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: According to an embodiment, a processing apparatus includes a memory and a processor coupled to the memory. The processor is configured to: execute data access that is at least one of data writing to the memory and data reading from the memory; receive access control information for controlling timing of the data access to be executed; and control the timing of the data access based on the received access control information.

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: Techniques are described for enabling users of a cloud provider network to request the conditional termination of computing resources based on a forecasted availability of replacement capacity at a specified time or range of time in the future. A cloud provider network provides an application programming interface that can be used to make such requests, where the computing resources are hosted by the cloud provider network as part of a capacity pool shared by tenants of the cloud provider network. This type of request can be generated, for example, by a user desiring to terminate the use of some number of unproductive computing resources only if the user can be reasonably assured by the cloud provider network that capacity will be available at a future time when the user will likely need the capacity again.

Abstract: The present disclosure relates to systems, methods, and computer readable media for predicting deployment growth on one or more node clusters and selectively permitting deployment requests on a per cluster basis. For example, systems disclosed herein may apply tenant growth prediction system trained to output a deployment growth classification indicative of a predicted growth of deployments on a node cluster. The system disclosed herein may further utilize the deployment growth classification to determine whether a deployment request may be permitted while maintaining a sufficiently sized capacity buffer to avoid deployment failures for existing deployments previously implemented on the node cluster. By selectively permitting or denying deployments based on a variety of factors, the systems described herein can more efficiently utilize cluster resources on a per-cluster basis without causing a significant increase in deployment failures for existing customers.

Abstract: Methods, apparatus, systems and machine-readable storage media of an edge computing device using an edge server CPU with dynamic deterministic scaling is disclosed. A processing circuitry arrangement includes processing circuitry with processor cores operating at a center base frequency and memory. The memory includes instructions configuring the processing circuitry to configure a first set of the processor cores of the CPU to switch the operating at the center base frequency to operating at a first modified base frequency, and a second set of the processor cores to switch the operating at the center base frequency to operating at a second modified base frequency. A same processor core within the first set or the second set can be configured to switch operating between the first modified base frequency or the second modified base frequency.

Abstract: Among other things, a machine-based method comprises receiving an application specification comprising one or more algorithms. Each algorithm is not necessarily suitable for concurrent execution on multiple nodes in parallel. One or more different object classes are grouped into one or more groups, each being appropriate for executing the one or more algorithms of the application specification. The executing involves data that is available in objects of the object classes. A user is enabled to code an algorithm of the one or more algorithms for one group in a single threaded environment without regard to concurrent execution of the algorithm on multiple nodes in parallel. An copy of the coded algorithm is distributed to each of the multiple nodes, without needing additional coding. The coded algorithm is caused to be executed on each node in association with at least one instance of a group independently of and in parallel to executing the other copies of the coded algorithm on the other nodes.

Abstract: In one aspect, a micro-service control architecture provides a modular, flexible platform for designing, diagnosing, updating and/or expanding process control systems. Each service is containerized to provide portability and isolation from other components of the process control system. In another aspect, a function block diagram includes a “shadow” block that acts as an interface to an external, custom calculation engine, thereby enabling the custom calculation engine to operate synchronously with respect to other function blocks of the function block diagram.

Abstract: Provided are techniques for collecting capacity data of virtual machines by leveraging agent data. A list of one or more virtual machines for which capacity data is to be retrieved is obtain from an asset server, where the one or more virtual machines are identified using a licensing measurement. Infrastructure data and hypervisor data are obtained from an infrastructure server. For each of the one or more virtual machines, the infrastructure data and the hypervisor data are used to request, from a capacity scanner on each of the one or more virtual machines, capacity data for that virtual machine. The capacity data for each of the one or more virtual machines is sent to the asset server, and the asset server performs load balancing of software that is to be executed on the one or more virtual machines using the capacity data.

Abstract: Provided is a virtual machine (VM) management method of 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 and scheduling deployment of VMs and predicting workload of VMs.

Abstract: Systems and methods for dynamically moving virtual machine (VM) data based upon context are described. In some embodiments, an Information Handling System (IHS) may include a processor and a memory coupled to the processor, the memory having program instructions stored thereon that, upon execution, cause the IHS to: select a VM having a plurality of VM files; identify, among the plurality of VM files, a movable VM file; and transfer the movable VM file from a first storage tier to a second storage tier based upon a usage classification associated with the movable VM file.

Abstract: This application describes a virtual machine deployment method and an operation and maintenance management (OMM) virtual machine. The method includes: obtaining, by an OMM virtual machine, a quantity and a specification of service virtual machines created in virtual network function application software to which the OMM virtual machine belongs; and determining, by the OMM virtual machine based on load that needs to be carried by the application software and the quantity and the specification of the service virtual machines, a module to be configured for each service virtual machine. The described implementations avoid or reduce waste of virtual machine resources.

Abstract: Data processing systems and methods, according to various embodiments, are adapted for efficiently processing data to allow for the streamlined assessment of risk ratings for one or more vendors. In various embodiments, the systems/methods may use one or more particular vendor attributes (e.g., as determined from scanning one or more webpages associated with the particular vendor) and the contents of one or more completed privacy templates for the vendor to determine a vendor risk rating for the particular vendor. As a particular example, the system may scan a website associated with the vendor to automatically determine one or more security certifications associated with the vendor and use that information, along with information from a completed privacy template for the vendor, to calculate a vendor risk rating that indicates the risk of doing business with the vendor.

Abstract: In an embodiment, a guest operating system (OS) running on a virtual machine (VM) detects a VM migration, where the embodiment comprises storing, by the guest OS, a VM identifier (VMID) provided by the VM and a first host identifier (HID) provided by a host computer system in a computer memory. The embodiment also comprises determining, by the guest OS, that the VM performs migrations that are transparent to the guest OS. The embodiment further comprises detecting, by the guest OS, that the VM has been migrated based on a comparison of the first HID to a second HID provided to the guest OS in response to an HID request from the guest OS.

Abstract: Disclosed is a system and a method for creating a layered and portable multi-tenant architecture. Initially, a cloud container for a platform is created. The cloud container includes a namespace for a tenant accessing the platform. Further, a logical isolation for the namespace of the tenant is created. Subsequently, a plurality of tenants in the namespace is generated based on the logical isolation. The tenant is further allowed to access a tenant database via Application Programming Interfaces (APIs), thereby creating a layered multi-tenant architecture.

Abstract: Methods, systems, and computer-readable media for job scheduling based on job execution history are disclosed. A request is received to schedule a workload comprising a plurality of jobs. A resource allocation score for the workload is determined. The resource allocation score represents (at least in part) an estimated likelihood of successful execution of the workload. A first portion of the workload is scheduled for execution, and a remaining portion (if any) of the workload is delayed. A quantity of jobs in the first portion of the workload is determined based (at least in part) on the resource allocation score. Execution of the first portion of the workload is initiated.

Abstract: Methods and systems for providing services using mixed instance catalogs are described herein. A catalog may comprise a plurality of first virtual machines and a plurality of second virtual machines. The capacity of a first virtual machine may be larger than the capacity of a second virtual machine. Connection requests to access a service associated with the catalog may be distributed among the plurality of first virtual machines and the plurality of second virtual machines.

Abstract: A method is described for measuring the vulnerability of an AI module to spoofing attempts, including the classification and/or regression onto which the AI module maps the update data set is ascertained as an unperturbed result for a predefined data set in the input space E; at least one perturbation S having a dimensionality d<D is applied to the predefined data set so that at least one perturbed data set results in the input space E; the classification and/or regression onto which the AI module maps the perturbed data set is ascertained as the perturbed result; the deviation of the perturbed result from the unperturbed result is ascertained using predefined metrics; in response to the deviation satisfying a predefined criterion, it is determined that the AI module with regard to the predefined data set is vulnerable to spoofing attempts having a dimensionality d.

Abstract: Example implementations described herein are directed to searching ML models to facilitate IO load prediction. Even if there is no existing ML model applicable to the underlying container, example implementations described herein obtain the ML model for the nearest container. Through such example implementations, the underlying systems can reduce the total ML models maintained for the prediction of IO load and can enhance ML models with enough learning data for each ML model.

Abstract: Disclosed embodiments relate to generating an update package for updating software on an Electronic Control Unit (ECU) in a vehicle. Operations may include accessing a plurality of attributes of a software update to be stored on the ECU in the vehicle; accessing a corresponding plurality of attributes of current software stored on the ECU in the vehicle; comparing the plurality of attributes with the corresponding plurality of attributes; generating a delta file representing differences between the plurality of attributes and the corresponding plurality of attributes determined in the comparison; and providing the delta file to the ECU, wherein the delta file is configured to be processed by startup code in the ECU that enables the delta file to execute in the ECU in the vehicle.