Dell Products Inc.

Emc IP Holding Company LLC

Mohammad Rafey

Parminder Singh Sethi

Hung The Dinh

Bijan Kumar Mohanty

Mohamed Sohail

Siddharth Agrawal

Karun Thankachan

Prajnan Goswami

Jagatha Sai Siva Surya Lokesh Prasad

A system can determine performance data for updates created by update creators that execute on devices during a time period, the performance data indicating performance of a group of metrics for a respective update of the updates. The system can determine a first ranking of the update creators for the time period based on the performance data. The system can determine a second ranking of the update creators based on applying a pairwise comparison count of respective rankings of update creators of the first ranking and rankings for time periods prior to the time period. The system can determine whether to publish a first update from a first update creator of the update creators based on a position of the first update creator among the update creators in the second ranking.

Computer update performance assessment

Predictive build quality assessment is disclosed, e.g., to predict performance of a candidate software build prior to deploying the candidate software build in a production environment. An example method can include comparing performance of a candidate software build with performance data associated with clusters of previous builds. A cluster having similar performance to the candidate software build can be selected. A source code graph for the candidate software build can be compared to source code graphs of builds included in the selected cluster. A previous build in the cluster, which is associated with a source code graph that is similar to the candidate software build's source code graph, can be selected. Individual performance data associated with the selected previous build can be inferred to be the predicted performance of the candidate software build, thereby informing decision making regarding further testing, modification, and/or deployment of the candidate software build into production.

Predictive build quality assessment

A method may include receiving data from a device within a network, wherein the data is associated with one or more features of the device, and determining a subset of the features of the device that is associated with a runtime behavior of the device. The method may also perform a univariate analysis on a feature dataset that is associated with the subset of the features of the device, perform a multivariate analysis on the feature dataset that is associated with correlated features in the subset of the features, and generate a device signature based on the univariate analysis and the multivariate analysis. The method may also generate a predictive model based on the device signature, wherein the predictive model is trained on historical data of devices in the network, generate a device profile based on the predictive model that may be used to determine a device type and the runtime behavior of the device, and determine a segment of the network associated with the device based on the device type and the runtime behavior of the device.

System and method for profiling for microsegmentation based on unique signature of a device

An apparatus comprises a processing device configured to generate a model of a plurality of devices characterizing relationships between the devices, to build a device dependency chain for the devices based on the model, to predict workload for each of the devices in one or more time slots of a given time period, and to determine a deployment schedule for the devices based on the device dependency chain and the predicted workload. The processing device is also configured to utilize the deployment schedule to select a device of the devices on which to perform an operation, to determine whether the selected device corresponds to an additional device of the devices configured to operate in place of the selected device during performance of the operation, and to control performance of the operation on the selected device responsive to the determination of whether the selected device corresponds to the additional device.

Determining a deployment schedule for operations performed on devices using device dependencies and redundancies

An apparatus comprises a processing device configured to monitor, over time intervals each comprising multiple time slices, client requests to access software container instances hosted by container host devices of a geographically-distributed software container platform, and to generate cluster pattern data comprising geographic clusters for the software container instances in each of the time slices. The processing device is also configured to predict, for a given time slice in a given subsequent time interval, formation of a geographic cluster of client requests for a given software container instance based on the cluster pattern data. The processing device is further configured to calculate network distances from the predicted geographic cluster to each of the container host devices, to select one of the container host devices based on the calculated network distances, and to proactively replicate the given software container instance in the selected container host device.

Proactive replication of software containers using geographic location affinity to predicted clusters in a distributed computing environment

An apparatus comprises a processing device configured to generate a model characterizing relationships between devices in an information technology infrastructure, to build a device dependency chain for the devices based at least in part on the generated model, and to predict workload for each of the devices in each of two or more time slots of a given time period. The processing device is also configured to determine a deployment schedule for the devices based at least in part on the device dependency chain and the predicted workload for each of the devices in each of the two or more time slots of the given time period, to identify an operation to be performed on the devices, and to utilize the deployment schedule to select ones of the devices on which to perform the identified operation in each of the two or more time slots of the given time period.

Determining a deployment schedule for operations performed on devices using device dependencies and predicted workloads

An apparatus includes at least one processing device configured to obtain event metadata for events published by event sources to an event platform, the event metadata comprising static event tags for respective ones of the events. The at least one processing device is also configured to generate dynamic event tags having an association with event types based at least in part on analysis of real-time event traffic comprising a subset of the events published by the event sources to the event platform over a designated time period. The at least one processing device is further configured to train a machine learning model utilizing the static event tags and the association of the dynamic event tags with the event types, receive a query comprising event parameters, and provide a response to the query by utilizing the trained machine learning model to match events with the event parameters in the query.

Predictive event searching utilizing a machine learning model trained using dynamically-generated event tags

A method includes monitoring client requests to access software container instances hosted by container host devices of a geographically-distributed software container platform and identifying, for a given software container instance hosted by a first one of the container host devices, geographic clusters of the client requests. The method also includes calculating a network distance from a given one of the geographic clusters to each of at least a subset of the container host devices. The method further includes replicating the given software container instance in a second one of the container host devices responsive to determining that the calculated network distance from the given geographic cluster to the second container host device is at least a threshold amount less than the calculated network distance from the given geographic cluster to the first container host device.

Software container replication using geographic location affinity in a distributed computing environment

An apparatus includes a processing device configured to obtain time series diagnostic data associated with assets in an information technology (IT). The processing device is also configured to generate first modality information comprising behavior labels assigned to each of a plurality of time periods, a given behavior label for a given time period being based at least in part on measured feature values for the features collectively in the given time period. The processing device is further configured to generate second modality information comprising feature deltas characterizing differences between measured feature values for interdependent feature pairs. The processing device is further configured to perform multi-modal analysis of the time series diagnostic data to detect behavior patterns in the utilizing a machine learning model trained using the first modality information and the second modality information, and to initiate remedial action in the IT infrastructure responsive to detecting an anomalous behavior pattern.

Detecting behavior patterns utilizing machine learning model trained with multi-modal time series analysis of diagnostic data

A method comprises receiving a request to execute an instance of a given software container, determining source code entities of source code of the given software container, and generating a given software container profile for the given software container based at least in part on rankings associated with the source code entities. The method also comprises creating a resource management plan for the given software container utilizing one or more machine learning algorithms, the resource management plan comprising resource management metric thresholds determined based at least in part on historical resource utilization data for additional software containers having associated software container profiles similar to the given software container profile. The method further comprises executing the instance of the given software container on a software container platform, and managing resource utilization of the executing instance of the given software container based at least in part on the resource management plan.

Resource management for software containers using container profiles

A method includes detecting an error that has occurred in one or more assets of an enterprise system and generating a visual error signature of the detected error, the visual error signature comprising at least a portion of a graph-based visualization of operation of the assets. The method also includes providing the generated visual error signature for the detected error as input to a machine learning model and utilizing the machine learning model to classify the visual error signature for the detected error as belonging to at least a given one of a plurality of error classes, the machine learning model being trained using historical visual error signatures for previously-detected errors. The method further includes identifying at least one action taken to remediate each of one or more previously-detected errors of the given error class and remediating the detected error utilizing one or more of the identified actions.

Diagnosing and remediating errors using visual error signatures

An apparatus includes a processing device comprising a processor coupled to a memory. The processing device is configured to obtain metrics characterizing performance, over two or more periods of time, of software container instances of each of a plurality of software container images. The processing device is also configured to determine, for each of the two or more periods of time, a periodic quality ranking of the plurality of software container images based at least in part on the obtained metrics. The processing device is further configured to generate an overall quality ranking of the plurality of software container images utilizing a consensus ranking aggregation algorithm configured to aggregate the periodic quality rankings of the plurality of software container images across the two or more periods of time, and to publish the overall quality ranking of the plurality of software container images to a software container registry.

Software container image quality ranking based on performance of instances of the software container images

An apparatus comprises a processing device configured to obtain runtime call pattern data for microservices in an information technology infrastructure, to generate a model of the runtime call pattern data characterizing transitions between states of the microservices, and to capture point of interest events from the runtime call pattern data utilizing the generated model. The processing device is also configured to determine, for a given sliding window time slot, association rules between the captured point of interest events, a given association rule characterizing a relationship between first and second point of interest events corresponding to first and second state transitions occurring during the given sliding window time slot for first and second ones of the microservices. The processing device is further configured to control operation of the microservices in the information technology infrastructure based at least in part on the determined association rules.

Controlling operation of microservices utilizing association rules determined from microservices runtime call pattern data

A method includes identifying at least one software update available for a given computing device, determining a state of the given computing device, and utilizing a machine-learning based predictive model to assess compatibility of the at least one software update with the given computing device based at least in part on the state of the given computing device, the machine learning-based predictive model being trained utilizing historical incident data for a plurality of incidents associated with application of software updates to a plurality of computing devices. The method also includes generating a recommendation notification indicating compatibility of the at least one software update with the given computing device, and providing the recommendation notification in conjunction with presentation of one or more user interface features controlling whether to apply the at least one software update to the given computing device.

Software update compatibility assessment

A method is used in monitoring an application in a computing environment. The method represents execution of the application on a system as a finite state machine. The finite state machine depicts at least one state of the application, where the state indicates at least one of successful application execution and unsuccessful application execution. The method identifies an error state within the finite state machine, where the error state indicates the unsuccessful application execution. The method identifies, by analyzing the finite state machine, a non-error state as a cause of the unsuccessful application execution, where the unsuccessful application execution is represented as a path comprising a plurality of states, where the path comprises the non-error state. The method maps the non-error state to a location in the application to identify the cause of the unsuccessful application execution.

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Bengaluru, India

Mohammad Rafey