Patents by Inventor Qingwei Lin
Qingwei Lin has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).
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Publication number: 20250245212Abstract: An anomaly monitor customization tool executes a selected subset of backend anomaly detectors to process a time-series dataset for a performance metric and receives, from the backend anomaly detectors, anomaly report data identifying a first set of events included in the time-series dataset and flagged as anomalies and characterizes the anomalies by assigning anomaly type classifiers. An alert rule is generated based on feedback pertaining to discrepancies between the first set of events flagged as anomalies and a second set of events in the time-series dataset that are of interest to the user, and the alert rule is used to generate modified configuration data that is, in turn, used to provision a customized anomaly monitor.Type: ApplicationFiled: January 31, 2024Publication date: July 31, 2025Inventors: Jeffrey Xin LI, Minghua MA, Yu KANG, Chaoyun ZHANG, Qingwei LIN, Yingnong DANG, Cong CHEN
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Publication number: 20250232158Abstract: A method, computer program product, and computing system for processing an incident request using a triage engine associated with a cloud computing system. A candidate triage group generative artificial intelligence (AI) model is identified by processing the incident request. An assignment recommendation is generated from the candidate triage group generative AI model by processing the incident request using the candidate triage group generative AI model using training data associated with the respective candidate triage group. A target triage group is selected for triaging the incident request by processing the assignment recommendation from the candidate triage group generative AI model using the triage engine.Type: ApplicationFiled: January 17, 2024Publication date: July 17, 2025Inventors: Ze Li, Yu Kang, Qingwei Lin, Murali Mohan Chintalapati, Minghua Ma
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Publication number: 20250147851Abstract: Systems and techniques for multi-phase cloud service node error prediction are described herein. A set of spatial metrics and a set of temporal metrics may be obtained for node devices in a cloud computing platform. The node devices may be evaluated using a spatial machine learning model and a temporal machine learning model to create a spatial output and a temporal output. One or more potentially faulty nodes may be determined based on an evaluation of the spatial output and the temporal output using a ranking model. The one or more potentially faulty nodes may be a subset of the node devices. One or more migration source nodes may be identified from one or more potentially faulty nodes. The one or more migration source nodes may be identified by minimization of a cost of false positive and false negative node detection.Type: ApplicationFiled: December 30, 2024Publication date: May 8, 2025Inventors: Qingwei LIN, Kaixin SUI, Yong XU
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Publication number: 20250086047Abstract: Methods and systems for detecting systemwide service issues by using anomaly localization. In an example, a method includes receiving time-series monitoring data for multiple services, the time-series monitoring data including multiple dimensions and an error metric; for the monitoring data from each service, evaluating scopes within the monitoring data based on an objective function for a time-series of the error metric to identify at least one anomalous scope, each scope including at least one dimension and a value for the dimension; based on evaluating the scopes, generating a ranked list of scopes for each service based on objective function scores for the scopes; correlating the ranked lists of scopes across the multiple services to identify a cross-service anomaly; and generating an alert for the services based on the cross-service anomaly, the alert indicating at least one scope as a potential root cause for the cross-service anomaly.Type: ApplicationFiled: November 27, 2024Publication date: March 13, 2025Applicant: Microsoft Technology Licensing, LLCInventors: Mohit VERMA, Julien HOACHUCK, Qingwei LIN, Pooja RANI, Namrata JAIN, Rakesh NAMINENI, Jimmy WONG, Si QIN, Yu KANG, Jeffrey Ding HE, Yingnong DANG, Jian ZHANG, Bo QIAO, Kamaljit BATH
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Patent number: 12216552Abstract: Systems and techniques for multi-phase cloud service node error prediction are described herein. A set of spatial metrics and a set of temporal metrics may be obtained for node devices in a cloud computing platform. The node devices may be evaluated using a spatial machine learning model and a temporal machine learning model to create a spatial output and a temporal output. One or more potentially faulty nodes may be determined based on an evaluation of the spatial output and the temporal output using a ranking model. The one or more potentially faulty nodes may be a subset of the node devices. One or more migration source nodes may be identified from one or more potentially faulty nodes. The one or more migration source nodes may be identified by minimization of a cost of false positive and false negative node detection.Type: GrantFiled: June 29, 2018Date of Patent: February 4, 2025Assignee: Microsoft Technology Licensing, LLCInventors: Qingwei Lin, Kaixin Sui, Yong Xu
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Publication number: 20250036448Abstract: The present application is directed to stranded resource recovery in a cloud computing environment. A resource utilization signal at each of a plurality of nodes that each hosts corresponding virtual machines (VMs) is measured. Based on each resource utilization signal, a set of candidate nodes is identified. Each candidate node comprises a stranded resource that is unutilized due to utilization of a bottleneck resource. The identification includes calculating an amount of the stranded resource at each candidate node. From a plurality of VMs hosted at the set of candidate nodes, a set of candidate VMs is identified for migration for stranded resource recovery. The identification includes calculating a score for each candidate VM based on a degree of imbalance between the stranded resource and the bottleneck resource at a candidate node hosting the candidate VM. Migration of at least one candidate VM in the set of candidate VMs is initiated.Type: ApplicationFiled: November 28, 2022Publication date: January 30, 2025Inventors: Saurabh AGARWAL, Bo QIAO, Chao DU, Jayden CHEN, Karthikeyan SUBRAMANIAN, Nisarg SHETH, Qingwei LIN, Si QIN, Thomas MOSCIBRODA, Luke Rafael RODRIGUEZ
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Patent number: 12197271Abstract: Systems and techniques for multi-factor cloud service storage device error prediction are described herein. A set of storage device metrics and a set of computing system metrics may be obtained. A feature set may be generated using the set of storage device metrics and the set of computing system metrics. Members of the feature set may be validated by evaluating a validation training dataset using the members of the feature set. A modified feature set may be created based on the validation. A storage device failure model may be created using the modified feature set. A storage device rating range may be determined by minimizing a cost of misclassification of a storage device. A set of storage devices to be labeled may be identified as having a high probability of failure.Type: GrantFiled: July 21, 2023Date of Patent: January 14, 2025Assignee: Microsoft Technology Licensing, LLCInventors: Yong Xu, Qingwei Lin, Kaixin Sui
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Patent number: 12189466Abstract: Methods and systems for detecting systemwide service issues by using anomaly localization. In an example, a method includes receiving time-series monitoring data for multiple services, the time-series monitoring data including multiple dimensions and an error metric; for the monitoring data from each service, evaluating scopes within the monitoring data based on an objective function for a time-series of the error metric to identify at least one anomalous scope, each scope including at least one dimension and a value for the dimension; based on evaluating the scopes, generating a ranked list of scopes for each service based on objective function scores for the scopes; correlating the ranked lists of scopes across the multiple services to identify a cross-service anomaly; and generating an alert for the services based on the cross-service anomaly, the alert indicating at least one scope as a potential root cause for the cross-service anomaly.Type: GrantFiled: October 28, 2022Date of Patent: January 7, 2025Assignee: Microsoft Technology Licensing, LLCInventors: Mohit Verma, Julien Hoachuck, Qingwei Lin, Pooja Rani, Namrata Jain, Rakesh Namineni, Jimmy Wong, Si Qin, Yu Kang, Jeffrey Ding He, Yingnong Dang, Jian Zhang, Bo Qiao, Kamaljit Bath
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Patent number: 12184527Abstract: According to implementations of the subject matter described herein, there is provided a solution of providing a health index of a service. In this solution, a plurality of incident information sets associated with a plurality of services are obtained. The plurality of services are provisioned in a computing environment. An incident information set indicates at least one incident reported during operation of a service. Respective health indices are determined for the plurality of services based on respective ones of the plurality of incident information sets and a health classification policy. The respective health indices indicate respective health statuses of the plurality of services and being determined from a same health index range. Through unified use of incident information, the determined health indices can indicate universal and consistent health statuses for different services.Type: GrantFiled: June 30, 2020Date of Patent: December 31, 2024Assignee: Microsoft Technology Licensing, LLCInventors: Yu Kang, Rulei Yu, Bo Qiao, Pu Zhao, Qingwei Lin, Jian Sun, Li Yang, Xiaofeng Gao, Pochian Lee, Dongmei Zhang, Zhangwei Xu, Liqun Li, Xu Zhang
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Publication number: 20240419506Abstract: An efficiency engine identifies container sizes for containers of a workload and allocates the containers across server clusters and nodes based on peak resource usage requirements of the containers. Runtime feedback signals are generated from monitors within the containers indicative of a quality of service and resource usage. A decision engine can identify a bin packing action to take based upon the runtime feedback signals, and a control plane can perform the identified bin packing actions to adjust bin packing based upon the runtime feedback signals. Also, adaptive adjustment can be performed based on feedback signals and using a prediction engine.Type: ApplicationFiled: September 14, 2021Publication date: December 19, 2024Inventors: Rahul MOHANA NARAYANAMURTHY, Ye YU, Yixin FANG, Si QIN, Jie YAN, Qingwei LIN, Maosen HUANG, Tao SHEN, Xiaofeng ZHEN
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Patent number: 12112214Abstract: The present disclosure relates to systems, methods, and computer readable media for predicting expansion failures and implementing defragmentation instructions based on the predicted expansion failures and other signals. For example, systems disclosed herein may apply a failure prediction model to determine an expansion failure prediction associated with an estimated likelihood that deployment failures will occur on a node cluster. The systems disclosed herein may further generate defragmentation instructions indicating a severity level that a defragmentation engine may execute on a cluster level to prevent expansion failures while minimizing negative customer impacts. By uniquely generating defragmentation instructions for each node cluster, a cloud computing system can minimize expansion failures, increase resource capacity, reduce costs, and provide access to reliable services to customers.Type: GrantFiled: July 19, 2023Date of Patent: October 8, 2024Assignee: Microsoft Technology Licensing, LLCInventors: Shandan Zhou, Saurabh Agarwal, Karthikeyan Subramanian, Thomas Moscibroda, Paul Naveen Selvaraj, Sandeep Ramji, Sorin Iftimie, Nisarg Sheth, Wanghai Gu, Ajay Mani, Si Qin, Yong Xu, Qingwei Lin
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Publication number: 20240143433Abstract: Methods and systems for detecting systemwide service issues by using anomaly localization. In an example, a method includes receiving time-series monitoring data for multiple services, the time-series monitoring data including multiple dimensions and an error metric; for the monitoring data from each service, evaluating scopes within the monitoring data based on an objective function for a time-series of the error metric to identify at least one anomalous scope, each scope including at least one dimension and a value for the dimension; based on evaluating the scopes, generating a ranked list of scopes for each service based on objective function scores for the scopes; correlating the ranked lists of scopes across the multiple services to identify a cross-service anomaly; and generating an alert for the services based on the cross-service anomaly, the alert indicating at least one scope as a potential root cause for the cross-service anomaly.Type: ApplicationFiled: October 28, 2022Publication date: May 2, 2024Applicant: Microsoft Technology Licensing, LLCInventors: Mohit VERMA, Julien HOACHUCK, Qingwei LIN, Pooja RANI, Namrata JAIN, Rakesh NAMINENI, Jimmy WONG, Si QIN, Yu KANG, Jeffrey Ding HE, Yingnong DANG, Jian ZHANG, Bo QIAO, Kamaljit BATH
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Publication number: 20230401103Abstract: A method for dynamically adjusting a number of virtual machines for a workload, includes: receiving a probability indicator for each of a plurality of N sequential stages, where N is a natural number greater than 1, of a likelihood that a virtual machine assigned to a workload will be evicted during the N sequential stages; predicting a target number of virtual machines to configure in a current stage for a subsequent stage from among the plurality of N sequential stages based on the probability indicator, a target capacity for the workload, and a current price for maintaining a virtual machine; and configuring a number of virtual machines for the workload during the current stage based on the target number to be loaded for the workload for the subsequent stage.Type: ApplicationFiled: June 9, 2022Publication date: December 14, 2023Inventors: Soumya RAM, Preston Tapley STEPHENSON, Alexander David FISCHER, Mahmoud SAYED, Robert Edward MINNEKER, Eli Cortex Custodio VILARINHO, Felipe VIEIRA FRUJERI, Inigo GOIRI PRESA, Sidhanth M. PANJWANI, Yandan WANG, Camille Jean COUTURIER, Jue ZHANG, Fangkai YANG, Si QIN, Qingwei LIN, Chetan BANSAL, Bowen PANG, Vivek GUPTA
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Publication number: 20230385141Abstract: Systems and techniques for multi-factor cloud service storage device error prediction are described herein. A set of storage device metrics and a set of computing system metrics may be obtained. A feature set may be generated using the set of storage device metrics and the set of computing system metrics. Members of the feature set may be validated by evaluating a validation training dataset using the members of the feature set. A modified feature set may be created based on the validation. A storage device failure model may be created using the modified feature set. A storage device rating range may be determined by minimizing a cost of misclassification of a storage device. A set of storage devices to be labeled may be identified as having a high probability of failure.Type: ApplicationFiled: July 21, 2023Publication date: November 30, 2023Inventors: Yong Xu, Qingwei LIN, Kaixin SUI
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Publication number: 20230359512Abstract: The present disclosure relates to systems, methods, and computer readable media for predicting expansion failures and implementing defragmentation instructions based on the predicted expansion failures and other signals. For example, systems disclosed herein may apply a failure prediction model to determine an expansion failure prediction associated with an estimated likelihood that deployment failures will occur on a node cluster. The systems disclosed herein may further generate defragmentation instructions indicating a severity level that a defragmentation engine may execute on a cluster level to prevent expansion failures while minimizing negative customer impacts. By uniquely generating defragmentation instructions for each node cluster, a cloud computing system can minimize expansion failures, increase resource capacity, reduce costs, and provide access to reliable services to customers.Type: ApplicationFiled: July 19, 2023Publication date: November 9, 2023Inventors: Shandan ZHOU, Saurabh AGARWAL, Karthikeyan SUBRAMANIAN, Thomas MOSCIBRODA, Paul Naveen SELVARAJ, Sandeep RAMJI, Sorin IFTIMIE, Nisarg SHETH, Wanghai GU, Ajay MANI, Si QIN, Yong XU, Qingwei LIN
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Patent number: 11748185Abstract: Systems and techniques for multi-factor cloud service storage device error prediction are described herein. A set of storage device metrics and a set of computing system metrics may be obtained. A feature set may be generated using the set of storage device metrics and the set of computing system metrics. Members of the feature set may be validated by evaluating a validation training dataset using the members of the feature set. A modified feature set may be created based on the validation. A storage device failure model may be created using the modified feature set. A storage device rating range may be determined by minimizing a cost of misclassification of a storage device. A set of storage devices to be labeled may be identified as having a high probability of failure.Type: GrantFiled: June 29, 2018Date of Patent: September 5, 2023Assignee: Microsoft Technology Licensing, LLCInventors: Yong Xu, Qingwei Lin, Kaixin Sui
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Patent number: 11726836Abstract: The present disclosure relates to systems, methods, and computer readable media for predicting expansion failures and implementing defragmentation instructions based on the predicted expansion failures and other signals. For example, systems disclosed herein may apply a failure prediction model to determine an expansion failure prediction associated with an estimated likelihood that deployment failures will occur on a node cluster. The systems disclosed herein may further generate defragmentation instructions indicating a severity level that a defragmentation engine may execute on a cluster level to prevent expansion failures while minimizing negative customer impacts. By uniquely generating defragmentation instructions for each node cluster, a cloud computing system can minimize expansion failures, increase resource capacity, reduce costs, and provide access to reliable services to customers.Type: GrantFiled: June 12, 2020Date of Patent: August 15, 2023Assignee: Microsoft Technology Licensing, LLCInventors: Shandan Zhou, Saurabh Agarwal, Karthikeyan Subramanian, Thomas Moscibroda, Paul Naveen Selvaraj, Sandeep Ramji, Sorin Iftimie, Nisarg Sheth, Wanghai Gu, Ajay Mani, Si Qin, Yong Xu, Qingwei Lin
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Publication number: 20230179501Abstract: According to implementations of the subject matter described herein, there is provided a solution of providing a health index of a service. In this solution, a plurality of incident information sets associated with a plurality of services are obtained. The plurality of services are provisioned in a computing environment. An incident information set indicates at least one incident reported during operation of a service. Respective health indices are determined for the plurality of services based on respective ones of the plurality of incident information sets and a health classification policy. The respective health indices indicate respective health statuses of the plurality of services and being determined from a same health index range. Through unified use of incident information, the determined health indices can indicate universal and consistent health statuses for different services.Type: ApplicationFiled: June 30, 2020Publication date: June 8, 2023Inventors: Yu Kang, Rulei Yu, Bo Qiao, Pu Zhao, Qingwei Lin, Jian Sun, Li Yang, Xiaofeng Gao, Pochian LEE, Dongmei ZHANG, Zhangwei Xu, Liqun Li, Xu ZHANG
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Patent number: D1052954Type: GrantFiled: August 15, 2024Date of Patent: December 3, 2024Inventor: Qingwei Lin
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Patent number: D1057511Type: GrantFiled: June 18, 2024Date of Patent: January 14, 2025Inventor: Qingwei Lin