Abstract: A method and apparatus for selecting one or more instances to perform database recovery is provided. According to one aspect, one or more performance metrics are determined for each instance on each node in a cluster. Based on these performance metrics, which may be weighted to give one or more metrics more significance relative to the other metrics, an overall score is determined for each instance on each surviving node when a node in the cluster fails. One or more instances with the highest of these scores are selected. The selected instances are assigned the task of performing the database recovery process. Because instances best able to handle to database recovery process are selected, the database recovery process consumes less time, thereby making data accessible in less time after a node fails.

Abstract: A method and apparatus for selecting one or more instances to perform database recovery is provided. According to one aspect, one or more performance metrics are determined for each instance on each node in a cluster. Based on these performance metrics, which may be weighted to give one or more metrics more significance relative to the other metrics, an overall score is determined for each instance on each surviving node when a node in the cluster fails. One or more instances with the highest of these scores are selected. The selected instances are assigned the task of performing the database recovery process. Because instances best able to handle to database recovery process are selected, the database recovery process consumes less time, thereby making data accessible in less time after a node fails.

Abstract: Runtime load balancing of work across a clustered computing system involves servers calculating, and clients utilizing, current service performance grades of each instance in the system. A performance grade for an instance is based on performance metrics for that instance, where the computation used may vary by policy. Examples of possible policies include: (a) using estimated bandwidth as a performance grade, (b) using spare capacity as a performance grade, or (c) using response time as a performance grade. Clients distribute work requests across servers in the system as the requests arrive. Work requests can be distributed according to performance grades, and/or flags associated with the performance grades. Automatically and intelligently directing work requests to the best server instances, based on real-time service performance metrics, minimizes the need to manually relocate work within the clustered system.