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公开(公告)号：US20200322239A1
公开(公告)日：2020-10-08
申请号：US16906907
申请日：2020-06-19
Applicant: Lightbend, Inc.
Inventor： Amit SASTURKAR , Vishal SURANA , Omer Emre VELIPASAOGLU , Abhinav A. VORA , Aiyesha Lowe MA
Abstract: The technology disclosed relates to understanding traffic patterns in a network with a multitude of processes running on numerous hosts. In particular, it relates to using at least one of rule based classifiers and machine learning based classifiers for clustering processes running on numerous hosts into local services and clustering the local services running on multiple hosts into service clusters, using the service clusters to aggregate communications among the processes running on the hosts and generating a graphic of communication patterns among the service clusters with available drill-down into details of communication links. It also relates to using predetermined command parameters to create service rules and machine learning based classifiers that identify host-specific services. In one implementation, user feedback is used to create new service rules or classifiers and/or modify existing service rules or classifiers so as to improve accuracy of the identification of the host-specific services.
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公开(公告)号：US20180205620A1
公开(公告)日：2018-07-19
申请号：US15919064
申请日：2018-03-12
Applicant: Lightbend, Inc.
Inventor： Amit SASTURKAR , Vishal SURANA , Omer Emre VELIPASAOGLU , Abhinav A. VORA , Aiyesha Lowe MA
CPC classification number: H04L43/045 , H04L29/06 , H04L29/08072 , H04L43/062 , H04W12/08
Abstract: The technology disclosed relates to understanding traffic patterns in a network with a multitude of processes running on numerous hosts. In particular, it relates to using at least one of rule based classifiers and machine learning based classifiers for clustering processes running on numerous hosts into local services and clustering the local services running on multiple hosts into service clusters, using the service clusters to aggregate communications among the processes running on the hosts and generating a graphic of communication patterns among the service clusters with available drill-down into details of communication links. It also relates to using predetermined command parameters to create service rules and machine learning based classifiers that identify host-specific services. In one implementation, user feedback is used to create new service rules or classifiers and/or modify existing service rules or classifiers so as to improve accuracy of the identification of the host-specific services.
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公开(公告)号：US20190250971A1
公开(公告)日：2019-08-15
申请号：US16276431
申请日：2019-02-14
Applicant: Lightbend, Inc.
Inventor： Amit SASTURKAR , Arun KEJARIWAL , Uday K. CHETTIAR , Vishal SURANA , Omer Emre VELIPASAOGLU , Dhruv Hemchand JAIN , Mohamed A. ABDELHAFEZ
Abstract: The technology disclosed relates to building ensemble analytic rules for reusable operators and tuning an operations monitoring system. In particular, it relates to analyzing a metric stream by applying an ensemble analytical rule. After analysis of the metric stream by applying the ensemble analytical rule, quantized results are fed back for expert analysis. Then, one or more type I or type II errors are identified in the quantized results, and one or more of the parameters of the operators are automatically adjusted to correct the identified errors. The metric stream is further analyzed by applying the ensemble analytical rule with the automatically adjusted parameters.
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公开(公告)号：US20190018667A1
公开(公告)日：2019-01-17
申请号：US16133608
申请日：2018-09-17
Applicant: Lightbend, Inc.
Inventor： Abhinav VORA , Aiyesha MA , Amit SASTURKAR , Oliver KEMPE , Narayanan ARUNACHALAM , Alan NGAI , Vishal SURANA , Omer Emre VELIPASAOGLU
Abstract: The technology disclosed relates to sub-clustering within service clusters in real-time. In particular, it relates to accessing a network topology that records node data and connection data including processes running on numerous hosts grouped into local services on the hosts, the local services running on multiple hosts grouped into service clusters and sub-clusters of service clusters, and network connections used by the service clusters to connect the hosts grouped into service connections, wherein the node data includes software versions of the processes and process data with configuration files and clustering the multiple hosts with the service clusters into the sub-clusters based at least in part on the software versions.
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Patent Agency Ranking
公开(公告)号：US20180167260A1
公开(公告)日：2018-06-14
申请号：US15841198
申请日：2017-12-13
Applicant: Lightbend, Inc.
Inventor： Omer Emre VELIPASAOGLU , Arun KEJARIWAL , Alan Honkwan NGAI , Craig David UPSON , Uday K. CHETTIAR
CPC classification number: H04L41/0609 , H04L5/0058 , H04L41/0654 , H04L41/0681 , H04L41/22 , H04L43/067
Abstract: The technology disclosed relates to differential analysis of sets of time series pairs. In particular, it relates to building estimators of magnitude of difference between two time series. After the basic estimators are built, they are combined into ensemble estimators using linear or nonlinear prediction models to improve their accuracy. In one application, the ensemble is used for estimating the magnitudes of difference over sets of metric pairs observed from distributed applications and systems running over a computer network. The metric pairs are then ranked in decreasing order of magnitude of difference to guide an operator in prioritizing his root cause analysis of faults, thereby reducing the time-to-resolution of problems.
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公开(公告)号：US20200379892A1
公开(公告)日：2020-12-03
申请号：US16891015
申请日：2020-06-02
Applicant: Lightbend, Inc.
Inventor： Omer Emre VELIPASAOGLU , Alan Honkwan NGAI
Abstract: The disclosed technology teaches configuring and reconfiguring an application running on a system, receiving a test configuration file with performance evaluation criteria and bounds for configuration dimensions defining a configuration hyperrectangle. The technology includes instantiating a reference instance and a test instance, subject to similar operating stressors and automatically testing alternative configurations within the configuration hyperrectangle, configuring and reconfiguring components of the test instance in the test cycles at configuration points within the configuration hyperrectangle, and applying a test stimulus to both instances for a dynamically determined cycle time. A test cycle time is dynamically determined by applying the performance evaluation criteria to determine a performance difference, evaluating stabilization of performance difference as the cycle progresses, dynamically determining the cycle to be complete when a stabilization criteria applied to the performance difference is met, advancing to a next configuration point until a test completion criteria is met, and reporting results.
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公开(公告)号：US20200319951A1
公开(公告)日：2020-10-08
申请号：US16909920
申请日：2020-06-23
Applicant: Lightbend, Inc.
Inventor： Amit SASTURKAR , Arun KEJARIWAL , Uday K. CHETTIAR , Vishal SURANA , Omer Emre VELIPASAOGLU , Dhruv Hemchand JAIN , Mohamed A. ABDELHAFEZ
Abstract: The technology disclosed relates to building ensemble analytic rules for reusable operators and tuning an operations monitoring system. In particular, it relates to analyzing a metric stream by applying an ensemble analytical rule. After analysis of the metric stream by applying the ensemble analytical rule, quantized results are fed back for expert analysis. Then, one or more type I or type II errors are identified in the quantized results, and one or more of the parameters of the operators are automatically adjusted to correct the identified errors. The metric stream is further analyzed by applying the ensemble analytical rule with the automatically adjusted parameters.
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公开(公告)号：US20190235944A1
公开(公告)日：2019-08-01
申请号：US16382074
申请日：2019-04-11
Applicant: Lightbend, Inc.
Inventor： Omer Emre VELIPASAOGLU , Vishal SURANA , Amit SASTURKAR
CPC classification number: G06F11/079 , G06F11/0709 , G06F11/0751 , G06F11/0772 , G06F11/0787 , G06F11/3006 , G06F11/32 , G06F11/323 , G06F11/34 , G06F11/3409 , G06F11/3452 , H04L41/064 , H04L41/142 , H04L41/147 , H04L41/16 , H04L41/5025 , H04L43/045
Abstract: The technology disclosed relates to learning how to efficiently display anomalies in performance data to an operator. In particular, it relates to assembling performance data for a multiplicity of metrics across a multiplicity of resources on a network and training a classifier that implements at least one circumstance-specific detector used to monitor a time series of performance data or to detect patterns in the time series. The training includes producing a time series of anomaly event candidates including corresponding event information used as input to the detectors, generating feature vectors for the anomaly event candidates, selecting a subset of the candidates as anomalous instance data, and using the feature vectors for the anomalous instance data and implicit and/or explicit feedback from users exposed to a visualization of the monitored time series annotated with visual tags for at least some of the anomalous instances data to train the classifier.
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公开(公告)号：US20190158369A1
公开(公告)日：2019-05-23
申请号：US16261134
申请日：2019-01-29
Applicant: Lightbend, Inc.
Inventor： Amit SASTURKAR , Vishal SURANA , Omer Emre VELIPASAOGLU , Abhinav A. VORA , Aiyesha Lowe MA
Abstract: The technology disclosed relates to understanding traffic patterns in a network with a multitude of processes running on numerous hosts. In particular, it relates to using at least one of rule based classifiers and machine learning based classifiers for clustering processes running on numerous hosts into local services and clustering the local services running on multiple hosts into service clusters, using the service clusters to aggregate communications among the processes running on the hosts and generating a graphic of communication patterns among the service clusters with available drill-down into details of communication links. It also relates to using predetermined command parameters to create service rules and machine learning based classifiers that identify host-specific services. In one implementation, user feedback is used to create new service rules or classifiers and/or modify existing service rules or classifiers so as to improve accuracy of the identification of the host-specific services.
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