Abstract: An embodiment may involve: transmitting, by a non-production computational instance and to a central computational system, a configuration for a service provided by the central computational system, wherein the non-production computational instance is arranged to test the configuration; appending, to the configuration at the non-production computational instance, a synchronization identifier to indicate that the configuration has been synchronized with the central computational system; receiving, by a production computational instance and from the non-production computational instance, a copy of the configuration; reading, by the production computational instance, the synchronization identifier from the copy of the configuration; determining that the synchronization identifier is not reflected as part of a synchronization history maintained at the production computational instance; and, in response to determining that the synchronization identifier is not reflected in the synchronization history, transmitting

Abstract: An embodiment may involve: transmitting, by a non-production computational instance and to a central computational system, a configuration for a service provided by the central computational system, wherein the non-production computational instance is arranged to test the configuration; appending, to the configuration at the non-production computational instance, a synchronization identifier to indicate that the configuration has been synchronized with the central computational system; receiving, by a production computational instance and from the non-production computational instance, a copy of the configuration; reading, by the production computational instance, the synchronization identifier from the copy of the configuration; determining that the synchronization identifier is not reflected as part of a synchronization history maintained at the production computational instance; and, in response to determining that the synchronization identifier is not reflected in the synchronization history, transmitting

Abstract: A system may include a central computational instance configured to provide a service to a plurality of computational instances, wherein the service is associated with configurations respectively corresponding to the computational instances. The system may also include a computational instance dedicated to a managed network and storing a synchronization history specifying information that was synchronized between the computational instance and the central computational instance.

Abstract: The present approach relates to techniques for efficiently monitoring time-series data for a metric of interest using limited subsets of the time-series data and, based on the modeling, to generate predictions for the metric. Based on the predictions for the metric, one or more actions may be configured to be taken when the predicted value for the metric is outside of a specified range or exceeds a specified threshold.

Abstract: A system may include a central computational instance configured to provide a service to a plurality of computational instances, wherein the service is associated with configurations respectively corresponding to the computational instances. The system may also include a computational instance dedicated to a managed network and storing a synchronization history specifying information that was synchronized between the computational instance and the central computational instance.

Abstract: The present approach relates to techniques for efficiently monitoring time-series data for a metric of interest using limited subsets of the time-series data and, based on the modeling, to generate predictions for the metric. Based on the predictions for the metric, one or more actions may be configured to be taken when the predicted value for the metric is outside of a specified range or exceeds a specified threshold.

Abstract: The present approach relates to techniques for efficiently monitoring time-series data for a metric of interest using limited subsets of the time-series data and, based on the modeling, to generate predictions for the metric. Based on the predictions for the metric, one or more actions may be configured to be taken when the predicted value for the metric is outside of a specified range or exceeds a specified threshold.

Abstract: The present approach relates to techniques for efficiently monitoring time-series data for a metric of interest using limited subsets of the time-series data and, based on the modeling, to generate predictions for the metric. Based on the predictions for the metric, one or more actions may be configured to be taken when the predicted value for the metric is outside of a specified range or exceeds a specified threshold.