handling facility based on the labeled facility power dataset, and continually and automatically updating by the PUE profile engine the estimated PUE of the PUE profile as the facility power dataset is received and provide the labeled facility power dataset.

Abstract: A method for managing an ontology of a data handling facility of a communication system. The method includes discovering connections between some of a plurality of electrically powered components of the data handling facility based on time-series data obtained from a plurality of electrical power monitors, and forming a physical ontology layer of the data handling facility and a logical ontology layer of the data handling facility. In addition, the method includes receiving a query from a user concerning a hypothetical modification to the operation of the data handling facility, and forecasting change in the operation of the data handling facility based on the query received from the user.

Abstract: In some examples, a method for managing an ontology of a data handling facility of a communication system includes receiving data points from a node of the data handling facility, the data points indicating the node as a reporting node. The method also includes performing name-based data validation of the data points. The method also includes responsive to the data points passing the name-based data validation, recording the data points in a data store. The method also includes determining a performance metric based on the data points. The method also includes comparing the performance metric to an expected metric. The method also includes responsive to the performance metric having a difference from the expected metric that exceeds a standard deviation, performing remedial actions regarding the data handling facility.

Abstract: Introduced herein is a computer-implemented system for creating a digital twin of an electrical system using auto-discovery techniques. The system receives power data from meters in an electrical system. For each meter, the system captures a power profile related to a component connected to the meter and creates a set of delta data representing change in power over time. The system detects correlated changes by comparing the sets of delta data and generates a system dataset by combining the sets of delta data. The system detects echoes of power fluctuations of the electrical system from the system dataset and creates a digital twin of the electrical system.

Abstract: Introduced herein is a computer-implemented system for creating a digital twin of an electrical system using auto-discovery techniques. The system receives power data from meters in an electrical system. For each meter, the system captures a power profile related to a component connected to the meter and creates a set of delta data representing change in power over time. The system detects correlated changes by comparing the sets of delta data and generates a system dataset by combining the sets of delta data. The system detects echoes of power fluctuations of the electrical system from the system dataset and creates a digital twin of the electrical system.

Abstract: Described herein is a capacity system that predicts capacity needs of a system. The capacity system captures outputs from meters coupled to subsystems within the system, where the outputs are not expressed in standard power units. For each subsystem, the capacity system determines values for a set of conversion factors related to the subsystem. The conversion factors describe the design and operation of the subsystem. The capacity system converts the outputs to standard power units based on the set of conversion factors and simulates behavior of the subsystems based on trends shown by the converted outputs. The capacity system forecasts an expected capacity of the system over a time period and uses the forecast to predict a date during the time period when the subsystem will experience a reduction in capacity.

Abstract: An exemplary query parsing system accesses user input text representative of a search query. The system generates a plurality of candidate parses of the user input text. Each of the plurality of candidate parses includes a candidate what portion and a candidate where portion determined from the user input text. The system generates a score for each candidate parse in the plurality of candidate parses based on whether one or more of a plurality of predefined parsing features are found in the respective candidate parse and on a machine learning mode. The system selects, from the plurality of candidate parses based on the scores for the plurality of candidate parses, the candidate what portion and the candidate where portion of a particular candidate parse included in the plurality of candidate parses as search parameters for the search query. Corresponding methods and systems are also disclosed.

Abstract: A simulator extracts sensor data from multiple systems. The sensor data includes measurements taken by sensors disposed at the multiple systems. The simulator standardizes the sensor data into a common format and classifies the sensor data according to a performance metric. A model of a target system for the performance metric is generated based on the standardized sensor data. The simulator can simulate the impact on the performance metric for a target system based on a simulated change to the multiple systems. The simulator can generate a network interface including a tool that enables end users to interact with the simulation and to determine procedures for mitigating the impact.

Abstract: An exemplary query parsing system accesses user input text representative of a search query. The system generates a plurality of candidate parses of the user input text. Each of the plurality of candidate parses includes a candidate what portion and a candidate where portion determined from the user input text. The system generates a score for each candidate parse in the plurality of candidate parses based on whether one or more of a plurality of predefined parsing features are found in the respective candidate parse and on a machine learning mode. The system selects, from the plurality of candidate parses based on the scores for the plurality of candidate parses, the candidate what portion and the candidate where portion of a particular candidate parse included in the plurality of candidate parses as search parameters for the search query. Corresponding methods and systems are also disclosed.

Abstract: An exemplary query parsing system accesses user input text representative of a search query. The system generates a plurality of candidate parses of the user input text. Each of the plurality of candidate parses includes a candidate what portion and a candidate where portion determined from the user input text. The system generates a score for each candidate parse in the plurality of candidate parses based on whether one or more of a plurality of predefined parsing features are found in the respective candidate parse and on a machine learning mode. The system selects, from the plurality of candidate parses based on the scores for the plurality of candidate parses, the candidate what portion and the candidate where portion of a particular candidate parse included in the plurality of candidate parses as search parameters for the search query. Corresponding methods and systems are also disclosed.

Abstract: An apparatus referred to as a profiling server monitor receives data corresponding to the operation of physical hardware in a virtual computing environment. An example is power consumption data. The profiling server monitor analyzes the data received and determines an operation to perform or a business rule to follow in order to, as one example, reduce power consumption of the virtual computing environment.

Abstract: An exemplary query parsing system accesses user input text representative of a search query. The system generates a plurality of candidate parses of the user input text. Each of the plurality of candidate parses includes a candidate what portion and a candidate where portion determined from the user input text. The system generates a score for each candidate parse in the plurality of candidate parses based on whether one or more of a plurality of predefined parsing features are found in the respective candidate parse and on a machine learning mode. The system selects, from the plurality of candidate parses based on the scores for the plurality of candidate parses, the candidate what portion and the candidate where portion of a particular candidate parse included in the plurality of candidate parses as search parameters for the search query. Corresponding methods and systems are also disclosed.