Abstract: A method performs wireless localization data acquisition and calibration using a visual 3D model. The method includes: receiving image data and transmitter measurement data from a device; localizing the image data in the visual 3D model to determine a device location in a physical space; and using the device location and the transmitter measurement data to perform at least one of: determining transmitter fingerprint localization data and storing the transmitter fingerprint localization data in a fingerprint database; or calibrating a radio frequency (RF) propagation model for proximity estimation.

Abstract: A method performs wireless localization data acquisition and calibration using a visual 3D model. The method includes: receiving image data and transmitter measurement data from a device; localizing the image data in the visual 3D model to determine a device location in a physical space; and using the device location and the transmitter measurement data to perform at least one of: determining transmitter fingerprint localization data and storing the transmitter fingerprint localization data in a fingerprint database; or calibrating a radio frequency (RF) propagation model for proximity estimation.

Abstract: Nodes of a computing cluster can be selected to run new computing jobs while providing acceptable performance of jobs running on the nodes. Respective performance metrics of respective workloads on respective computing nodes can be determined. Each workload can include a new computing job and the performance metrics can be determined based at least in part on respective measured performance data of the ones of the computing nodes and information of the new computing job. Candidate ones of the computing nodes can be determined based at least in part on the respective performance metrics. One of the candidate computing nodes can be selected based at least in part on the information of the new computing job. In some examples, identification of the new computing job can be transmitted to the selected node. In some examples, state data of the nodes can be updated based on the performance data.

Abstract: Nodes of a computing cluster can be selected to run new computing jobs while providing acceptable performance of jobs running on the nodes. Respective performance metrics of respective workloads on respective computing nodes can be determined. Each workload can include a new computing job and the performance metrics can be determined based at least in part on respective measured performance data of the ones of the computing nodes and information of the new computing job. Candidate ones of the computing nodes can be determined based at least in part on the respective performance metrics. One of the candidate computing nodes can be selected based at least in part on the information of the new computing job. In some examples, identification of the new computing job can be transmitted to the selected node. In some examples, state data of the nodes can be updated based on the performance data.