Abstract: Automated computer-implemented processes and systems manage and troubleshoot a service provided by a distributed application executing in a distributed computing system. Processes query objects of the distributed computing system to identify candidate objects for addition to the service. Processes generate recommendations in a graphical user interface (“GUI”) that enable a user to select and enroll the one or more candidate objects into the service via the GUI. Processes monitor a key performance indicator (“KPI”) of the service for violations of a corresponding service level object (“SLO”) threshold. When the KPI violates the SLO threshold, processes determine a root cause of a performance problem with the service based on a metric-association rule associated with the KPI violation of the SLO threshold and displays the performance problem and a recommendation that corrects the performance problem in a GUI.

Abstract: The current document is directed to methods and systems that collect metric data within computing facilities, including large data centers and cloud-computing facilities. In a described implementation, two or more metric-data sets are combined to generate a multidimensional metric-data set. The multidimensional metric-data set is compressed for efficient storage by clustering the multidimensional data points within the multidimensional metric-data set to produce a covering subset of multidimensional data points and by then representing the multidimensional-data-point members of each cluster by a cluster identifier rather than by a set of floating-point values, integer values, or other types of data representations. The covering set is constructed to ensure that the compression does not result in greater than a specified level of distortion of the original data.

Abstract: The current document is directed to methods and systems that collect metric data within computing facilities, including large data centers and cloud-computing facilities. In a described implementation, two or more metric-data sets are combined to generate a multidimensional metric-data set. The multidimensional metric-data set is compressed for efficient storage by clustering the multidimensional data points within the multidimensional metric-data set to produce a covering subset of multidimensional data points and by then representing the multidimensional-data-point members of each cluster by a cluster identifier rather than by a set of floating-point values, integer values, or other types of data representations. The covering set is constructed to ensure that the compression does not result in greater than a specified level of distortion of the original data.