Salesforce Com, Inc.

Salesforce, Inc.

Ana Bertran

Sudhish Iyer

Lauren Valdivia

Daisuke Kawamoto

Nicholas Roan

Xiaohong Huang

Zeqiang Wang

Carl Morgenstern

Nicholas Murray

Dmitry Volkov

Zhanara Amans

Michael Elizarov

Jiaping Zhang

Garren Bellew

Gerald Gibson, Jr

Elena Novakovskaia

Philip Dolle

Yuriy Loukachev

Kyle Gilson

Anish Kanchan

Steve Bobrowski

Chin Lee

Kunal Vashi

Gautham Ramachandran

Zahid Rahman

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.

Anomaly detection and root cause analysis in a multi-tenant environment

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.

System and methods for risk assessment in a multi-tenant cloud environment

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.

Scalable multi-channel content distribution and optimization using peer comparison

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.

Technologies for capacity remediation in multi-tenant cloud environments

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.

Database system performance degradation detection

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San Francisco, CA, United States of America

Ana Bertran