Abstract: Disclosed is a system for converting a high-level runtime model to a low-level runtime model where the high-level runtime model runs on a client computer system, and the low-level runtime model runs on a server computer system. The server system has installed thereon a pool of hardware accelerators, and the low-level runtime model is targeted to the pool of accelerators. Outputs of the low-level runtime model are returned to the high-level runtime model as if the high-level runtime model computed the outputs.

Abstract: Methods, apparatus, systems, and articles of manufacture are disclosed to predict power consumption in a server. An example apparatus includes interface circuitry to obtain a power prediction request corresponding to the server range determiner circuitry to divide a training data set into a first sub-range of data and a second sub-range of the data; a data point in the training data set representative of resource utilization of a workload and a corresponding power consumption metric of the workload; model trainer circuitry to train first candidate models based on the first sub-range of the data and second candidate models based on the second sub-range of the data; and prediction selector circuitry to: select a first prediction model from the first candidate models; and select a second prediction model from the second candidate models, outputs of the first and the second prediction models to predict the power consumption of the server.

Abstract: Disclosed are various examples for threat detection and security for edge devices in communication with Internet-of-Things (IoT) devices. In one example, a baseline behavior profile for a gateway virtual machine is transmitted from a management service to a gateway security process executed in a gateway device. The management service receives an anomaly notification including an indication of an anomaly from the baseline behavior profile. The managements service generates a user interface that shows a description of the anomaly.

Abstract: Disclosed is a system for converting a high-level runtime model to a low-level runtime model where the high-level runtime model runs on a client computer system, and the low-level runtime model runs on a server computer system. The server system has installed thereon a pool of hardware accelerators, and the low-level runtime model is targeted to the pool of accelerators. Outputs of the low-level runtime model are returned to the high-level runtime model as if the high-level runtime model computed the outputs.

Abstract: Disclosed are various examples for threat detection and security for edge devices in communication with Internet-of-Things (IoT) devices. In one example, a baseline behavior profile for a gateway virtual machine is transmitted from a management service to a gateway security process executed in a gateway device. The management service receives an anomaly notification including an indication of an anomaly from the baseline behavior profile. The managements service generates a user interface that shows a description of the anomaly.

Abstract: Disclosed are various examples for threat detection and security for edge devices in communication with Internet-of-Things (IoT) devices. In one example, a profile is associated with a virtual machine of a gateway device. The profile includes an expected behavior for the virtual machine. The virtual machine is executed by a hypervisor of the gateway device. An actual behavior for the virtual machine is determined. A remedial action is performed. The remedial action is based on an anomaly between the expected behavior and the actual behavior.

Abstract: Disclosed are various examples for threat detection and security for edge devices in communication with Internet-of-Things (IoT) devices. In one example, a profile is associated with a virtual machine of a gateway device. The profile includes an expected behavior for the virtual machine. The virtual machine is executed by a hypervisor of the gateway device. An actual behavior for the virtual machine is determined. A remedial action is performed. The remedial action is based on an anomaly between the expected behavior and the actual behavior.