Abstract: Techniques for predicting anomalies in forecasted time-series data are disclosed. A system. A system predicts whether a monitored computing system will experience anomalies by comparing forecasted values associated with components in the monitored computing system to threshold values. The system utilizes time-series machine learning models to forecast workloads of computing resources in the monitored computing system. The system trains and tests multiple different versions of a time-series model and selects the most accurate version to generate forecasts for a particular workload in the computing system. The system compares the forecasts to threshold values to predict anomalies. Based on detecting anomalies, the system generates recommendations for remediating predicted anomalies.

Abstract: Techniques for selecting and training candidate time-series models to forecast computational workloads are disclosed. A system creates a candidate set of time-series models for forecasting computing workloads by filtering the sets of parameter values to a number that meets system performance specifications. The system selects different sets of parameter values for different candidate models based on analyzing correlogram data. The system identifies in the correlogram data a set of one or more correlation values that (a) meet or exceed a threshold value, and (b) meet a distance criteria from the threshold value. The system trains the candidate set of time-series models with a training data set. The system selects the best-performing time-series model to generate forecasts for a particular computing resource in a computing system.

Abstract: Techniques for multi-layer forecasting of computational workloads are disclosed. A system identifies a level of granularity associated with a request to forecast a computational workload for a particular entity. The system obtains attribute data of computational resources at the specified level of granularity. The system determines whether computational resources, not specified in the request, should be included in a workload forecast. The system applies a time-series forecast model to time-series data obtained from computational resources associated with the request. The system presents one or more workload forecasts for computational workloads associated with the request.

Abstract: Techniques for time-series analysis for forecasting computational workloads are disclosed. A resource management system monitors a computing system and obtains metrics data from the computing system. The metrics data is stored as a set of historical data points of a data set. A first portion of the set of historical data points comprise an outlier that does not correspond to a seasonality pattern associated with a second portion of the set of historical data points. The resource management system tests a first time-series model that incorporates a first exogenous variable corresponding to a first exogeneous factor to determine that the first time-series model fits both the first portion of the set of historical data points and the second portion of the set of historical data points within an error threshold. Then, the resource management system selects the first time-series model to predict future data points of the data set.

Abstract: Techniques for time-series analysis for forecasting computational workloads are disclosed. A resource management system monitors a computing system and obtains metrics data from the computing system. The metrics data is stored as a set of historical data points of a data set. A first portion of the set of historical data points comprise an outlier that does not correspond to a seasonality pattern associated with a second portion of the set of historical data points. The resource management system tests a first time-series model that incorporates a first exogenous variable corresponding to a first exogeneous factor to determine that the first time-series model fits both the first portion of the set of historical data points and the second portion of the set of historical data points within an error threshold. Then, the resource management system selects the first time-series model to predict future data points of the data set.

Abstract: Provided is a cladded single crystal sapphire optical device (e.g., a s sapphire optical fiber or wafer). In one embodiment, the innovation provides a method for forming a cladding in a single crystal sapphire optical device by reactor irradiation. The reactor irradiation creates ions external to the optical device that enter the optical device, displace atoms in the optical device, and are implanted in the optical device, thus modifying the index of refraction of the optical device near the surface of the optical device and creating a cladding in the sapphire optical device.

Abstract: Provided is a cladded single crystal sapphire optical fiber. In one embodiment, the innovation provides a method for forming a cladding in a single crystal sapphire optical fiber by reactor irradiation. The reactor irradiation creates ions external to the fiber that enter the fiber, displace atoms in the fiber, and are implanted in the fiber, thus modifying the index of refraction of the fiber near the surface of the fiber and creating a cladding in the sapphire fiber.

Abstract: Provided is a cladded single crystal sapphire optical fiber. In one embodiment, the innovation provides a method for forming a cladding in a single crystal sapphire optical fiber by reactor irradiation. The reactor irradiation creates ions external to the fiber that enter the fiber, displace atoms in the fiber, and are implanted in the fiber, thus modifying the index of refraction of the fiber near the surface of the fiber and creating a cladding in the sapphire fiber.

Abstract: A method includes transmitting, by a processing system included in a user device, a query wrapper including a search query and a multi-state request to a remote device. The method further includes receiving search results from the remote device, wherein the search results include a multi-state result. The multi-state result includes multi-state instructions and access mechanisms that may be used to launch a primary application state and one or more secondary application states. The method further includes displaying the multi-state result as a user-selectable link in a search engine results page, whereby the user-selectable link indicates the primary application state. Additionally, the method includes launching the primary application state and the one or more secondary application states indicated by the multi-state result according to the multi-state instructions and access mechanisms.

Abstract: In an aspect, the invention is directed to a vehicle chassis for a vehicle that is at least partly driven by an electric motor, wherein the vehicle chassis incorporates a battery pack that acts as a structural member of the chassis.

Abstract: In an aspect, the invention is directed to a vehicle chassis for a vehicle that is at least partly driven by an electric motor, wherein the vehicle chassis incorporates a battery pack that acts as a structural member of the chassis.