Accelerated behavior change for upgrades in a distributed system is described herein. A method as described herein can include facilitating a file system upgrade of a first computing node of a computing cluster from a first file system version to a second file system version that is newer than the first file system version, wherein the file system upgrade comprises pre-restart operations and a system restart performed subsequent to the pre-restart operations; activating a supervisor system of the first computing node in response to the first computing node completing the file system upgrade; and causing, in response to the activating, the supervisor system of the first computing node to initiate concurrent performance of the pre-restart operations of the file system upgrade at second computing nodes of the computing cluster, distinct from the first computing node.

Accelerated behavior change for upgrades in a distributed system is described herein. A method as described herein can include facilitating a file system upgrade of a first computing node of a computing cluster from a first file system version to a second file system version that is newer than the first file system version, wherein the file system upgrade comprises pre-restart operations and a system restart performed subsequent to the pre-restart operations; activating a supervisor system of the first computing node in response to the first computing node completing the file system upgrade; and causing, in response to the activating, the supervisor system of the first computing node to initiate concurrent performance of the pre-restart operations of the file system upgrade at second computing nodes of the computing cluster, distinct from the first computing node.

A method including retrieving, by a processor associated with a virtual private network (VPN) server, an initial operating system stored in a non-volatile memory, the initial operating system being associated with the VPN server providing VPN services; storing, by the processor, the initial operating system in a volatile memory; executing, by the processor, the initial operating system from the volatile memory to obtain a VPN operating system; storing, by the processor, the VPN operating system in the volatile memory; and executing, by the processor, the VPN operating system from the volatile memory to provide the VPN services. Various other aspects are contemplated.

In one embodiment, a system for managing a virtualization environment includes a set of host machines, each of which includes a hypervisor, virtual machines, and a virtual machine controller, and a data migration system configured to identify one or more existing storage items stored at one or more existing File Server Virtual Machines (FSVMs) of an existing virtualized file server (VFS). For each of the existing storage items, the data migration system is configured to identify a new FSVMs of a new VFS based on the existing FSVM, send a representation of the storage item from the existing FSVM to the new FSVM, such that representations of storage items are sent between different pairs of FSVMs in parallel, and store a new storage item at the new FSVM, such that the new storage item is based on the representation of the existing storage item received by the new FSVM.

A machine learning model is trained for user activity detection and context detection on a mobile device. The machine learning model is configured to learn a statistical relationship between an always-on sensing modality of the mobile device and actual user context. Rather than user annotations, the machine learning model is enhanced and personalized for the always-on sensing modality by automated annotations obtained from non-always-on sensing modalities. The non-always-on sensing modality opportunistically provides an imperfect label of user context, where the imperfect label has a known associated probability of error.

A method and apparatus for waking up a device, an electronic device, and a storage medium are provided, which are related to fields of image processing and deep learning. The method includes: acquiring an environment image of a surrounding environment of a target device in real time, and recognizing a face region of a user in the environment image; acquiring a plurality of facial landmarks in the face region, and acquiring a left eye image and a right eye image according to the facial landmarks; acquiring a left eye sight classification result and a right eye sight classification result according to the left eye image and the right eye image; and waking up the target device in a case of determining that the user is looking at the target device according to the left eye sight classification result and the right eye sight classification result.

The present specification discloses a digital device for performing a hibernation booting process and a control method therefor. Here, the digital device according to an embodiment of the present invention comprises: a first memory; a second memory storing a snapshot image generated on the basis of pieces of page data of the first memory; and a control unit for generating the snapshot image, wherein the control unit primarily deduplicates duplicated page data in the first memory and selectively secondarily deduplicates duplicated page data by comparing the duplicated page data with the snapshot image prestored in the second memory, wherein data fragmentation is minimized through the secondary deduplication step.

The present specification discloses a digital device for performing a hibernation booting process and a control method therefor. Here, the digital device according to an embodiment of the present invention comprises: a first memory; a second memory storing a snapshot image generated on the basis of pieces of page data of the first memory; and a control unit for generating the snapshot image, wherein the control unit primarily deduplicates duplicated page data in the first memory and selectively secondarily deduplicates duplicated page data by comparing the duplicated page data with the snapshot image prestored in the second memory, wherein data fragmentation is minimized through the secondary deduplication step.

Examples disclosed herein relate to a computing device that includes a central processing unit, a management controller separate from the central processing unit, and a security co-processor. The management controller is powered using an auxiliary power rail that provides power to the management controller while the computing device is in an auxiliary power state. The security co-processor includes device unique data. The management controller receives the device unique data and stores a representation at a secure location. At a later time, the management controller receives endorsement information from an expected location of the security co-processor. The management controller determines whether to perform an action on the computing device based on an analysis of the endorsement information and the stored representation of the device unique data.

Examples disclosed herein relate to a computing device that includes a central processing unit, a management controller separate from the central processing unit, and a security co-processor. The management controller is powered using an auxiliary power rail that provides power to the management controller while the computing device is in an auxiliary power state. The security co-processor includes device unique data. The management controller receives the device unique data and stores a representation at a secure location. At a later time, the management controller receives endorsement information from an expected location of the security co-processor. The management controller determines whether to perform an action on the computing device based on an analysis of the endorsement information and the stored representation of the device unique data.