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公开(公告)号：US12086016B2
公开(公告)日：2024-09-10
申请号：US16917238
申请日：2020-06-30
Applicant: salesforce.com, inc.
Inventor： Ana Bertran , Yuriy Loukachev , Xiaohong Huang , Nicholas Murray , Nicholas Roan , Lauren Valdivia , Anish Kanchan , Kyle Gilson
CPC classification number: G06F11/0709 , G06F9/505 , G06F9/5083 , G06F11/079 , G06F11/3006 , G06F11/323 , G06F11/328 , G06F11/3433 , G06F11/3452 , G06F11/3466 , G06F11/0712 , G06F11/0793 , G06F2201/81
Abstract: System and methods are described for anomaly detection and root cause analysis in database systems, such as multi-tenant environments. In one implementation, a method comprises receiving an activity signal representative of resource utilization within a multi-tenant environment; detecting a plurality of anomalies in the activity signal; computing a priority score for each of the plurality of anomalies; correlating at least a subset of the plurality of anomalies to one or more performance metrics of the multi-tenant environment; and transmitting a remediation signal to one or more devices in the multi-tenant environment based on the correlations and the priority scores.
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公开(公告)号：US11663544B2
公开(公告)日：2023-05-30
申请号：US16774822
申请日：2020-01-28
Applicant: salesforce.com, inc.
Inventor： Jiaping Zhang , Ana Bertran , Elena Novakovskaia , Zhanara Amans , Garren Bellew , Philip Dolle
IPC: G06Q10/06 , G06N20/00 , G06Q10/10 , G06Q10/0635 , G06Q10/0637
CPC classification number: G06Q10/0635 , G06N20/00 , G06Q10/06375 , G06Q10/10
Abstract: A method of early warning and risk assessment of incidents in a multi-tenant cloud environment is provided. The method includes: capturing a plurality of data metrics; automatically generating derived features from the plurality of captured data metrics; automatically selecting risk assessment features from the derived features and the captured data metrics; and predicting the risk of an incident in the multi-tenant cloud environment within a specified time window in the future and one or more possible root causes of the incident by applying the newly selected risk assessment features to a trained risk assessment model. The trained risk assessment model has been trained using machine learning techniques to predict the risk of an incident in the multi-tenant cloud environment within a specified time window in the future, provide an explanation of possible root causes of the incident, and assign a strength level to each possible root cause.
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公开(公告)号：US11055162B2
公开(公告)日：2021-07-06
申请号：US16176872
申请日：2018-10-31
Applicant: salesforce.com, inc.
Inventor： Dmitry Volkov , Daisuke Kawamoto , Ana Bertran , Lauren Valdivia , Sudhish Iyer , Xiaohong Huang
Abstract: Among other things, embodiments of the present disclosure relate to detecting performance degradation in database systems. For example, some embodiments of the present disclosure help to identify events associated with anomalous database system parameter states and assess the severity of such anomalous events. Other embodiments may be described and/or claimed.
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Patent Agency Ranking
公开(公告)号：US20210406148A1
公开(公告)日：2021-12-30
申请号：US16917238
申请日：2020-06-30
Applicant: salesforce.com, inc.
Inventor： Ana Bertran , Yuriy Loukachev , Xiaohong Huang , Nicholas Murray , Nicholas Roan , Lauren Valdivia , Anish Kanchan , Kyle Gilson
Abstract: System and methods are described for anomaly detection and root cause analysis in database systems, such as multi-tenant environments. In one implementation, a method comprises receiving an activity signal representative of resource utilization within a multi-tenant environment; detecting a plurality of anomalies in the activity signal; computing a priority score for each of the plurality of anomalies; correlating at least a subset of the plurality of anomalies to one or more performance metrics of the multi-tenant environment; and transmitting a remediation signal to one or more devices in the multi-tenant environment based on the correlations and the priority scores.
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公开(公告)号：US20210232995A1
公开(公告)日：2021-07-29
申请号：US16774822
申请日：2020-01-28
Applicant: salesforce.com, inc.
Inventor： Jiaping Zhang , Ana Bertran , Elena Novakovskaia , Zhanara Amans , Garren Bellew , Philip Dolle
Abstract: A method of early warning and risk assessment of incidents in a multi-tenant cloud environment is provided. The method includes: capturing a plurality of data metrics; automatically generating derived features from the plurality of captured data metrics; automatically selecting risk assessment features from the derived features and the captured data metrics; and predicting the risk of an incident in the multi-tenant cloud environment within a specified time window in the future and one or more possible root causes of the incident by applying the newly selected risk assessment features to a trained risk assessment model. The trained risk assessment model has been trained using machine learning techniques to predict the risk of an incident in the multi-tenant cloud environment within a specified time window in the future, provide an explanation of possible root causes of the incident, and assign a strength level to each possible root cause.
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公开(公告)号：US11223676B1
公开(公告)日：2022-01-11
申请号：US17158465
申请日：2021-01-26
Applicant: salesforce.com, inc.
Inventor： Gautham Ramachandran , Ana Bertran , Zeqiang Wang , Gerald Gibson, Jr. , Michael Elizarov
IPC: G06F15/173 , H04L29/08 , G06F11/34 , G06N7/00 , G06F11/30
Abstract: A method of data processing includes identifying a segment of entity identifiers that are associated with a target tenant and correspond to a set of clients that are to receive at least one content object via a first channel of a plurality of supported channels. The method includes modifying a feature associated with communication of content for a test subset of the segment relative to a control subset of the segment, determining a first metric corresponding to the control subset and the test subset in association with the communication of the content via the first channel and a second metric associated with the target tenant over a second channel of the plurality of channels. The method includes comparing the second metric to a metric associated with a peer group of tenants, and adjusting subsequent communications for the target based at least in part on the comparing and the first metric.
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公开(公告)号：US11088925B2
公开(公告)日：2021-08-10
申请号：US15876548
申请日：2018-01-22
Applicant: salesforce.com, inc.
Inventor： Ana Bertran , Carl Morgenstern , Daisuke Kawamoto , Nicholas Roan , Steve Bobrowski , Sudhish Iyer , Chin Lee , Kunal Vashi , Zahid Rahman
Abstract: Multitier, multitenant architecture of pods comprise multiple stacks with different metrics and workload compositions that constantly change over time. A computer system may identify an overall pod time-to-live (TTL) based on the changing metrics and workloads. The TTL may be a forecasted time that pod remediation is needed to avoid negative impact on pod performance and customer experience. Additionally, the computer system may identify the appropriate remediation(s) for each pod. The computer system may compare and prioritize remediations across a collection of pods with different configurations and workload characteristics based on the TTLs. Other embodiments may be described and/or claimed.
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公开(公告)号：US20230230010A1
公开(公告)日：2023-07-20
申请号：US17578642
申请日：2022-01-19
Applicant: salesforce.com, inc.
Inventor： Gautham Ramachandran , Gerald Gibson, JR. , Zeqiang Wang , Ana Bertran
CPC classification number: G06Q10/06393 , G06Q30/0201
Abstract: Methods, computer readable media, and devices for quantifying an infrastructure service health as a score and optimizing performance of the infrastructure service based on benchmarks of dynamically identified control groups are disclosed. One method may include determining, for an infrastructure service of an organization, a metric health score for one or more metrics and an overall health score for the organization, creating, for at least one of the metrics, a number of control groups based on a timeframe criteria and including a set of organizations having a metric health score for the timeframe criteria similar to the organization, and maximizing performance of the infrastructure service using machine learning to compare, for at least one metric, performance impacts to the organization based on service changes with the number of control groups for the at least one metric.
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