Abstract: Disclosed are techniques for automatically determining whether a new disruption of service alert corresponds to a pattern of failures and automatically applying remedies based on the determined pattern. Datasets of historical disruption of service alerts on networked computing clusters are used to train a machine learning algorithm to identify patterns between alerts. When a new disruption of service alert is received, historical disruption of service alerts for the originating networked computing cluster are also received and provided as input to the machine learning model. The machine learning model then automatically determines whether the new alert fits a pattern with the historical alerts from the same cluster, and when a fit is found, remedial actions are sourced from the alerts that fit the pattern to be applied automatically to the originating networked computing cluster.

Abstract: A mechanism is provided in a data processing system for creating clusters on demand. The mechanism installs a cluster on the data processing system. The cluster comprises a master node and a managed node having distributed software installed thereon. The mechanism stores state of cluster on external volumes and removes references specific to the cluster from the state of the cluster stored on the external volumes. The mechanism takes a snapshot of the state of the cluster stored on the external volumes to form a set of volume templates and takes a snapshot of images of the master node and the managed node to form a set of node type images.

Abstract: A mechanism is provided in a data processing system for creating clusters on demand. The mechanism installs a cluster on the data processing system. The cluster comprises a master node and a managed node having distributed software installed thereon. The mechanism stores state of cluster on external volumes and removes references specific to the cluster from the state of the cluster stored on the external volumes. The mechanism takes a snapshot of the state of the cluster stored on the external volumes to form a set of volume templates and takes a snapshot of images of the master node and the managed node to form a set of node type images.

Abstract: A mechanism is provided in a data processing system for creating clusters on demand. The mechanism installs a cluster on the data processing system. The cluster comprises a master node and a managed node having distributed software installed thereon. The mechanism stores state of cluster on external volumes and removes references specific to the cluster from the state of the cluster stored on the external volumes. The mechanism takes a snapshot of the state of the cluster stored on the external volumes to form a set of volume templates and takes a snapshot of images of the master node and the managed node to form a set of node type images.

Abstract: A mechanism is provided in a data processing system for creating clusters on demand. The mechanism installs a cluster on the data processing system. The cluster comprises a master node and a managed node having distributed software installed thereon. The mechanism stores state of cluster on external volumes and removes references specific to the cluster from the state of the cluster stored on the external volumes. The mechanism takes a snapshot of the state of the cluster stored on the external volumes to form a set of volume templates and takes a snapshot of images of the master node and the managed node to form a set of node type images.