Abstract: Approaches for manage and measuring workload on computer resources by measuring workload in ways that are independent of physical hardware and that correlate to ways in which it is desirable to measure and manage workload for applications and clients. Specifically, logical abstractions of workload, referred to as services, are defined for a cluster of nodes running a multi-node database server of a database. A service is a category of work hosted on a subset of multi-node database servers, such as work that is performed for an online order entry application. The workload is measured and demarcated by the service no matter which node in a single or multi-node system performs the service, providing a global view of performance realized by the system.

Abstract: Approaches are used for efficiently and effectively managing the dynamic allocation of resources of multi-node database systems between services provided by the multi-node database server. A service is a category of work that is hosted on the database server. The approaches manage allocation of resources at different levels. For services that use a particular database, the performance realized by the services is monitored. Resources assigned to the database are allocated between these services to ensure performance goals for each are met. Resources assigned to a cluster of nodes are allocated between the databases to ensure that performance goals for all the services that use the databases are met. Resources assigned to a farm of clusters are assigned amongst clusters based on service level agreements and back-end policies. The approach uses a hierarchy of directors to manage resources at the different levels.

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.

Abstract: Approaches are used for efficiently and effectively managing the dynamic allocation of resources of multi-node database systems between services provided by the multi-node database server. A service is a category of work that is hosted on the database server. The approaches manage allocation of resources at different levels. For services that use a particular database, the performance realized by the services is monitored. Resources assigned to the database are allocated between these services to ensure performance goals for each are met. Resources assigned to a cluster of nodes are allocated between the databases to ensure that performance goals for all the services that use the databases are met. Resources assigned to a farm of clusters are assigned amongst clusters based on service level agreements and back-end policies. The approach uses a hierarchy of directors to manage resources at the different levels.

Abstract: Approaches for manage and measuring workload on computer resources by measuring workload in ways that are independent of physical hardware and that correlate to ways in which it is desirable to measure and manage workload for applications and clients. Specifically, logical abstractions of workload, referred to as services, are defined for a cluster of nodes running a multi-node database server of a database. A service is a category of work hosted on a subset of multi-node database servers, such as work that is performed for an online order entry application. The workload is measured and demarcated by the service no matter which node in a single or multi-node system performs the service, providing a global view of performance realized by the system.

Abstract: Mechanisms are provided for preserving data wherein one or more nodes in a distributed computing system experiences an error. In one embodiment, when an error occurs, an error event is identified. Based on this error event, a set of identified execution units is suspended and a set of identified data is collected. All suspended execution units are then released, i.e., allowed to continue execution at the point where the units were suspended. The data collected during suspension is then used to diagnose the cause of the error.