Other

Citrix Systems, Inc.

Thomas Portegys

William Deforeest

Systems and methods are disclosed for using machine learning (e.g., neural networks and/or combinatorial learning) to solve the non-linear problem of predicting the provisioning of a server farm (e.g., cloud resources). The machine learning may be performed using commercially available products, such as the SNNS product from The University of Stuttgard of Germany. The system, which includes a neural network for machine learning, is provided with an identification of inputs and outputs to track, and the system provides correlations between those. Rather than static rules, the machine learning provides dynamic provisioning recommendations with corresponding confidence scores. Based on the data collected/measured by the neural network, the provisioning recommendations will change as well as the confidence scores.

Managing computer server capacity

A portable distance measuring device that works by spanning separately targeted endpoints is described. The device contains a laser distance measuring component and sensing components that track changes in position and orientation of the device, such as accelerometers and gyroscopes. Distance is measured by pointing the laser at an endpoint and measuring the distance to it. Once this measurement is confirmed, the device can be moved to a different vantage location to measure a second endpoint with the laser. The orientation and position of the device for the second distance measurement relative to the first measurement are calculated by the position and orientation sensors. Together these values are sufficient to calculate the distance spanning the endpoints. This calculation is performed by a computer contained in the device and the distance displayed to the user.

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Duvall, WA, United States of America

Thomas Portegys