Abstract: Disclosed herein a method for transforming a single processor system into an effective multicore system with few modifications to the existing processor. The transformation is achieved by wrapping the processor with a CPU Manager module, which intercepts all CPU transactions, remaps addresses, manages interrupt lines, and controls the CPU clock using clock gating. The transformation to n effective multicore system brings about reduced area and power impacts compared to a full duplication of the whole system, while still reusing the existing program in a multicore environment.

Abstract: A system-on-chip includes; a first virtual machine, a second virtual machine, a first memory controller including a first mailbox, and configured to control operation of a memory device under control of the first virtual machine, and a second memory controller including a second mailbox, and configured to control operation of the memory device under control of the second virtual machine, wherein the first mailbox and the second mailbox are directly connected in hardware via a bus.

Abstract: Managing the resource demand load for edge systems is significantly more complex than for other systems, such as cloud environments. Edge resource demand load scheduling systems and methods are disclosed that can ensure that edge systems operate smoothly and efficiently while balancing multiple scheduling objectives. Scheduling techniques disclosed herein may utilize heuristic rules for candidate edge system selection (e.g., utilizing ARMA/ARIMA averages and/or service level objectives) and modified best fit decreasing (mBFD) assignment/allocation techniques.

Abstract: Embodiments of the present disclosure include techniques for machine language processing. In one embodiment, the present disclosure includes configuring functional modules on a machine learning processor to execute a plurality of machine learning (ML) operations during a plurality of time segments. During the time segments, a first portion of the ML operations execute serially and at least one other ML operation executes during at least a majority of the time of each of the time segments. Serial ML operations may be processed simultaneously with the at least one other ML operation.

Abstract: Managing the resource demand load for edge systems is significantly more complex than for other systems, such as cloud environments. Embodiments herein provide edge resource demand load estimation systems and methods that inform scheduling and associated edge orchestration to ensure that edge system resource capacity is appropriately utilized. Efficient utilization allows an increased number of applications to be deployed at a reduced level of reserved resources. Also presented are embodiments of assurance mechanisms for monitoring edge resource demand load characterizations. In one or more embodiments, when an estimate or estimates are deemed to not be valid (e.g., having experienced stationary drift), updated estimates may be obtained.

Abstract: A computing platform comprising a plurality of disaggregated data center resources and an infrastructure processing unit (IPU), communicatively coupled to the plurality of resources, to compose a platform of the plurality of disaggregated data center resources for allocation of microservices cluster.

Abstract: Managing the resource demand load for edge systems is significantly more complex than for other systems, such as cloud environments. A time period in which an application or task is operating based on initial demand resource load values that are provided by a customer may be inaccurate, which may expose sub-standard execution. Embodiments herein seek to significantly mitigate the potential of sub-standard execution. Embodiments collect a repository of resource demand load usage data over a time period that can be used to accurately determine the statistical moments of uncertain resource demand load. In one or more embodiments, a repository of hypervector and/or hyperspace representations may be generated and used to help with resource demand load estimation.

Abstract: Managing the resource demand load for edge systems is significantly more complex than for other systems, such as cloud environments. A time period in which an application or task is operating based on initial demand resource load values that are provided by a customer may be inaccurate, which may expose sub-standard execution. Embodiments herein seek to significantly mitigate the potential of sub-standard execution. Embodiments collect a repository of resource demand load usage data over a time period that can be used to accurately determine the statistical moments of uncertain resource demand load. In one or more embodiments, a repository of hypervector and/or hyperspace representations may be generated and used to help with resource demand load estimation.

Abstract: Converting shared libraries is provided. A shadow shared library is generated based on symbol information of each respective symbol of an original shared library. A symbol receiver that corresponds to the shadow shared library is generated. The symbol receiver corresponding to the shadow shared library and the original shared library are deployed as a microservice on a set of servers.

Abstract: Embodiments herein describe a processor system that includes an integrated, adaptive accelerator. In one embodiment, the processor system includes multiple core complex chiplets that each contain one or processing cores for a host CPU. In addition the processor system includes an accelerator chiplet. The processor system can assign one or more of the core complex chiplets to the accelerator chiplet to form an IO device while the remaining core complex chiplets form the CPU for the host. In this manner, rather than the accelerator and the CPU having independent computer resources, the accelerator can be integrated into the processor system of the host so that hardware resources can be divided between the CPU and the accelerator depending on the needs of the particular application(s) executed by the host.

Abstract: Techniques described herein relate to a method for managing a distributed multi-tiered computing (DMC) environment. The method includes normalizing, by a local controller associated with an DMC domain, task resource demand dimensions for each task associated with a scheduling job; summing the resource demand dimension for each task to generate resource demand vectors; classifying tasks based on priority; sorting tasks based on associated resource demand vectors; obtaining critical path, earliest start time, and latest start time associated with each task; sorting tasks based on critical path and earliest start time; and generating scheduling assignments based on the priority, capacity of devices in a final candidate list, resource demand vectors, earliest start time, and the critical path.

Abstract: A data management method, apparatus, device, a computer-readable storage medium, and system are provided. The method is applied to a first network device, the first network device determines target invalid data existing in a second network device, the first network device obtains a first RPC, and the first RPC carries information about target invalid data that needs to be deleted. Then, the first network device sends the first RPC to the second network device, where the first RPC is used to indicate the second network device to delete the target invalid data. In this application, residual data is automatically identified, and the residual data is cleared, to automatically delete the residual data in batches, thereby saving device resources and improving an operation and maintenance management effect.

Abstract: A method for managing a production environment includes obtaining container information associated with application containers in the production environment, in response to the obtaining: performing an attributes analysis on a set of data attributes of the container information to obtain an initial container grouping, assigning, based on the attributes analysis, a set of migration sensitivity tags to each of the application containers, generating a migration strategy based on the set of migration sensitivity tags, and initiating a migration of at least a portion of the application containers based on the migration strategy.

Abstract: An information handling system may include at least one processor and a memory. The information handling system may be configured to assign a workload to a particular node selected from among a plurality of nodes of an information handling system cluster by: determining scores for each respective node of the plurality of nodes based on hardware metrics of the respective nodes; based on the determined scores, selecting a particular node having a highest score; and causing the workload to execute on the particular node.

Abstract: Computer-implemented methods and systems for integrating computer applications are disclosed. One method includes querying a primary computer application for current state of a newly created object; receiving the current state of the object, and generating object data for a secondary computer application based on the current state of the object. The method further includes communicating an object creation request to a secondary computer application, the object creation request including the generated object data, receiving a secondary computer application object identifier from the secondary computer application upon creation of the object at the secondary computer application, and communicating the secondary computer application object identifier to the primary computer application for storing in a record of the object created at the primary computer application.

Abstract: There is provided a computer-implemented method comprising, at an intermediary process acting as an intermediary between a publisher system and a plurality of subscriber processes, the intermediary process having a publisher data store storing one or more subscriptions for which the intermediary process is a publisher and a subscriber data store storing one or more subscriptions for which the intermediary process is a subscriber.

Abstract: Techniques for standardizing a catalog of data and for using the standardized data to implement various APIs are disclosed. Non-standardized data is received. This data includes information describing items, customer information, and unstructured review data. The non-standardized data is converted to a standardized format, resulting in the generation of standardized data. The standardized data includes a hierarchy of defined categories. Each category is associated with a set of attribute types. The standardized data also includes anonymized profiles. The unstructured review data is also provided structure. A data model is generated based on the standardized data. Various APIs can then use the data model to perform operations.

Abstract: There are provided systems and methods for managing data dependencies in an N-layer architecture for data loading optimizations. A service provider, such as an electronic transaction processor for digital transactions, may utilize different decision services that implement rules and/or artificial intelligence models for decision-making of data including data in production computing environment. Decision services may be used for data processing and decision-making, where multiple decision services may be invoked during run-time in order to complete a data processing request. When processing data, data loads may be required by decision services, where multiple data loads that are the same or similar may be utilized by different data services. Thus, the service provider may provide data loading optimization by making these data loads available across multiple decision services. This may be done based on an intelligent and/or algorithmic process based on data storage requirements.

Abstract: Embodiments for executing selected remediation scripts to remedy issues in a data storage system through an API/script recorder component or process that presents a DVR (digital video recorder)-like interface and capability to capture scripts and API calls as they are used when navigating a graphical user interface (GUI). Internal components of this function perform the recording and macro creation to allow constant playback of these recorded and selected scripts. By easily generating and managing scripts macros, system administrators can easily re-execute customer workflows to apply known fixes to current issues in the data storage system.

Abstract: A processing system is in a host center configured to communicate with edge computers each of which is mounted on one of a plurality of moving bodies, and the processing system is configured to perform host processing on a service data set related to a cloud service. The processing system includes a processor configured to set divided data of the service data set, and search, for each of the divided data, the edge computer that satisfies a necessary condition required for performing distribution processing on the divided data by monitoring whether each of the edge computers satisfies the necessary condition.