Salesforce Com, Inc.

Daisuke Kawamoto

Sudhish Iyer

Ana Bertran

Dmitry Volkov

Carl Morgenstern

Lauren Valdivia

Xiaohong Huang

Nicholas Roan

Steve Bobrowski

Chin Lee

Kunal Vashi

Zahid Rahman

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

System and methods are described for deriving normalized infrastructure metrics to represent customer experience of a cloud computing system, continuously evaluating a profile for a tenant of the cloud computing system and determining a customer experience indicator for the tenant in real-time or near real-time, without losing accuracy, based at least in part on the infrastructure metrics and the profile.

Computation of customer experience metrics in a multi-tenant cloud computing environment

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San Francisco, CA, United States of America

Daisuke Kawamoto