Abstract: A method for allocating resources for a machine learning model is disclosed. A machine learning model to be executed on a special purpose machine learning model processor is received. A computational data graph is generated from the machine learning model. The computational dataflow graph represents the machine learning model which includes nodes, connector directed edges, and parameter directed edges. The operations of the computational dataflow graph is scheduled and then compiled using a deterministic instruction set architecture that specifies functionality of a special purpose machine learning model processor. An amount of resources required to execute the computational dataflow graph is determined. Resources are allocated based on the determined amounts of resources required to execute the machine learning model represented by the computational dataflow graph.

Abstract: A method for managing specialized hardware resources includes obtaining, by a multi-scheme virtual allocation agent, a request for allocating a plurality of virtual resources to the specialized hardware resources in an information handling system, wherein the request specifies a first scheme of allocation, wherein the information handling system operates in a second scheme of allocation that is different from the first scheme of allocation, in response to the request: installing a first virtual resource of the plurality of virtual resources on a first specialized hardware resource of the specialized hardware resources, performing a dummy virtual resource installation to install a plurality of dummy variables based on the second scheme, installing a second virtual resource of the plurality of virtual resources on a second specialized hardware resource of the specialized hardware resources based on the second scheme, and deleting the plurality of dummy virtual resources from the hardware resources.

Abstract: Systems and methods for a confidential compute architecture integrated with direct swap caching are described. An example method for managing a near memory and a far memory includes, in response to determining that the far memory contains an encrypted version of a first block of data, retrieving from the far memory the encrypted version of the first block of data, decrypting the first block of data using a first key for exclusive use by a first virtual machine associated with the system, and providing a decrypted version of the first block of data to the requestor. The method further includes swapping out a second block of data having an address conflict with the first block of data from the near memory to the far memory, where the second block of data is encrypted using a second key for exclusive use by a second virtual machine associated with the system.

Abstract: The disclosure relates to a method, system and computer readable media for redesigning and redeploying a network service (NS) upon determining that a running instance of the NS fails to meet at least one NS requirement. The method comprises detecting a change in at least one input model of a NS design; executing a traceability analysis for the change in the at least one input model of the NS design; and upon determining, based on the traceability analysis, that the NS fails to meet the at least one NS requirement, redesigning and redeploying the NS.

Abstract: A mainframe processing system comprises a memory operable to store data transformation instructions and one or more processors, at least one of the one or more processors operably coupled to the memory. The one or more processors are configured to receive batch processing data from one or more data sources, transform the batch processing data into a plurality of mainframe processes based on the data transformation instructions, and dispatch each mainframe process to a respective mainframe site. The batch processing data received by the processor indicates a plurality of batch processes formatted for mainframe processing as a batch. Each batch process is transformed to a respective mainframe process formatted for mainframe processing on an individual basis.

Abstract: A system for providing a safety critical operating environment container architecture, the system including a computing device having a memory communicatively connected to at least one multi-core processor, the memory containing instructions configuring the processer to receive at least a software module to be executed on the at least one multi-core processor, create a separate virtual environment for the at least a software module, wherein creating the virtual environment further includes generating a virtualization layer and allocating a dedicated private static memory space through the virtualization layer, wherein the separate virtual environment includes a dedicated operating system, integrate the at least a software module into the virtual environment by instantiating, the software module into at least one software container, wherein the at least one software container includes a plurality of dedicated software packages and execute the software module on each software container for each virtual environm

Abstract: An example system includes a node and a scheduler. The scheduler is configured to deploy a first workload for execution on the node. The scheduler is also configured to reserve a first amount of resources of the node for the first workload based on a determination that the first workload is running in a transient state. The scheduler is also configured to reduce the first amount of the resources of the node reserved for the first workload based on a determination that the first workload is running in a non-transient state.

Abstract: A computing device may include a memory and a processor cooperating with the memory and configured to provide first and second application layers that include different versions of a virtual application accessible by a client device. The first and second versions of the virtual application are isolated from each other in their respective application layers, each with separate application libraries.

Abstract: A virtual machine (VM) management method may involve simulating a change in deployment of VMs deployed on physical servers including a first physical server and a second physical server physically separated from the first physical server. Server workload prediction based on possible VM deployment may be used for scheduling deployment of VMs to obtain a satisfactory deployment of the VMs on the servers. This may involve classification of storage loads of the VMs, forming a prediction model for predicting prediction load based on storage loads in a classification, and selecting a prediction model for use in determining a target prediction load on a physical server as a result of target VM being analyzed.

Abstract: A system and method for inspecting different types of cloud workloads for cybersecurity threats, all deployed in a cloud computing environment, includes a unifying extractor to expose different compute types to agnostic inspectors. The method includes accessing a first cloud workload of a first type from a plurality of deployed cloud workloads; accessing a second cloud workload of a second type from the plurality of deployed cloud workloads; extracting data from each of the first cloud workload and the second cloud workload into a storage layer having a data schema, based on a predefined data structure; and inspecting the extracted data to detect a first target object, the target object indicating a cybersecurity threat, wherein extraction for each of the first cloud workload and the second cloud workload is based on the workload type.

Abstract: Disclosure is made of methods, apparatus and system for clustering processes for use by a cloud platform. Process clustering may include receiving traffic data transmitted and received between each pair of processes in a set of processes. A matrix may be generated based on the traffic data, the matrix including a row and a column for each process in the set of processes. The matrix may be hierarchically clustered based on the traffic data, the hierarchical clustering outputting a plurality of clusters, each cluster including one or more processes in the set of processes. The plurality of clusters may then be merged into a set of merged clusters of processes.

Abstract: A Non-Uniform Memory Access (NUMA) node virtual machine provisioning system includes a connection system and a physical NUMA node coupled to a NUMA node virtual machine provisioning subsystem that modifies NUMA node information in at least one database to create a first virtual NUMA node that is provided by a first subset of NUMA node resources in the physical NUMA node, modifies connection system information in the at least one database to dedicate a first subset of connection system resources in the connection system to the first virtual NUMA node, and deploys a first virtual machine on the first virtual NUMA node such that the first virtual machine performs operations using the first subset of NUMA node resources that provide the first virtual NUMA node, and using the first subset of connection system resources dedicated to the first virtual NUMA node.

Abstract: The techniques disclosed herein enable systems to safely deploy a plurality of upgrade variants to different resource units that provide a service by utilizing small-scale deployment and validation. To deploy upgrade variants, a system receives a selection of upgrade variants from a feature group and automatically selects an appropriate set of resource units at which to deploy the upgrade variants. The system is further configured to collect and analyze telemetry data from the set of resource units to determine if any problems have occurred as a result of the deployed upgrade variants. By analyzing the telemetry data, the system can also identify one or more upgrade variants that are causing the problems. In response, the system can remove the identified variants and proceed with deployment of the remaining upgrade variants.

Abstract: A set of deployment applications that are configured to deploy a plurality of resources that compose a containerized application onto at least one cluster of a plurality of different clusters is identified. Each cluster includes a plurality of nodes onto which the plurality of resources can be deployed. A set of clusters of the plurality of clusters onto which the plurality of resources have been deployed as a plurality of deployed resources is identified. User interface imagery that includes information that: identifies a set of deployed resources of the plurality of deployed resources of the containerized application; identifies a total number of deployment applications in the set of deployment applications; and identifies a total number of clusters in the set of clusters is generated. The user interface imagery is presented on a display device.

Abstract: A system may forecast a workload for a cluster of nodes in a database management system. The system may generate a reconfiguration plan based on the forecasted workload. The system may obtain a heterogenous configuration set. The heterogenous configuration set may include respective configuration sets for the complete sets of nodes. The system may forecast, based on a first machine learning model, respective performance metrics for nodes in each of the complete sets. The system may forecast a cluster performance metric for the entire cluster of nodes based on a second machine learning model. The system may include, in response to satisfaction of an acceptance criterion, the heterogenous configuration set in the reconfiguration plan. The system may cause the cluster of nodes to be reconfigured based on the reconfiguration plan.

Abstract: In an embodiment, a computer-implemented method for dynamically exchanging runtime state data between datacenters with a gateway using a controller bridge is disclosed. In an embodiment, the method comprises: receiving one or more first runtime state data from one or more logical sharding central control planes (“CCPs”) controlling one or more logical sharding hosts; receiving one or more second runtime state data from a gateway that is controlled by a CCP that also controls one or more physical sharding hosts; aggregating to aggregated runtime state data, the one or more first runtime state data received from the one or more logical sharding CCPs and the one or more second runtime state data received from the gateway; determining updated runtime state data based on the aggregated runtime state data, the one or more first runtime state data, and the one or more second runtime state data; and transmitting the updated runtime state data to at least one of the one or more logical sharding CCPs and the gateway.

Abstract: In an approach to providing virtualized hardware resources, one or more reconfigurable hardware devices (RHDs) are configured to instantiate a first virtual System-on-Chip (vSoC) instance of one or more vSoC instances, the first vSoC instance implementing at least one virtualized system component.

Abstract: The method and technique involves calculating workload usage models from multiple data sources for IoT backbone infrastructure platforms used in device-to-cloud communication. Based on these built models, the simulator uses virtual connected devices to predict machine size, number of machines, storage & network resource options required for the IoT backbone. Validated sets are then benchmarked data is then fed to a machine learning algorithm, which then recommends optimal outcomes for cloud based IoT backend platforms including machine sizes, number of machines, storage & network options and costs across various cloud providers like AWS, GCP & Azure.

Abstract: Devices and techniques are generally described for an edge computing system. In various examples, the edge computing system may comprise a host kernel comprising a kernel-based virtual machine hypervisor. In some examples, the edge computing system may comprise virtualization software effective to communicate with the kernel-based virtual machine hypervisor to execute guest virtual machines. In various further examples, the edge computing system may comprise an engine virtual machine with access to at least one hardware component. The edge computing system may further comprise a control plane virtual machine. The control plane virtual machine may include components effective to receive a first request and determine an application corresponding to the first request, and a virtual machine manager effective to control the virtualization software to generate a virtual machine, configured in accordance with a configuration specific for the virtual machine, for executing the application.

Abstract: There is provided a remote work supporting apparatus and method capable of checking work content of each operator and thereby supporting remote work while preventing the occurrence of human errors. A remote work supporting system for supporting work for a target computer apparatus to be executed remotely by a plurality of operators, and a remote work supporting method executed by the remote work supporting system are designed to: create virtual machines for providing a virtual computer environment for the target computer apparatus by respectively associating the virtual machines with the respective operators and provide each of the created virtual machines to each of the corresponding operators; and monitor respective pieces of work content regarding the virtual machines corresponding to the respective operators, presume correct work content from among the respective pieces of work content, and cause the presumed work content to be reflected in the target computer apparatus.