Abstract: Disclosed are methods, systems and computer program products for optimizing computer system resource utilization during in-game resource farming. In some non-limiting embodiments or aspects, the present disclosure describes a method for optimizing computer system resource utilization during in-game resource farming, the method including detecting a gameplay state associated with an executing instance of a gaming application and based on the detected gameplay state selecting a gaming application mode from among a plurality of available gaming application modes. In some non-limiting embodiments or aspects, the method may also include implementing the selected gaming application mode for subsequent execution of the gaming application on the computing system.

Abstract: Disclosed here are systems and methods that allow users, upon detecting errors within a running workflow, to either 1) pause the workflow and directly correct its design before resuming the workflow, or 2) pause the workflow, correct the erred action within the workflow, resume running the workflow, and afterwards apply the corrections to the design of the workflow. The disclosure comprises functionality that pauses a single workflow and other relevant workflows as soon as the error is detected and while it is corrected. The disclosed systems and methods improve communication technology between the networks and servers of separate parties relevant and/or dependent on successful execution of other workflows.

Abstract: Aspects of the present disclosure involve systems, methods, devices, and the like for segmentation of the processor architecture platform. In one embodiment, a system and method are introduced which enable the use of a segmented platform in an extended network. The segmented platform is introduced for processing using standardized plugins enabling the use of processing and services available at the segmented network. In another embodiment, processing on the segmented platform can include the integration of microservices for the completion of the transaction.

Abstract: A system and method for detecting and fixing robotic process automation failures, including collecting tasks from at least one client computerized device, processing the tasks via robotic process automation, collecting tasks that failed to complete per task type, recording successful execution steps per each of the failed tasks, evaluating the recorded successful execution steps with respect to the failed task types, and providing selected execution steps that best fix the failed tasks, thereby fixing the robotic process automation failures.

Abstract: A container isolation method for a netlink resource includes receiving, by a kernel executed by a processor, a trigger instruction from an application program. The method also includes creating, by the kernel according to the trigger instruction, a container corresponding to the application program, creating a netlink namespace for the container, and sending a notification to the application program indicating that the netlink namespace is created. The method further includes receiving, by the kernel, a netlink message from the container, wherein the netlink message comprises entries generated when the container runs. The method additionally includes storing, by the kernel, the entries based on an identifier of the netlink namespace for the container, to send an entry required by the container to user space of the container.

Abstract: A unique identifier is stored in shared data storage that is accessible to at least a first virtual storage processor and a second virtual storage processor within a virtual storage appliance. The unique identifier is generated when the virtual storage appliance is first started up, and then used by the first virtual storage processor to obtain at least one Internet Protocol (IP) address for use by a management stack that initially executes in the first virtual storage processor. In response to failure of the first virtual storage processor, the unique identifier is used by the second virtual storage processor to obtain, for use by the management stack while the management stack executes in the second virtual storage processor after the failure, the same IP address obtained by the first virtual storage processor.

Abstract: Systems and methods are presented for creating encrypted containers in cloud computing environments. In one embodiment, a method is provided that includes receiving a request to create a virtual machine at an application node. The request may contain encryption parameters for use in encrypting the virtual machine. The virtual machine may be created at the application node and may include an associated memory for use during execution of the virtual machine. An encryption key may be received and the memory may be encrypted. An encrypted container image and may be mounted within the virtual machine. The encrypted container image may be executed within the virtual machine.

Abstract: A system may include a host processor, a coprocessor for accelerating tasks received from the host processor, and one or more memory dies mounted to the coprocessor. The coprocessor and the memory die may be part of an integrated circuit package. The memory die may convey configuration bit streams to one or more logic sectors in programmable circuitry of the coprocessor over through-silicon vias. Each logic sector may include one or more data registers that are loaded with configuration data from the memory die. Multiple data registers may be loaded with configuration data simultaneously. The configuration data may be loaded onto an array of configuration memory cells using the data registers. Multiple data registers may be pipelined to allow simultaneous loading of configuration data into multiple sub-arrays of the array of configuration memory cells.

Abstract: An SMI task to be completed across multiple SMI events. An OS agent can be employed to determine a current load on a computing device. Based on the load, the OS agent can create an SMI message that specifies a maximum duration for an SMI event and that segments the SMI data for the SMI task. The OS agent can provide the SMI message to BIOS as part of requesting that the SMI task be performed. During the resulting SMI event, the BIOS can reassemble the segmented SMI data and then perform the SMI task. If this processing cannot be completed within the specified maximum duration for an SMI event, the BIOS can pause its processing and cause a subsequent SMI event to occur during which the processing can be resumed. In this way, the SMI task can be completed across multiple SMI events while ensuring that no single SMI event exceeds the specified maximum duration.

Abstract: Techniques for scheduling operations for a task graph on a processing device are provided. The techniques include receiving a task graph that specifies one or more passes, one or more resources, and one or more directed edges between passes and resources; identifying independent passes and dependent passes of the task graph; based on performance criteria of the processing device, scheduling commands to execute the passes; and transmitting scheduled commands to the processing device for execution as scheduled.

Abstract: Methods and systems for rapid deployment of virtual computing resources are described. One method includes receiving a text file defining a collection of desired virtual computing resources, the text file including a plurality of text fields defining a project name, a virtual local area network (VLAN) and a plurality of virtual servers to be provisioned within the VLAN, an IP address to be assigned, a processing capability, and a definition of disks associated with the corresponding virtual server. The method also includes executing a scheduled job to deploy a plurality of virtual computing resources according to the text file. The scheduled job parses the text file, issues commands to a provisioning interface to provision virtual computing resources, and validates provisioning of the plurality of virtual computing resources based on status messages received from the provisioning interface indicating a result of provisioning in response to each command.

Abstract: In an acceleration processing method, an acceleration processing device combines a first acceleration application and a second acceleration application to obtain a first combined application, and burns the first combined application to a first acceleration resource, where the first combined application includes a top module, the first acceleration application, and the second acceleration application, and the top module includes a statement used to invoke the first acceleration application and a statement used to invoke the second acceleration application.

Abstract: Disclosed is a method for task pruning that can be utilized in existing resource allocation systems to improve the systems' robustness without requiring changing to existing mapping heuristics. The pruning mechanism leverages a probability model, which calculates the probability of a task competing before its deadline in the presence of task dropping, and only schedules tasks that are likely to succeed. Pruning tasks whose chance of success is low improves the chance of success for other tasks. Tasks that are unlikely to succeed are either deferred from current scheduling event or are preemptively dropped from the system. The pruning method can benefit service providers by allowing them to utilize their resources more efficiently and use them only for tasks that can meet their deadlines. The pruning method further helps end users by making the system more robust in allowing more tasks to complete on time.

Abstract: A plurality of requests are received for computing processing. At least some of the plurality of requests are replicated. The requests are replicated based on a fractional replication factor. Each received request and each replicated request are transmitted to a computer resource for processing. At least some embodiments provide the capability for meeting tail latency targets with improved performance and reduced cost.

Abstract: A first command is fetched for execution on a GPU. Dependency information for the first command, which indicates a number of parent commands that the first command depends on, is determined. The first command is inserted into an execution graph based on the dependency information. The execution graph defines an order of execution for plural commands including the first command. The number of parent commands are configured to be executed on the GPU before executing the first command. A wait count for the first command, which indicates the number of parent commands of the first command, is determined based on the execution graph. The first command is inserted into cache memory in response to determining that the wait count for the first command is zero or that each of the number of parent commands the first command depends on has already been inserted into the cache memory.

Abstract: A method of dynamically tuning a hypervisor includes detecting that a high-performance virtual machine was launched on the hypervisor. The method further includes, in response to the detecting, modifying, by a processing device, a configuration of the hypervisor to increase performance of the high-performance virtual machine on the hypervisor.

Abstract: Embodiments relate to a system, program product, and method for supporting elastic execution of a machine learning (ML) workload using application based profiling. A joint profile comprised of both ML application execution and resource usage data is generated. One or more feature(s) and signature(s) from the joint profile are identified, and a ML execution model for ML application execution and resource usage is built. The ML execution model leverages the feature(s) and signature(s) and is applied to provide one or more directives to subsequent application execution. The application of the ML execution model supports and enables the ML execution to elastically allocate and request one or more resources from a resource management component, with the elastic allocation supporting application execution.

Abstract: A system and method for dynamically determining parameters of crafting activities for individual users in a virtual space are disclosed. The crafting activity parameters may be determined based on rate of usage of the virtual space by the users. In some examples, a user usage rate of the virtual space may be determined to measure an amount of time a user spend in the virtual space during a period of real-world time. Based on the determined user usage rate of the virtual space, crafting activity parameters may be determined, for example, to balance differences in time spent in the virtual space by the users. In some implementations, for such determinations of the crafting activity parameters, one or more user normalization standards may be obtained.

Abstract: Exemplary embodiments described herein relate to a destination path for use with multiple different types of VMs, and techniques for using the destination path to convert, copy, or move data objects stored in one type of VM to another type of VM. The destination path represents a standardized (canonical) way to refer to VM objects from a proprietary VM. A destination location may be specified using the canonical destination path, and the location may be converted into a hypervisor-specific destination location. A source data object may be copied or moved to the destination location using a hypervisor-agnostic path.

Abstract: Systems and methods are disclosed for optimizing distribution of resources to data elements, comprising receiving one or more user-defined objectives associated with a group of data elements, wherein at least one of the user-defined objectives includes an objective related to a selected target group; receiving one or more constraints associated with the group of data elements, wherein at least one of the constraints comprises resources apportionable to each data element in the group of data elements; developing a first prediction of a performance of the group of data elements during a time period based on the one or more user-defined objectives and the one or more constraints; and apportioning at least a portion of the resources to each data element in the group of data elements based on the first prediction once the time period has started.