Abstract: Disclosed are various approaches for the partitioning of virtualization on systems with multiple core processors. In one approach, hardware extensions for virtualizations are enabled on one or more first cores of a plurality of cores of the processor. The hardware extensions for virtualization are disabled on one or more second cores of the plurality of cores. A virtual machine instance is executed on the first cores having the hardware extensions for virtualization enabled. A real-time operating system is executed on the second cores having the hardware extensions for virtualization disabled.

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: A method for providing a binary format compilation target for an operating system, virtualization platform or kernel includes instantiating one or more modules to create an instance of the binary format compilation target using a an interface compatible with the operating system, virtualization platform or kernel, assigning resources to the instance via the kernel or a kernel of the virtualization platform or operating system, and executing one or more applications on the instance. Source code may be decoded in a virtualization platform to instantiate modules involved in executing applications or processes that are in a native binary format in a virtualized native format instance.

Abstract: Disclosed are various examples of providing AI accelerator access as a service at the edge. In some embodiments an artificial intelligence (AI) accelerator device identifier is transmitted to register an AI accelerator with the AI broker service. An AI processing request for the AI accelerator is received from a networked computing device. A bus redirect of the AI accelerator to the networked device is enabled. An AI workload is performed controlled by the networked device through the bus redirect.

Abstract: Aspects of remote edge virtualization management are described. An edge hypervisor shadow application is executed. The edge hypervisor shadow application acts as an Input/Output for an edge hypervisor that is IP inaccessible to a virtualization service. The edge hypervisor shadow application receives a hypertext transport protocol (HTTP) communication from the virtualization service. A Message Queue Telemetry Transport (MQTT) message is generated to include the HTTP request, and is published to an MQTT broker service, the MQTT message comprising the HTTP request.

Abstract: Aspects of wireless communications for edge virtualization management are described. An edge hypervisor launches a wireless communications virtual machine (VM) that includes a guest operating system (OS). The guest OS provides drivers for a wireless adapter of the edge device. A component of the wireless communications VM configures network connection data that enables a wireless adapter to connect to a wireless network. The wireless communications VM executes a Dynamic Host Configuration Protocol (DHCP) component. A wireless network connection is enabled using the edge hypervisor and the wireless communications virtual machine. The DHCP component identifies IP data for the wireless network connection.

Abstract: Disclosed herein is the integration into edge nodes of a telecommunications network system of client computer system and server computer system where the server computer system includes a pool of shareable accelerators and the client computer runs an application program that is assisted by the pool of accelerators. The edge nodes connect to user equipment, and some of the user equipment can themselves act as one of the client computer systems. In some embodiments, the accelerators are GPUs, and in other embodiments, the accelerators are artificial intelligence accelerators.

Abstract: Disclosed herein is the integration into edge nodes of a telecommunications network system of client computer system and server computer system where the server computer system includes a pool of shareable accelerators and the client computer runs an application program that is assisted by the pool of accelerators. The edge nodes connect to user equipment, and some of the user equipment can themselves act as one of the client computer systems. In some embodiments, the accelerators are GPUs, and in other embodiments, the accelerators are artificial intelligence accelerators.

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 approaches for the partitioning of virtualization on systems with multiple core processors. In one approach, hardware extensions for virtualizations are enabled on one or more first cores of a plurality of cores of the processor. The hardware extensions for virtualization are disabled on one or more second cores of the plurality of cores. A virtual machine instance is executed on the first cores having the hardware extensions for virtualization enabled. A real-time operating system is executed on the second cores having the hardware extensions for virtualization disabled.

Abstract: Disclosed are various examples of providing edge component redirection for IoT analytics groups. In some embodiments, an Internet-of-Things (IoT) analytics group is identified. The IoT analytics group includes an IoT device that communicates through an interface device of a first edge device. A grouping interface policy is generated to specify a bus redirect from the first edge computing device to a second edge computing device. The grouping interface policy is transmitted for implementation using the edge devices.

Abstract: A method for generating a video can include: acquiring a profile of a video template, the profile being configured to describe one or more reference materials of the video template and an animated special effect corresponding to each of the one or more reference materials; acquiring the one or more reference materials based on the profile; acquiring a target material; acquiring at least one replaced reference material by replacing at least one of the one or more reference materials with the target material; and generating a target video with the animated special effect by rendering the one or more replaced reference material based on the profile.

Abstract: Disclosed are various examples of providing cross platform accelerator remoting between complex instruction set computer (CISC) components and reduced instruction set computer (RISC) components of a computing environment. An accelerator remoting server receives accelerator instructions executable by a locally installed accelerator device and provides the accelerator instructions to the accelerator device. The accelerator remoting server transmits accelerator results to an accelerator remoting client to complete the cross platform or platform agnostic accelerator remoting.

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 of providing edge component redirection for IoT analytics groups. In some embodiments, an Internet-of-Things (IoT) analytics group is identified. The IoT analytics group includes an IoT device that communicates through an interface device of a first edge device. A grouping interface policy is generated to specify a bus redirect from the first edge computing device to a second edge computing device. The grouping interface policy is transmitted for implementation using the edge devices.

Abstract: Disclosed are various examples of providing AI accelerator access as a service at the edge. In some embodiments an artificial intelligence (AI) accelerator device identifier is transmitted to register an AI accelerator with the AI broker service. An AI processing request for the AI accelerator is received from a networked computing device. A bus redirect of the AI accelerator to the networked device is enabled. An AI workload is performed controlled by the networked device through the bus redirect.

Abstract: Disclosed are various examples of providing edge component redirection for IoT analytics groups. In some embodiments, a grouping interface policy is received for an Internet-of-Things (IoT) device that communicates through an interface device of a first edge computing device. The grouping interface policy indicates that the IoT device is assigned to a second edge computing device. A bus redirect of the interface device is enabled. The bus redirect routes traffic between the IoT device and the second edge computing device.

Abstract: A method for generating a video can include: acquiring a profile of a video template, the profile being configured to describe one or more reference materials of the video template and an animated special effect corresponding to each of the one or more reference materials; acquiring the one or more reference materials based on the profile; acquiring a target material; acquiring at least one replaced reference material by replacing at least one of the one or more reference materials with the target material; and generating a target video with the animated special effect by rendering the one or more replaced reference material based on the profile.