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.

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.