Abstract: A disclosed computer-implemented method may include providing, by a processor, an artificial intelligence model (“predictive model”) pre-configured to infer, via a plurality of features included in the predictive model trained using data representative of computing resource requirements of current tenants of a multitenant computing platform, computing resource requirements for potential tenants of the multitenant computing platform. The method may also include receiving, by the processor, at least one query comprising data that describes an attribute of a potential tenant of the multitenant computing platform, and determining, by the processor, based on an inference of the predictive model generated based on the at least one query, an output comprising an estimated computing resource requirement of the potential tenant. Various other methods, systems, and computer-readable media are also disclosed.

Abstract: A disclosed computer-implemented method may include providing, by a processor, an artificial intelligence model (“predictive model”) pre-configured to infer, via a plurality of features included in the predictive model trained using data representative of computing resource requirements of current tenants of a multitenant computing platform, computing resource requirements for potential tenants of the multitenant computing platform. The method may also include receiving, by the processor, at least one query comprising data that describes an attribute of a potential tenant of the multitenant computing platform, and determining, by the processor, based on an inference of the predictive model generated based on the at least one query, an output comprising an estimated computing resource requirement of the potential tenant. Various other methods, systems, and computer-readable media are also disclosed.

Abstract: A disclosed computer-implemented method may include providing, by a processor, an artificial intelligence model (“predictive model”) pre-configured to infer, via a plurality of features included in the predictive model trained using data representative of computing resource requirements of current tenants of a multitenant computing platform, computing resource requirements for potential tenants of the multitenant computing platform. The method may also include receiving, by the processor, at least one query comprising data that describes an attribute of a potential tenant of the multitenant computing platform, and determining, by the processor, based on an inference of the predictive model generated based on the at least one query, an output comprising an estimated computing resource requirement of the potential tenant. Various other methods, systems, and computer-readable media are also disclosed.

Abstract: A disclosed computer-implemented method may include providing, by a processor, an artificial intelligence model (“predictive model”) pre-configured to infer, via a plurality of features included in the predictive model trained using data representative of computing resource requirements of current tenants of a multitenant computing platform, computing resource requirements for potential tenants of the multitenant computing platform. The method may also include receiving, by the processor, at least one query comprising data that describes an attribute of a potential tenant of the multitenant computing platform, and determining, by the processor, based on an inference of the predictive model generated based on the at least one query, an output comprising an estimated computing resource requirement of the potential tenant. Various other methods, systems, and computer-readable media are also disclosed.

Abstract: A system for reading at least one wireless resonant sensor includes a signal parameter measuring device, a reader housing, a computing device electrically connected to the signal parameter measuring device, and a plurality of reader antennas disposed within the reader housing and electrically connected to the signal parameter measuring device, wherein the plurality of reader antennas comprises a first reader antenna for signal output and a second reader antenna for signal input. The signal parameter measuring device is configured to sweep frequencies over a range of frequencies to acquire signal parameters for the wireless resonant sensor. The computing device is configured to determine changes in resonant frequency of the wireless resonator sensor based on the signal parameters. The reader housing may be sized and shaped for placement against a surface of a vessel in which a chemical or biological process is occurring.

Abstract: A system is provided for monitoring foam levels within a vessel wherein one or more chemical reactions or biological growths are occurring. The system includes a resonant sensor positioned outside of the vessel at a position to measure foam level within the vessel, the resonant sensor having an inductive element and a capacitive element and tuned to provide for enhanced sensitivity of changes in local permittivity to resonate.

Abstract: A system for reading at least one wireless resonant sensor includes a signal parameter measuring device, a reader housing, a computing device electrically connected to the signal parameter measuring device, and a plurality of reader antennas disposed within the reader housing and electrically connected to the signal parameter measuring device, wherein the plurality of reader antennas comprises a first reader antenna for signal output and a second reader antenna for signal input. The signal parameter measuring device is configured to sweep frequencies over a range of frequencies to acquire signal parameters for the wireless resonant sensor. The computing device is configured to determine changes in resonant frequency of the wireless resonator sensor based on the signal parameters. The reader housing may be sized and shaped for placement against a surface of a vessel in which a chemical or biological process is occurring.

Abstract: A system for metering of application services in utility computing environments includes one or more meter agents and a meter aggregator. A meter agent may be configured to periodically collect usage data corresponding to one or more resources, and provide the usage data to the meter aggregator. The meter aggregator may be configured to aggregate the usage data associated with an application service, where the application service includes a plurality of application processes. In addition, the meter aggregator may be configured to compute a billing amount based on an aggregate resource usage by the application service over a specified time period.

Abstract: A system for providing application redeployment mappings using filtered resource usage data includes a meter agent and an optimizer. The meter agent may be configured to collect measurements quantifying usage of one or more source resources by one or more applications. In response to a specification of a set of constraints associated with a deployment of one or more applications to a target execution environment, the optimizer may be configured to obtain a filtered aggregation of the measurements collected by the meter agent, and use the filtered aggregation to generate a mapping of at least one application to one or more target resources of the target execution environment in accordance with the set of constraints. The optimizer may implement a mathematical model to generate the mapping based on a determination of an optimum value of an objective function.