Abstract: A system and method for orchestrating distributed operations to be executed in a distributed computing system with multiple virtual infrastructures uses a distributed operation descriptor to find any Cloud-Native Network Function (CNF) entry in the distributed operation descriptor. For each found CNF entry, a CNF descriptor is retrieved from a CNF catalog and parsed to find an overridable property for a CNF described in the CNF descriptor for which a property override is defined. Then, a target virtual infrastructure is selected from the multiple virtual infrastructures to perform a lifecycle management operation of the CNF. Instructions are then transmitted to a local operator in the target virtual infrastructure with the property override so that information regarding the CNF is transmitted to a local orchestrator of the target virtual infrastructure to perform the lifecycle management operation of the CNF at the target virtual infrastructure using the property override.

Abstract: Disclosed are a parallel method and device for convolution computation and data loading of a neural network accelerator. The method needs two input feature maps and two convolution kernel cache blocks, and sequentially stores the input feature maps and 64 convolution kernels into cache sub-blocks according to a loading length, so as to execute convolution computation and simultaneously load data of a next group of 64 convolution kernels.

Abstract: An example methodology includes declaring a node in a cluster of multiple nodes as a lead node to perform a cluster upgrade. While the lead node is performing the upgrade operations, the remaining nodes in the cluster continue to provide the cluster services. The upgrade operations on the lead node include migrating applications on the lead node to one of the other nodes in the cluster and performing upgrade of the lead node. In some cases, the upgrade operations include rebooting the lead node with a new OS image and installing new versions of the applications or re-installing the same applications on the lead node. The upgrade operations on the lead node may also include, once the lead node is upgraded, stopping applications and cluster services on the other nodes in the cluster, creating a new cluster that includes the lead node, and starting the applications on the lead node.

Abstract: A request for completion by an artificial intelligence (AI) model is received. Tasks required to fulfill the request are identified. An AI model is selected based on the tasks and capabilities of the AI model. The request is transmitted to the selected AI model. A response is received from the AI model. The response is then transmitter to a requester.

Abstract: A method of collecting and reporting inventory of resources deployed in a data center that includes hardware resources, a virtualization management software executed to provision virtual resources from the hardware resources, and a cloud management server executed to provision the virtual resources for tenants of the data center, includes the steps of: executing a first API call to the virtualization management software to collect first inventory of virtual resources deployed in the data center and a second API call to the cloud computing management software to collect second inventory of virtual resources deployed in the data center; storing the first and second inventory; and in response to an inventory request from a central orchestrator, initially sending a subset of the stored first and second inventory to the central orchestrator in accordance with parameters included in the inventory request, and thereafter sending updates to the subset to the central orchestrator periodically.

Abstract: A first hypervisor running on a first processor cluster is provided. During operation, the first hypervisor can determine a first set of processing nodes and a first memory unit of the first processor cluster in response to the booting up of a first Basic Input/Output System (BIOS) of the first processor cluster. The first hypervisor can discover a second hypervisor running on a second processor cluster comprising a second set of processing nodes and a second memory unit. The first hypervisor can operate, with the second hypervisor, a distributed system comprising the first and second sets of processing nodes and the first and second memory units. The first hypervisor can then operate, with the second hypervisor, a global virtual machine on the distributed system. The virtual memory space of the global virtual machine can be mapped to respective memory spaces of the first and second processor clusters.

Abstract: A system can be provided for deploying bare metal clusters that satisfy custom resource requests. For example, the system can receive from a client device, a custom resource request. The custom resource request can include a set of requirements for a bare metal cluster. The set of requirements can include a number of nodes for the bare metal cluster. The system can determine a set of resources that satisfies the set of requirements. The set of resources can include virtual Internet Protocol (IP) addresses and a set of baseband management controller (BMC) IP addresses. A number of BMC IP addresses in the set of BMC IP addresses can be equal to the number of nodes for the bare metal cluster. Additionally, the system can generate, based on the set of resources, a configuration file for the bare metal cluster and deploy, based on the configuration file, the bare metal cluster.

Abstract: In one embodiment, an apparatus includes: a plurality of execution circuits to execute and instruct micro-operations (?ops), where a subset of the plurality of execution circuits are capable of execution of a fused ?op; a fusion circuit coupled to at least the subset of the plurality of execution circuits, wherein the fusion circuit is to fuse at least some pairs of producer-consumer ?ops into fused ?ops; and a fusion throttle circuit coupled to the fusion circuit, wherein the fusion throttle circuit is to prevent a first ?op from being fused with another ?op based at least in part on historical information associated with the first ?op. Other embodiments are described and claimed.

Abstract: Job execution environment control techniques are described to manage policy selection and implementation to control use of job executors by a computing device, automatically and without user intervention. These techniques are usable to select a policy from a plurality of policies that is then used to control lifecycles of job executors of a job execution environment of a computing device. Further, these techniques are usable to respond dynamically to change the selected policy during runtime of the application in response to changes in the job execution environment.

Abstract: A signal processing device and a display apparatus for vehicles including the same are disclosed. The signal processing device includes a processor configured to perform signal processing for a display located in a vehicle, wherein the processor is configured to execute first to third virtual machines on a hypervisor in the processor, the second virtual machine is operated for a first display, the third virtual machine is operated for a second display, and the first virtual machine in the processor is configured to share at least some of data with the second virtual machine and the third virtual machine for processing of divided data. Consequently, the plurality of virtual machines for the plurality of displays in the vehicle may divide and process data.

Abstract: A method, computer program product and computer system for predicting excessive resource usage in a distributed computing environment is provided. A processor retrieves a portion of code associated with a workload in a distributed computing environment. A processor retrieves account information associated with the workload. A processor determines a likelihood that the workload is indicative of excessive resource usage based on the portion of code and the account information associated with the workload. In response to the likelihood of excessive resource usage exceeding a threshold, a processor reschedules the workload in the distributed computing environment.

Abstract: A computer-implemented method of monitoring programmatic containers performed through executing an agent processor is disclosed. The method comprises transmitting, by a processor, one or more deployment configurations from a monitoring server related to an application hosted in a container to a backend device, the processor receiving, from the backend device, a plurality of monitoring configurations for the application, the processor merging the plurality of monitoring configurations for the application into a merged monitoring configuration for the application, the processor providing the merged monitoring configuration for the application to the monitoring server, and the processor periodically receiving, from the monitoring server, telemetry data that characterizes one or more instances of the application.

Abstract: Systems and methods are provided for lock-free thread scheduling. Threads may be placed in a ring buffer shared by all computer processing units (CPUs), e.g., in a node. A thread assigned to a CPU may be placed in the CPU's local run queue. However, when a CPU's local run queue is cleared, that CPU checks the shared ring buffer to determine if any threads are waiting to run on that CPU, and if so, the CPU pulls a batch of threads related to that ready-to-run thread to execute. If not, an idle CPU randomly selects another CPU to steak threads from, and the idle CPU attempts to dequeue a thread batch associated with the CPU from the shared ring buffer. Polling may be handled through the use of a shared poller array to dynamically distribute polling across multiple CPUs.

Abstract: Example embodiments relate generally to systems and methods for continuous data protection (CDP) and more specifically to an input and output (I/O) filtering framework and log management system to seek a near-zero recovery point objective (RPO).

Abstract: Application migration using data movement capabilities of a storage system, including: executing an application on a first on-premises cloud infrastructure; storing, in a first storage system that is coupled for data communications with the first on-premises cloud infrastructure, data associated with the application; replicating, from the first storage system to a second storage system that is coupled for data communications with a second on-premises cloud infrastructure, the data; and initiating, based on the data, execution of a new instance of the application on the second on-premises cloud infrastructure.

Abstract: Techniques are disclosed relating to training a machine learning model to understand one or more rules without explicitly executing the rule. In some embodiments, a computer system generates synthetic samples for a trained machine learning model usable to make a classification decision, where the synthetic samples are generated from a rule and a set of existing samples. In some embodiments, the set of existing samples are selected based on exceeding a confidence threshold for the classification decision. In some embodiments, the computer system retrains the trained machine learning model using the synthetic samples.

Abstract: A method for the detection of anomalies in a networked water distribution system is provided. It improves detection methods based on an iterative modification of control variables of the network, by determining a reduced set of entities of the water distribution network on which control variables should be iteratively modifier. The invention increases the computing costs, and the reliability of such methods of detecting anomalies in a water distribution system.

Abstract: A framework for interpreting machine learning models is proposed that utilizes interpretability methods to determine the contribution of groups of input variables to the output of the model. Input variables are grouped based on dependencies with other input variables. The groups are identified by processing a training data set with a clustering algorithm. Once the groups of input variables are defined, scores related to each group of input variables for a given instance of the input vector processed by the model are calculated according to one or more algorithms. The algorithms can utilize group Partial Dependence Plot (PDP) values, Shapley Additive Explanations (SHAP) values, and Banzhaf values, and their extensions among others, and a score for each group can be calculated for a given instance of an input vector per group. These scores can then be sorted, ranked, and then combined into one hybrid ranking.

Abstract: Systems and methods are provided for reusing machine learning models. For example, the applicability of prior models may be compared using one or more assessment values, including a similarity threshold and/or an accuracy threshold. The similarity threshold may identify a similarity of data between a first data set used to generate a first model and a new data set that is received by the system. When the similarity between these two data sets is exceeded, the system may reuse a model with the highest similarity value. When an accuracy value of the data set does not exceed an accuracy threshold, the system may initiate a retraining process to generate a second ML model associated with the second data.

Abstract: There is described methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training a reinforcement learning system. The reinforcement learning system comprises an agent configured to perform actions based upon a policy and an intrinsic reward system configured to generate intrinsic reward values for the agent based upon the actions taken by the agent. The method comprises training the reinforcement learning system based upon a plurality of tasks. The training comprises updating the agent's policy based upon the intrinsic reward values generated by the intrinsic reward system and updating the intrinsic reward system based upon an extrinsic reward value obtained based upon the task being performed by the agent. The training further comprises re-initializing the agent's policy when an expiration criterion associated with the agent is met.