Salesforce Com, Inc.

Salesforce, Inc.

Nicholas Roan

Ana Bertran

Sudhish Iyer

Lauren Valdivia

Daisuke Kawamoto

Xiaohong Huang

Carl Morgenstern

Nicholas Murray

Chin Lee

Kunal Vashi

Zahid Rahman

Yuriy Loukachev

Kyle Gilson

Anish Kanchan

Steve Bobrowski

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

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

Growing community of inventors

San Francisco, CA, United States of America

Nicholas Roan