Abstract: Method, computer program product, and system to prioritize the execution of processing task requests in a task request queue, where the processing task requests are related to an environment. The method includes adding processing task requests to a task request queue in response to the detection of predefined actions in an environment. The method also includes adding additional processing task requests or adjusting a priority level of not yet completed task requests in the task request queue, in response to detecting subsequent predefined actions in the environment.

Abstract: A system may include multiple computing nodes, each including a hypervisor, a controller virtual machine and multiple virtual machines. The hypervisor may include a host agent configured to start a service and determine whether a performance of the service has met a criteria. If the performance of the service has met the criteria, the hypervisor may further determine whether the service has any pending critical operations, and if no critical operations are pending, stop the service. In some examples, each service may create a process configured to monitor the performance of the service. Examples of the performance of the service may include memory utilization and the service response time.

Abstract: Methods and systems can be used for swapping running executables in a container. A container orchestrator process receives a request to run an application in a container. A first execution of the application is initiated, in the container, in a first child process of the container orchestrator process. The container orchestrator process listens for file changes for the application. At least one file change is determined for the application, and in response to determining the at least one file change for the application, a second execution of the application is initiated in the container, without restarting the container, using the at least one file change, in a second child process of the container orchestration process. After initiating the second execution of the application in the container, the first child process is terminated.

Abstract: Technology for enabling a hypervisor to perform copy on write features on encrypted storage of a virtual machine. An example method may involve: receiving, by a guest program from a hypervisor, an indication that identifies a first storage block of a first virtual machine, wherein the first storage block is write protected by the hypervisor; identifying, by the guest program, a second storage block of a second virtual machine; and copying, by the guest program, data of the first storage block to the second storage block, wherein the data of the first storage block and data of the second storage block are encrypted using different cryptographic inputs.

Abstract: An embodiment includes establishing a demand threshold for adding a locale to an installation list based at least in part on selection data indicative of an input from a user. The embodiment also includes monitoring user inputs to a cloud computing environment so as to detect user inputs that include requests to install the locale. The embodiment also includes dynamically updating a demand score associated with the locale based on ongoing detection of locale installation requests during the monitoring of user inputs. The embodiment also includes automatically adding the locale to the installation list upon detecting that the demand score satisfied the demand threshold.

Abstract: Techniques are described for generating a programming score for a user based on source code submissions for a programming task set. In an embodiment, a programming system generates, at random, a programming task set for submission by a user. The programming system receives source codes submitted for the programming task set in a programming session with a client computer system of a user. The programming system executes the source codes to evaluate their accuracy. Based on the execution, the programming system determines that some source codes are evaluated to be accurate thereby solving corresponding programming tasks from the programming task set. Based on such an execution, programming system determines the programming score for the particular user for the programming task set.

Abstract: Embodiments of the present disclosure relate to a method, an electronic device, and a computer program product for controlling a virtual machine. The method comprises: determining external information related to the running of the virtual machine, the external information indicating a first period to be occupied by the virtual machine to process data in association with another virtual machine; determining internal information related to the running of the virtual machine, the internal information indicating a second period to be occupied by the virtual machine to process internally stored data; determining a stop period of the virtual machine based on the external information and the internal information; and controlling the virtual machine to stop running during the stop period.

Abstract: A method may include obtaining training code and extracting features from the training code. The extracted features of the training code may be mapped to natural language code vectors by a deep neural network. A natural language search query requesting source-code suggestions may be received, and the natural language search query may be mapped to a natural language search vector by the deep neural network. The method may include mapping the natural language search query to the natural language search vector in the same or a similar method as mapping the extracted features of the training code to natural language code vectors, and the natural language search vector may be compared to the natural language code vectors. Source code responsive to the natural language search query may be suggested based on the comparison between the natural language search vector and the natural language code vectors.

Abstract: This disclosure relates to an improved software development platform that enables the dynamic generation and deployment of advanced electronic forms. The platform includes graphical user interfaces that enable users to drag-and-drop or otherwise insert components into a development window to define the content, functionality, workflow, background processes, and other criteria for the electronic forms. The components may correspond to pre-defined or pre-packaged software bundles that can be integrated into the electronic forms to define input fields, design layouts, application functions, preconfigured templates, and background processes. After electronic forms are completed, options can be selected to deploy the forms in a cloud-based environment or in other ways.

Abstract: In an approach to risk prediction for bug-introducing changes, a computer retrieves one or more historic pull requests. A computer determines a unique file linking for each file included in the historic pull requests. A computer generates a file risk dataset. A computer performs chronological partitioning on the file risk dataset. A computer determines bug-introducing changes in the file risk dataset. A computer computes a collaborative file association between two or more of the files in the file risk dataset. A computer labels each of the files in the file risk dataset with an associated risk of introducing a bug. A computer generates a labelled file risk inducing ground truth dataset. A computer inputs the labelled file risk inducing ground truth dataset to a file risk prediction model. A computer extracts pull request features from the historic pull requests. A computer generates a pull request risk prediction model.

Abstract: Container images are managed in a clustered container host system with a shared storage device. Hosts of the system include a virtualization software layer that supports execution of virtual machines (VMs) in the hosts, and one or more VMs have implemented therein a container engine that supports execution of containers within the respective VMs. Deploying a container in a first VM includes creating a virtual disk in the storage device, storing a container image in the virtual disk, mounting the virtual disk to the first VM, and updating a metadata cache to associate the container image to the virtual disk. Deploying the container in a second VM executed in a host different from a host in which the first VM is executed, includes checking the metadata cache to determine that the container image is stored in the virtual disk, and mounting the virtual disk to the second VM.

Abstract: A digital content management system for using an enterprise resource planning (ERP) system converted from a legacy ERP using a shell conversion technique is provided. The system may include a final-converted ERP that communicates with one or more servers and one or more data stores in a network.

Abstract: A procedure generation system receives an input of a configuration definition, and generates an operation procedure for the configuration definition, the configuration definition including definitions of a plurality of state elements. The plurality of state elements includes a state element having a limiting condition that a value of a property within a definition of a state element may be referenced when the state element is in a predetermined state. The procedure generation system includes an appender that adds a stipulation that a state element having the limiting condition be in the predetermined state, to a condition of a state transition of a predetermined state element in which a property within a definition of a state element having the limiting condition is referenced.

Abstract: In some examples, a storage medium stores information relating to reset ports associated with respective virtual machines (VMs) of a plurality of VMs. A controller detects, based on the information, an activation of a first reset port associated with a first VM of the plurality of VMs. In response to the detecting, the controller provides an indication of the activation of the first reset port to a hypervisor that is separate from the controller, the indication to cause the hypervisor to reset the first VM.

Abstract: Disclosed in some examples are memory devices which include electrically programmable elements that specify values for one or more firmware search parameters for use by the bootloader in locating and reading the firmware object. The values of the firmware search parameters may be dynamically selected at manufacturing time by modifying the configuration of the electrically programmable elements by applying or not applying a specified voltage to the electrically programmable elements. In some examples, an electrically programmable element may include: a fuse, an anti-fuse, and/or an e-fuse.

Abstract: According to some embodiments, an automated provisioning system may receive a customer demand associated with an application to be executed in a cloud-based computing environment. The automated provisioning system may include a process allocator to communicate with Virtual Machine (“VM”) and container provisioners and determine cluster data. A machine learning based microservice setup platform, coupled to the automated provisioning system, may receive the cluster data and information about the customer demand. The machine learning based microservice setup platform may then execute policy rules based on the cluster data (and information about the customer demand) and generate a recommendation for the customer demand. The automated provisioning system may then assign the customer demand to one of a VM-based infrastructure and a container-based infrastructure in accordance with the recommendation generated by the machine learning based microservice setup platform.

Abstract: Throughput capacity may be changed to sustain throughput for accessing individual items in a database. A table hosted at storage nodes that provide access to the table in a database may be identified as allocated with a client-specified throughput capacity for accessing the table. Performance of access requests to the table at the storage nodes may be tracked. Based on the performance of the access requests, a change may be determined that modifies a throughput capacity for the table to sustain a guaranteed throughput for each access request independent of other access requests received for the table.

Abstract: An apparatus comprises a processing device configured to monitor development of a plurality of software applications, to identify a first one of the plurality of software applications that has a given version ready for deployment to a production environment of an information technology infrastructure, and to determine at least one dependency between the given version of the first one of the plurality of software applications and a given version of at least a second one of the plurality of software applications. The processing device is further configured to control deployment of the given version of the first one of the plurality of software applications in the production environment of the information technology infrastructure based at least in part on evaluating a deployment status of the given version of the second one of the plurality of software applications.

Abstract: Functionality is provided for profiling code by unwinding stacks in frame-pointer omitted executables using C++ exception stack unwinding information. Information is extracted from executable files, and used to optimize stack unwinding operations. In at least one embodiment, the system uses information that has been included for exception handling. Storage of such information can be optimized by exploiting patterns in stack deltas.

Abstract: Implementations detailed herein include description of a computer-implemented method. In an implementation, the method at least includes receiving an application instance configuration, an application of the application instance to utilize a portion of an attached accelerator during execution of a machine learning model and the application instance configuration including: an indication of the central processing unit (CPU) capability to be used, an arithmetic precision of the machine learning model to be used, an indication of the accelerator capability to be used, a storage location of the application, and an indication of an amount of random access memory to use.