Abstract: Systems for autonomous management of hyperconverged distributed computing and storage systems. A method embodiment commences upon receiving a set of system measurements that correspond to system metrics associated with the computing system. A user interface is presented to users to capture a set of user sentiment indications. Over a period of time, a time series of system measurements and a time series of user sentiment indications are captured and used to form a learning model that comprises dynamically-changing user sentiment correlations between the system measurements and the user sentiment. At some moment in time, a system metric threshold breach event occurs. The learning model is consulted to determine a tracking value between the set of user sentiment indications and the system metric pertaining to the system metric threshold. Based on the tracking value, the respective system metric threshold is adjusted to more closely track with the historical user sentiment indications.

Abstract: Systems for autonomous management of hyperconverged distributed computing and storage systems. A method embodiment commences upon receiving a set of system measurements that correspond to system metrics associated with the computing system. A user interface is presented to users to capture a set of user sentiment indications. Over a period of time, a time series of system measurements and a time series of user sentiment indications are captured and used to form a learning model that comprises dynamically-changing user sentiment correlations between the system measurements and the user sentiment. At some moment in time, a system metric threshold breach event occurs. The learning model is consulted to determine a tracking value between the set of user sentiment indications and the system metric pertaining to the system metric threshold. Based on the tracking value, the respective system metric threshold is adjusted to more closely track with the historical user sentiment indications.