Dell Products Inc.

Emc IP Holding Company LLC

Ian Gerard Roche

Dharmesh M Patel

Philip E Hummel

Sean Creedon

Methods and systems for managing threats to data processing systems are disclosed. To manage the threats, multiple threat management models may be utilized. The threat management models may include centralized models that rely on operable connectivity to particular systems, and distributed models that do not rely on operable connectivity to the particular systems. The data processing systems may flexibly switch between use of these models to respond to changes in operably connectivity of a distributed system.

System and method for threat management in distributed systems

Techniques described herein relate to a method for managing a computer vision environment. The method includes identifying, by a computer vision (CV) node of a plurality of CV nodes, a CV alert; in response to identifying the CV alert: identifying candidate CV nodes of the plurality of CV nodes; generating a dynamic CV zone using the candidate CV nodes; initiating performance of a CV workload by the dynamic CV zone to generate CV data associated with the CV alert; generating a CV alert case associated with the CV alert; obtaining CV data from the dynamic CV zone; updating the CV alert case using the CV data generated during the performance of the CV workload; and providing the updated CV alert case to a VMS.

Method and system for performing distributed computer vision workloads in a computer vision environment using a dynamic computer vision zone

A system in accordance with one or more embodiments comprises a processor, a processing system operatively connected to the processor, and an enhanced networking interface operatively connected to the processing system. The enhanced networking interface is programmed to obtain encoded data from a local data source, perform a decoding of the encoded data to obtain decoded data, provide the decoded data to the processing system. The processing system is programmed to obtain the decoded data from the enhanced networking interface, perform a computer vision (CV) inferencing on the decoded data to obtain inferencing data, provide the inferencing data to the processor, and perform, by the processor, a remediation action based on the inferencing data.

Method and system for using remote direct memory access to store metadata for encoded data using processing system storage

Methods and systems for providing security management services are disclosed. To provide security management services in a manner that reduces the quantity of hardware resources necessary to provide the security management services, the security manager may prioritize security management services based on a trained state of a data processing system to modify a level of surveillance of the persons. By doing so, the device management services may only be provided when certain conditions are met rather than continuously. To place the data processing system in the trained state, the data processing system may need to be trained to predict paths that individuals are likely to traverse using training data. To obtain the training data, previously traversed paths of individuals may be tracked through an environment and stored into a database.

System and method for security management though forensic analysis of images of scenes

Techniques described herein relate to a method for optimizing power for a computer vision environment. The method includes obtaining, by a computer vision (CV) manager, an initial power optimization request associated with a CV workload; in response to obtaining the initial power optimization request: obtaining CV workload information associated with the CV workload; obtaining first CV environment configuration information associated with the power optimization request; generating a power optimization report based on the first CV environment configuration information and the CV workload information using a power optimization model; and initiating performance of the CV workload in a CV environment based on the power optimization report.

Method and system for optimizing power for a computer vision environment

Techniques described herein relate to a method for managing a computer vision environment. The method includes identifying a CV alert; in response to identifying the CV alert: making a first determination that the CV node is not participating in a distributed workload associated with a higher priority CV alert; in response to the first determination, the CV node: selects candidate CV nodes of the plurality of CV nodes; initiates performance of the distributed CV workload by the candidate CV nodes to generate CV data associated with the CV alert; generates a CV alert case associated with the CV alert; obtains CV data from the candidate CV nodes that are performing the distributed CV workload; updates the CV alert case using the CV data generated during the performance of the distributed CV workload; and provides the updated CV alert case to a VMS.

Method and system for performing distributed computer vision workloads in a computer vision environment using a computer vision node leader

Techniques described herein relate to a method for managing a computer vision environment. The method includes identifying a CV alert; generating a CV alert case associated with the CV alert; identifying nearby CV nodes of the plurality of CV nodes; transmitting CV alert to the nearby CV nodes; for each of the nearby CV nodes: receiving the CV alert; determining, based on CV environment configuration information of the nearby CV node and the CV alert, whether to perform a distributed CV workload; when the determination is to perform the distributed CV workload: initiating performance of the distributed CV workload by the nearby CV nodes to generate CV data; updating the CV alert case using CV data generated during the performance of the distributed CV workload to obtain an updated CV alert case; and transmitting by the nearby CV node to the VMS the updated CV alert case.

Method and system for performing distributed computer vision workloads in a computer vision environment

Techniques are provided for caching of machine learning model training parameters. One method comprises training a machine learning model using a given training dataset; and caching a parameter of the machine learning model from the training with the given training dataset. The cached parameter of the machine learning model is used for a subsequent training of the machine learning model. The caching may be performed after each of multiple iterations of the training of the machine learning model. A given cached iteration of the training of the machine learning model may be identified using a key based on: (i) a hash of the given training dataset, (ii) a hash of the machine learning model parameter, and/or (iii) hyperparameters of the machine learning model. The caching of a given iteration of the machine learning model may occur when the given cached iteration is not found in a cache memory.

Caching of machine learning model training parameters

Methods and systems for managing operation of data processing systems are disclosed. To manage operation of the data processing systems, telemetry data for the data processing systems may be collected and used to estimate the health of the data processing systems. The rates and types of telemetry data this is collected may be dynamically adjusted based on the confidence in estimates for the health of the data processing systems. The collection rate and/or number of monitored quantities may be increased as the confidence in the estimated health of the data processing systems decreases.

System and method for remote management of data processing systems

Techniques described herein relate to a method for managing a computer vision environment. The method includes obtaining, by a video management system (VMS), a computer vision (CV) alert; in response to obtaining the CV alert: receiving confirmation of the CV alert; in response to the confirmation: identifying a nearest mobile agent of mobile agents associated with the CV alert; providing the CV alert and at least a portion of the CV data associated with the CV alert to the mobile agent; generating a CV alert case associated with the CV alert; obtaining additional CV data associated with the CV alert from CV nodes and the nearest mobile agent; and updating the CV alert case using the additional CV data.

Method and system for managing computer vision alerts using mobile agents in a computer vision environment

Techniques are provided for analytically generating micro-service Consumer- Driven Contracts and automated tests. One method comprises obtaining a plurality of usage data records for consumers of a service from a run-time environment; extracting data features from the usage data records; applying a clustering algorithm to the usage data records to assign the usage data records to a given usage pattern cluster of a plurality of usage pattern clusters based on the extracted data features, wherein each of the plurality of usage pattern clusters comprises usage data records; and performing the following steps when the clustering algorithm creates a new usage pattern cluster: creating a new Consumer-Driven Contract that defines consumer expectations of the service, with respect to the new usage pattern associated with the new usage pattern cluster; and generating automated Consumer-Driven Contract tests to test the new Consumer-Driven Contract.

Analytically generated micro-service consumer-driven contracts and automated tests

Techniques are provided for machine learning based query optimization for federated databases. An exemplary method comprises obtaining a query to be processed in a federated database; generating at least one predictive data movement instruction to move data to a target data source when the target data source satisfies one or more of a predefined efficiency criteria with respect to a query type of the query and a predefined capacity criteria at an expected execution time of the query; and generating a query execution plan for the query by calculating a cost of execution for a plurality of potential target data sources and selecting a target data source for the query based on the calculated cost of execution. The federated database optionally employs a dynamic federated query schema.

Machine learning based query optimization for federated databases

Techniques are provided for generating specifications for a microservice implementation of an existing application. An exemplary method comprises: analyzing request data and corresponding response data for an application implemented as a monolithic application and/or a Service Oriented Architecture application to generate data features; parsing an audit log and/or a transaction log of the application to identify interactions with a data store; clustering the data store interactions using an unsupervised learning technique to identify patterns of usage of the data store; selecting one or more service types to generate using a trained supervised machine learning model for the requests, the corresponding response data and the data store interactions; and generating an application programming interface specification, a data model specification and/or a message specification for the selected service types for a microservice implementation of the application. A run-time environment, a data definition language and/or message queues are optionally generated for the one or more selected service types.

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Glanmire, Ireland

Ian Gerard Roche