Abstract: A method of assigning processing resources is described. The method includes receiving an application and analyzing the application to determine an expected run time use of the processing resources. At least a portion of the processing resources are assigned to the application based on the expected run time use of the processing resources. A computing system architecture including a processing resource pool, an interface, and a special purpose optimization coprocessor is also described. The interface receives an application to be executed. The special purpose optimization coprocessor receives information about the application and the processing resource pool and outputs an allocation scheme for allocating tasks of the application to the processing resource pool.

Abstract: Disclosed are techniques for management of multiple processor cores in a multi-core system-on-chip (SoC). In an implementation, the SoC may configure each processor core in a first subset of processor cores as a management controller for performing system management functions for processor cores not in the first subset, the first subset comprising at least one processor core from the plurality of processor cores. System management functions are handled by the processor cores in the first subset, while operating system functions are handled by the processor cores not in the first subset. In an implementation, the number of processor cores to be included in the first subset (which may be zero if it is desired that the SoC may operate in legacy mode), may be controlled at boot time according to a boot setting.

Abstract: Concepts and technologies disclosed herein are directed to optimizing and reducing redundant dispatch tickets via a network knowledge graph. According to one aspect disclosed herein, a network knowledge graph generation system (“NKGGS”) can construct a machine learning model to determine a probability of an installation job needing a helper job to fulfill a service order. The NKGGS can execute the machine learning model to determine the probability. The machine learning model can determine the probability of the installation job needing the helper job to fulfill the service order based upon a network knowledge graph and a dependency score. The NKGGS can cluster the installation job with a plurality of installation jobs.

Abstract: This disclosure provides systems, devices, apparatus, and methods, including computer programs encoded on storage media, for dynamic wave pairing. A graphics processor may allocate one or more GPU workloads to one or more wave slots of a plurality of wave slots. The graphics processor may select a first execution slot of a plurality of execution slots for executing the one or more GPU workloads. The selection may be based on one of a plurality of granularities. The graphics processor may execute, at the selected first execution slot, the one or more GPU workloads at the one of the plurality of granularities.

Abstract: Embodiments of system and methods for providing centralized management of a software defined automation (“SDA”) system are disclosed. The SDA system comprises of a collection of controller nodes and logically centralized and yet physically distributed collection of compute nodes by monitoring activities of the compute nodes. In accordance with some embodiments, one or more components of the system monitor execution, network and security environments of the system to detect an event in a first environment. In response to the detected event, at least one component in the first environment is remediated, the remediation of the first environment creating a trigger to cause remediation of at least one component in each of a second and third environments.

Abstract: Methods, systems and computer program products for data fetching in a networked computing environment. In one embodiment, the method comprises receiving a request from an analytical node for a set of data for a defined job, and identifying in networked storage a subset of the data for the job. The subset of data is loaded to the analytical node based on the sequence in which the data are projected to be accessed in the job. In an embodiment, the request includes a specification for the job, and the specification is analyzed to identify the subset of data. In one embodiment, the subset of data is identified by identifying another job having a relationship to the defined job, and identifying the data used for that other job. In an embodiment, the networked computing environment is a cloud computing environment, and the defined job is an analytics job.

Abstract: An embodiment includes extracting, by a scheduler, function-tag data associated with a function identified by a deployment request. The embodiment also includes selecting, by the scheduler, a computing device within a server cluster to host the function based at least in part on a comparison of the function-tag data and host-tag data associated with the computing device. The embodiment also includes issuing, by the scheduler, an instruction to the computing device, wherein the issuing of the instruction causes an allocation of resources for hosting execution of the function.

Abstract: Some embodiments track internal actions of a development tool, and actions of a user of the tool. When the user faces a problem, information about the problem state is matched to a mitigation graph that was built from other user's sessions with the tool. Then documentation contextualized to the problem is synthesized, e.g., using machine learning models, and presented to the user. In addition to text and images, the documentation may include interaction points such as hyperlinks or buttons associated with code that mitigates the problem. For example, a mitigation sequence may proactively instruct the tool to perform certain actions such as an install or build or undo, or may automatically operate the tool user interface proactively in place of the user. Mitigation sequences may be sorted, and subjected to cost or outcome desirability prioritization. An administration tool provides mitigation graph management functionality. Personally identifiable information presence is constrained.

Abstract: A system and method are disclosed for orchestrating the execution of computing tasks. An orchestration engine can receive task requests over a network from a plurality of process engines. The process engines may correspond to respective edge or field devices that are remotely located as compared to the orchestration engine. Each task request may indicate at least one task requirement for executing a respective computing task. A plurality of computing instances that have available computing resources can be selected from a set of computing instances. A predicted runtime can be generated for each of the computing tasks. In an example, based on the predicted runtimes, task requirements, available computing resources, and associated network conditions, a schedule and allocation scheme are determined by the orchestration engine.

Abstract: A system may include a memory and a processor in communication with the memory. The processor may be configured to perform operations. The operations may include registering a custom resource definition for a tenant with a host and scaling a controller for the customer resource definition. The operations may include generating a replication using the customer resource definition, injecting information into the replication, and syncing a status of the custom resource definition between the host and the tenant.

Abstract: A system and method of combining table management software and customer experience skills by generating profiles of patrons, servers, and restaurants and using machine learning algorithms on those profiles to build more intimate relationships between patrons, food service establishments, and food service professionals. Trait matching provides optimized matchmaking between patrons and servers who share certain commonalities while also balancing the table management operations. Machine learning algorithms may be used to identify patterns of commonality that would not otherwise be recognized. This system allows patrons to choose servers at an establishment over a plurality of electronic devices by using the cluster analysis results. It provides servers more lateral work experience between participating restaurants, and restaurants more power to operate smoothly and build highly cohesive teams.

Abstract: A system performs joint placement and chaining of virtual network functions (VNFs) based on a genetic algorithm in response to a request for virtual network services, including an in-line service. The request includes a description of a virtual network of VNFs and virtual links connecting the VNFs. A description of a physical network including servers and physical links is provided. Each chromosome in a population encodes a mapping between the virtual links enumerated to form a locus and a corresponding sequence of server pairs. Each chromosome is evaluated against objective functions subject to constraints to identify a chromosome as a solution. The VNFs are placed on the servers according to the mapping encoded in the identified chromosome. According to the mapping, each VNF is mapped to one of the servers and each virtual link is mapped to a path composed of one or more of the physical links.

Abstract: Aspects of embodiments provide methods and network criticality managers for controlling data traffic in a network. The method may include: obtaining network topology information from a plurality of physical resources used to transfer data traffic in the network; determining priority levels for data traffic in the network; using the network topology information and the determined priority levels to calculate criticality values for physical resources from among the plurality of physical resources, wherein the criticality values for the physical resources indicate the relative importance of the physical resources in the network; and controlling the data traffic in the network based on the calculated criticality values.

Abstract: A resource processing method and apparatus. The method is achieved by a computer configured to execute the following steps: generating a resource allocation chart based on a resource allocation request; processing the resource allocation chart and generating an access entrance of the resource allocation chart; opening the access entrance to enable at least one third party resource supplier to respond to the resource allocation request via the access entrance. The method further comprises: when the resources are not available or not suitable for allocation, receiving a resource allocation chart, wherein the resource allocation chart is based on the resource allocation request; generating an access entrance of the resource allocation chart; opening the access entrance to at least one third party resource supplier; and receiving the allocated resources from the at least one third party resource supplier by responding to the resource allocation request through the access entrance.

Abstract: A method for managing client resources by receiving a desired load factor representing the number of instructions being executed per second (IOPS) to implement an application on a set of cores of a client device, based on the desired load factor and a latency factor, determining a maximum number of IOPS that can be executed by the cores of the client device before reaching system saturation, determining a pattern of the IOPS being executed on the set of cores based on historical IOPS information for the latency factor, and based on the historical IOPS information, determining to execute the IOPS on a subset of the set of cores.

Abstract: Embodiments herein may include systems, apparatuses, methods, and computer-readable media, for a multi-access edge computing (MEC) system. A MEC orchestrator is to receive a request for service that includes a workload from a user agent; and facilitate formation of a SLA for servicing the workload. To facilitate the formation of the SLA includes to obtain, via a decentralized contracting system, bids from a plurality of service providers to respectively service a plurality of functions or tasks of the workload. The MEC orchestrator is also to translate the workload into the plurality of functions or tasks, and schedule servicing of the functions of tasks with the one or more service providers, including one or more edge computing devices, in accordance with the SLA. Other embodiments may be described and/or claimed.

Abstract: Systems and methods to support flexible scaling and dynamic resource allocation for virtualized cable data plane applications. The system includes a head end together with a node to provide data to customer devices. A container operating that includes a data plane application that provides packets of data for transmission to the node. The data plane application is instantiated with at least one of a virtual networking function and a computing resource function.

Abstract: Systems that determine relationships between network components of a cluster using packet filters are disclosed. A system can identify objects that implement services of a cluster and network connections associated with respective pairs of the objects. The system can also filter out network connections from the identified network connections. The filtering can remove connections between source objects and destination objects based on the destination objects lacking any components that implement a service in cluster. The filtering can also retain network connections between source objects and destination objects based on the source objects including components that each implement at least one service, and based on the second destination object including components that each implement at least one service. Additionally, the system can generate relationship maps and network topologies using the determined relationships.

Abstract: Methods, systems, and apparatuses for tracking and generating analysis of queries submitted to virtual warehouses are described herein. A request to execute a query may be received. An organization may be determined, and the query may be modified to add an indication of the organization. A plurality of virtual warehouses may be identified. One of the plurality of virtual warehouses may be caused to execute the query. Query results and costs data may be received based on execution of the query by the first virtual warehouse. The costs data may indicate one or more costs associated with the execution of the query by the first virtual warehouse. The indication of the organization may be extracted from the query results. A user interface comprising query cost information corresponding to the organization may be generated.

Abstract: Disclosed are some implementations of systems, apparatus, methods and computer program products for executing process flows. A request to initiate execution of a flow is processed, where the request is received from a first user. The flow includes a plurality of flow elements, where each of the flow elements represents a corresponding set of computer-readable instructions. Execution of the flow is initiated and an operation mode assigned to one of the flow elements is determined. A set of computer-readable instructions corresponding to the flow element is executed according to the operation mode such that an identity of the first user is recorded in association with an operation on a database record, wherein permissions allocated in a user profile to the first user do not provide the first user permission to cause the operation on the database record.

Abstract: An electronic device control method, apparatus, and an electronic device, where the electronic device control method includes determining a running mode of a current running application in an electronic device, and controlling, based on the running mode, a component in the electronic device to execute an operation corresponding to the running mode, where the component includes a component employed for running the current running application.

Abstract: Certain embodiments described herein are directed to methods and systems for adding one or more nodes to a first cluster including a first node in a computer system. A method performed by the first node comprises receiving a first request from a second node to join the first cluster. The method also comprises retrieving a first cluster configuration associated with the first cluster from a distributed database through a first database server (DBS) and creating a second cluster configuration using the first cluster configuration and information received from the second node as part of the request. The method further comprises populating a first one or more local trust stores of a first one or more processes executing on the first node with a second one or more security certificates of a second one or more processes executing on the second node. The method further comprises writing the second cluster configuration to the distributed database and returning the second cluster configuration to the second node.

Abstract: A cable communications network provides an alternative communications path between a user equipment device and a data network to a cellular path for a communications session with a desired level of Quality of Service. A cable modem termination system, coupled to a wireless core network, e.g., a 5G core network, interacts with the wireless core network to attempt to establish a PDU session for a UE with a desired QoS level. The core sends a QoS service request message to the CMTS including a requested level of QoS, an IP address and port number for the session. The CMTS and cable modem, corresponding to the UE, negotiate and decide if the request desired QoS level can be supported over the cable between the CMTS and the cable mode for the session.

Abstract: Initiating the processing of resource events across disparate real-time processing networks, such as networks located international. In order to facilitate such resource events determinations are made that authorize the resource event participants to conduct the resource event across the international real-time processing networks. Once properly authorized the present invention provides for processing parameters to be determined, which may be specific to anyone of the resource participants and/or international real-time processing networks. Such processing parameters may be related to rules associated with settlement of the resource event, conversion rules for the international conversion of resources, resource sponsorship and the like. Once the resource event has been authorized and processing parameters determined, commands are sent to the respective interconnected international real-time processing networks that initiate the real-time processing of the resource event.

Abstract: Resource provider specifications, characterizing computing resources of computing resource providers, are received. The reachability of each IP address included in the received specification is determined. An agent is deployed that is operable to determine the value of each of a set of metrics in the environment of the host at which the agent is deployed. The agent determines the value of each metric of the set of metrics in the environment of the relevant host, and communicates the determined values to one or more computing devices that validate whether the resources characterized by the communicated values are sufficient to provide the performance characterized by the received specification and that each ISP router complies with a predetermined policy. For each computing resource provider validated and determined to comprise an ISP router compliant with policy, the specified computing resources are added to a pool of resources for cloud computing.

Abstract: Systems and methods are provided for managing dynamically allocated storage and processing units. The systems and methods include operations for determining, a usage pattern having a peak usage portion and a low usage portion; reserving a first collection of units on a dynamic unit allocation system during the peak usage portion; detecting a transition from the peak usage portion to the low usage portion; in response to detecting the transition, instructing the dynamic unit allocation system to reduce the first collection of units to reserve a second collection of units corresponding to a second amount of the low usage portion; selecting asynchronous tasks that consume a set of units greater than the second collection of units; and during a period of time that the dynamic unit allocation system is reducing the first collection of units, causing the asynchronous tasks to be executed by the dynamic allocation system.

Abstract: The present invention discloses a method for protecting a deep learning model based on confidential computing. In this solution, a use process of a deep learning model is divided into two stages: Data preprocessing and inference. At the data preprocessing stage, a data preprocessing model is mainly used to process inference data of an authorized user. The data preprocessing model is a lightweight processing module, which occupies less computing resources, and the data preprocessing model is deployed in a confidential computing environment. At the inference stage, an inference model is used to perform inference on preprocessed data, and the inference model is deployed in a common computing environment. In the entire process, copyright attestation of the deep learning model can be implemented without affecting inference accuracy of the model, and the infringement of the model copyright can be effectively resisted through model forgery, transfer learning, knowledge distillation, and the like.

Abstract: Example implementations relate to edge acceleration by offloading network dependent applications to a hardware accelerator. According to one embodiment, queries are received at a cluster of a container orchestration platform. The cluster includes a host system and a hardware accelerator, each serving as individual worker machines of the cluster. The cluster further includes multiple worker nodes and a master node executing on the host system or the hardware accelerator. A first worker node executes on the hardware accelerator and runs a first instance of an application. A distribution of the queries is determined among the worker machines based on a queuing model that takes into consideration the respective compute capacities of the worker machines. Responsive to receipt of the queries by the host system or the hardware accelerator, the queries are directed to the master node or one of the worker nodes in accordance with the distribution.

Abstract: A computer-controlled system for digitally evaluating one or more computer-executable and traceable metrics associated with a machine at a node. The system includes a computerized data collection wireless device for collecting digital data stored in the form of a plurality of computer-executable files from the machine. The computerized data collection wireless device includes a metric logistics device that generates a metric queue containing a plurality of computer-executable metric components storing information indicative of at least one of a date, time, geo-coordinates, machine identification details, and computer readable and executable performance data associated with the machine. The system includes a data aggregator and a computer-controlled metric appliance communicatively coupled to the data aggregator and receives the metric queue from the data aggregator.

Abstract: Embodiments of a method are disclosed. The method includes performing decentralized distributed deep learning training on a batch of training data. Additionally, the method includes determining a training time wherein the learner performs the decentralized distributed deep learning training on the batch of training data. Further, the method includes generating a table having the training time and other processing times for corresponding other learners performing the decentralized distributed deep learning training on corresponding other batches of other training data. The method also includes determining that the learner is a straggler based on the table and a threshold for the training time. Additionally, the method includes modifying a processing aspect of the straggler to reduce a future training time of the straggler for performing the decentralized distributed deep learning training on a new batch of training data in response to determining the learner is the straggler.

Abstract: The present invention aims at providing an approach to digital twin-based process control for efficient and accurate achievement of process objectives. Heretofore, a controller service module (18) runs an event-driven control process in a digital twin domain for control of process entities operated in a process domain. The behavior of process entities is modeled through execution of state machine models. Event data is communicated asynchronously to the controller service module (18) for storage in a process cycle buffer (26). A model-based process controller (24) reads input information in processing cycles and controls process entities by operating state machine models to reflect the input of event data. It is checked whether the operation of state machine models triggers the generation of external control commands which are then output by an outbound interface (32) to process entities for control processing.

Abstract: Systems and methods for mapping applications onto system resource of a computing platform are discussed. The computing platform may receive, using control circuitry, a request to run a plurality of applications on a computing platform having a plurality of system resources. The computing platform may determine a plurality of mapping configurations for the plurality of applications onto the plurality of system resources. The computing platform may execute the plurality of applications with each of the plurality of mapping configurations. The computing platform may determine at least one performance metric based on the executed plurality of applications for each of the plurality of mapping configurations. The computing platform may select a selected mapping configuration among the plurality of mapping configurations based on at least one determined performance metric.

Abstract: A processor may receive user data associated with one or more locations of a user in an environment. The processor may receive edge computing data associated with utilization of edge computing resources by the user. The processor may analyze the edge computing data to associate a context with an edge computing resource need. The processor may analyze the user data to associate a context with a location of the user within the environment. The processor may determine a first location of the user in the environment at a first time. The processor may predict a first edge computing need of the user in the first location. The processor may determine an arrangement of one or more edge computing devices configured to meet the first edge computing need of the user at the first time.

Abstract: A computer implemented method, computer program product, and system for managing execution of a workflow comprising a set of subworkflows, comprising optimizing the set of subworkflows using a deep neural network, wherein each subworkflow of the set of subworkflows has a set of tasks, wherein each task of the sets of tasks has a requirement of resources of a set of resources; wherein each task of the sets of tasks is enabled to be dependent on another task of the sets of tasks, training the deep neural network by: executing the set of subworkflows, collecting provenance data from the execution, and collecting monitoring data that represents the state of said set of resources, wherein the training causes the neural network to learn relationships between the states of said set of resources, the said sets of tasks, their parameters and the obtained performance, optimizing an allocation of resources of the set of resources to each task of the sets of tasks to ensure compliance with a user-defined quality metric b

Abstract: The disclosed technology is generally directed to blockchain technology. In one example of the technology, a first transaction node of a hosted permissioned blockchain network is provisioned for a first consortium member of the hosted permissioned blockchain network. A shared pool of validator nodes of the hosted permissioned blockchain network is provisioned. The shared pool of validator nodes includes at least one validator node. The shared pool of validator nodes is shared among the plurality of consortium members. The validator nodes of the shared pool of validator nodes are configured for blockchain transaction validation based on a BFT consensus protocol. A second transaction node of the hosted permissioned blockchain network is provisioned for a second consortium member of the hosted permissioned blockchain network. Each transaction node of the hosted permissioned blockchain network is separate from each validator node of the hosted permissioned blockchain network.

Abstract: Prediction logic analyzes previous data usage activities of a customer process running on a host machine to generate a first prediction indicating that the customer process will request a first data set at a first time. The prediction logic retrieves the first data set from long-term storage and loads the first data set into memory on the host machine in advance of the first time in order to provide the customer process with access to first data set in the memory during a period between the first time and a second time. The prediction logic further generates a second prediction indicating that the customer process will not access the first data set for a threshold period of time after the second time and stores the first data set in the long-term storage at the second time.

Abstract: The present disclosure relates to a plug-in for enhancing resource elastic scaling of a distributed data flow and a method for enhancing a plug-in for enhancing resource elastic scaling of a distributed data flow. The plug-in is connected to a scaling controller used for resource elastic scaling of a distributed data flow. The plug-in includes a decision maker, a decision model, and a scaling operation sample library. The scaling controller registers a data flow to the plug-in through a first interface. The scaling controller sends an optimal decision of resource scaling in each status to the plug-in through a second interface. The scaling operation sample library is configured to record the optimal decision of resource scaling in each status. The decision model is configured to predict a received data flow based on the optimal decision recorded in the scaling operation sample library, to generate a prediction decision.

Abstract: A system and method of managing highly-volatile statistics of a multi-threaded application environment. The statistics are represented by one or more statistics objects. Each statistics object of the one or more statistics objects is prefixed with an object header having a version counter with an initial version count of zero. Each statistics object is associated with a statistics class instance, each statistics class instance being associated with an associative registry that is configured to allocate smallest possible objects of a size equal to or greater than to that of the statistics objects, the registry segmenting the statistics objects according to a size class. Each allocated statistics object is constructed in the object frame after the object header. Then, the object header of each allocated statistics object is initialized. Once allocated, objects can be deallocated.

Abstract: Apparatuses, methods and storage medium for computing including determination of work placement on processor cores are disclosed herein. In embodiments, an apparatus may include one or more processors, devices, and/or circuitry to identify a favored core of the processor cores. The one or more processors, devices, and/or circuitry may be configured to determine whether to migrate a thread to or from the favored core. In some embodiments, the determination may be by a process executed by a driver and/or by an algorithm executed by a power control unit of the processor.

Abstract: Generating an output prioritized list of computerized work-items for prioritizing work based on a set of prioritized computer-executable rules includes a structured way of associating contributions and expenses with computerized work-items and enhancing current technological processes of selecting computerized work-items to meet prioritized computer-executable rules.

Abstract: A feature capacity scaling methodology is disclosed. In a computer-implemented method, components of a computing environment are automatically monitored, and have a feature capacity analysis using a forecast performed thereon. Provided the feature capacity analysis determines that features of the components are well utilized, a vertical scaling of the features is performed.

Abstract: A processing device, operatively coupled with one or more memory devices, is configured to provide a plurality of virtual memory controllers, partition one or more memory devices into a plurality of physical partitions, and associate each of the plurality of virtual memory controllers with one of the plurality of physical partitions. The processing device further provides a plurality of physical functions, wherein each of the plurality of physical functions corresponds to a different one of the plurality of virtual memory controllers, and presents the plurality of physical functions to a host computing system over a peripheral component interconnect express (PCIe) interface.

Abstract: Some embodiments provide a novel method for enforcing service policies at different container clusters configured by several SDN controller clusters. A first SDN controller cluster defines a particular service policy to be enforced for machines in first, second, and third container clusters. First, second, and third sets of network elements for the first, second, and third container clusters are managed by the first, a second, and a third SDN controller cluster respectively. For data message flows exchanged between machines in the first and second container clusters, the first SDN controller cluster distributes the particular service policy to service nodes only in the first container cluster. For data message flows exchanged between machines in the second and third container clusters, the first SDN controller cluster distributes the particular service policy to service nodes in at least one of the second and third container clusters.

Abstract: A device and a method for interlinking conventional fieldbus-based automatic control system with IoT at a subordinate position are provided. According to the present disclosure, the interlinking system device comprise a fieldbus connection unit connected to an operation device based on a fieldbus protocol and configured to operate as an input-output device, a fieldbus virtual input-output memory configured to memorize input-output information exchanged with the operation device, an IoT connection unit connected to an IoT platform based on an IoT protocol and configured to operate as an IoT device, a message formation unit configured to apply message metadata received from the IoT platform via the IoT connection unit and a message processing unit configured to process an input-output message based on the message metadata and the input-output information.

Abstract: In accordance with embodiments disclosed herein, there are provided mechanisms and methods for automating deployment of applications in a multi-tenant database environment. For example, in one embodiment, mechanisms include managing a plurality of machines operating as a machine farm within a datacenter by executing an agent provisioning script at a control hub, instructing the plurality of machines to download and instantiate a lightweight agent; pushing a plurality of URL (Uniform Resource Locator) references from the control hub to the instantiated lightweight agent on each of the plurality of machines specifying one or more applications to be provisioned and one or more dependencies for each of the applications; and loading, via the lightweight agent at each of the plurality of machines, the one or more applications and the one or more dependencies for each of the one or more applications into memory of each respective machine.

Abstract: In the disclosure of the present innovation the system utilizes artificial intelligence, machine learning, and data analytics for practical and robust digital democracy applications to dynamically predict the most important states to survey in a process. In the present invention, beneficiaries are surveyed about their preferences over various options in these surveyed states. In the current invention, when a state that has not been surveyed occurs, Bayesian methods are used to dynamically predict what a user's ballot would have looked like if that state had been surveyed and Bayesian prediction is then tested, and if the result is found to be robust then a decision is reached using voting logic and economic theory. In the system of the current invention, if the results are not robust, then the process is rerun to determine the most important states to survey.

Abstract: A system includes a subsystem, a database, a memory, and a processor. The subsystem includes a computational resource associated with a resource usage and having a capacity corresponding to a maximum resource usage value. The database stores training data that includes historical resource usages and historical events. The memory stores a machine learning algorithm that is trained, based on the training data, to predict, based on the occurrence of an event, that a future value of the resource usage at a future time will be greater than the maximum value. The processor detects that the event has occurred. In response, the processor applies the machine learning algorithm to predict that the future value of the resource usage will be greater than the maximum value. Prior to the future time, the processor increases the capacity of the computational resource to accommodate the future value of the resource usage.

Abstract: Generation and/or utilization of particular data structure(s) in more efficiently storing, retrieving, and/or presenting personal entry(s) is described herein. Implementations can utilize the data structure(s) in more efficiently responding to a vague user request that specifies a particular type of personal entry, but lacks any additional features of the personal entry(s) sought. Utilization of the data structure(s) can enable responsive personal entry(s) to be efficiently identified (e.g., processor and/or memory efficiency and/or with reduced latency). For example, some implementations can enable selection of a subgroup of personal entries to provide responsive to the vague request, without necessitating any processing of personal entries, that are not included in the selected subgroup, be performed responsive to the request.

Abstract: A system includes a memory component and a processing device, operatively coupled with the memory component, to receive a request to perform a first test of memory components at a test platform, identify test resources of the test platform that are associated with the memory components, identify, among the test resources, a subset of test resources that are not being used by a second test of the memory components at the test platform, and assign, based on the subset of the test resources, a test resource of the test resources to obtain an assigned test resource for use by the test.

Abstract: Some embodiments provide a hierarchical data service (HDS) that manages many resource clusters that are in a resource cluster hierarchy. In some embodiments, each resource cluster has its own cluster manager, and the cluster managers are in a cluster manager hierarchy that mimics the hierarchy of the resource clusters. In some embodiments, both the resource cluster hierarchy and the cluster manager hierarchy are tree structures, e.g., a directed acyclic graph (DAG) structure that has one root node with multiple other nodes in a hierarchy, with each other node having only one parent node and one or more possible child nodes.