An edge network computing system includes: a plurality of terminal devices; a plurality of edge servers connected to the terminal device through an access network; and a plurality of cloud servers connected to the plurality of edge servers through a core network. Each edge server is configured to: receive a plurality of computing tasks originated from one of the plurality of terminal devices; use a deep Q-learning neural network (DQN) with experience replay to select one of the plurality of could servers to offload a portion of the plurality of computing tasks; and send the portion of the plurality of computing tasks to the selected cloud server and forward results of the portion of the plurality of computing tasks received from the selected cloud server to the originating terminal device.

During a boot process of a computing device, a boot loader loads a kernel and an initial RAM disk image from a persistent storage device into RAM. The initial RAM disk image includes a file system that includes a camera application. The kernel is invoked, and the kernel mounts a RAM disk from the initial RAM disk image as a root file system. The kernel causes an initiation of the camera application into a user space. The camera application obtains an image frame from a camera. The camera application processes the image frame to generate a processed image frame, and provides the processed image frame to a frame buffer for presentation of the processed image frame on a display device.

In an example implementation according to aspects of the present disclosure, a method may include receiving, at a computing device, an attempt to authenticate a user via an authentication event and, upon validating the authentication event used, determining which authentication profile to initiate for the user to use the computing device. As an example, the computing device then initiates the determined authentication profile to launch a customized set of programs on the computing device for the user.

In an accelerator control system (100), a general-purpose server (110) includes a digest information generation unit (1112) that binarizes an accelerator function to generate first digest information (130) of the accelerator function and a server management control unit (1111) that compares the first digest information (130) created before the accelerator function is implemented on an FPGA function unit (122) with second digest information (130) notified from an accelerator board (120) and determines whether the accelerator function is rewritten, and the accelerator board (120) includes a digest information generation unit (1212) that generates the second digest information (130) of the accelerator function written in the FPGA function unit (122), and an FPGA management control unit (1211) that notifies the general-purpose server (110) serving as a rewriting source of the second digest information (130) generated.

Embodiments of the present disclosure describe a memory reclaiming method and a terminal. As discussed with respect to the embodiments described herein, the method may include determining, by a terminal according to a preset rule, a target application program in application programs run on a background, where the target application program is an application program that needs to be cleaned. The method may also include freezing, by the terminal, the target application program, and reclaiming data generated during running of a process of the target application program in memory. The method may also include unfreezing, by the terminal when receiving an input triggering instruction for the target application program, the target application program, and running the target application program.

During a boot process of a computing device, a boot loader loads a kernel and an initial RAM disk image from a persistent storage device into RAM. The initial RAM disk image includes a file system that includes a camera application. The kernel is invoked, and the kernel mounts a RAM disk from the initial RAM disk image as a root file system. The kernel causes an initiation of the camera application into a user space. The camera application obtains an image frame from a camera. The camera application processes the image frame to generate a processed image frame, and provides the processed image frame to a frame buffer for presentation of the processed image frame on a display device.

Software performance can be automatically managed in a distributed computing environment. In one example, a system that can receive metrics information describing resource usage by a first instance of a service in a distributed computing environment. The system can also determine a quality-of-service (QoS) constraint for the service. The system can then modify a definition file based on the metrics information and the QoS constraint, the definition file being configured for deploying instances of the service in the distributed computing environment. The system can deploy a second instance of the service in the distributed computing environment using the modified definition file. As a result, the second instance can more closely satisfy the QoS constraint than the first instance.

The technology disclosed herein provides a method including generating, at a PCIe interface, a functional capability table associating a plurality of computational functionalities (CFs) with a plurality of computational storage devices (CSDs) communicatively connected to each other via the PCIe interface, communicating the functional capability table to each of the plurality of CSDs, receiving, at a first of the plurality of CSDs (CSD1), a request for a computational process including the plurality of CFs, determining that a first of the CFs (CF1) is associated with the CSD1 and a second of the CFs (CF2) is associated with a CSD2, requesting a computational program memory (CPM2) at the CSD2 and performing the CF1 at CSD1.

Disclosed is an application cleaning method and device, a storage medium and an electronic device. In the embodiments of the present disclosure, the method involves: collecting multi-dimensional features of an application as samples, and constructing a sample set of the application; according to information gain ratios of the features with regard to sample classification, carrying out sample classification on the sample set so as to construct a decision tree model of the application; according to a prediction time, collecting corresponding multi-dimensional features of the application as prediction samples; and according to the prediction samples and the decision tree model, predicting whether the application can be cleaned.

A system for unloading tables of a database is provided. In some aspects, the system performs operations including determining that a number of accesses to a table occurring within a time period has satisfied an access threshold. The operations may further include identifying, in response to the determining, a first timestamp indicating a most recent access to the table. The operations may further include determining whether a difference between a current timestamp and the first timestamp satisfies a first time threshold. The operations may further include comparing, in response to the difference satisfying the first time threshold, a ratio of the difference and a size of the table to a ratio threshold. The operations may further include unloading, in response to satisfying the ratio threshold, the table. The operations may further include adjusting, based on the feedback, the first time threshold and/or the ratio threshold.