Abstract: A method for determining early warning on workload in a database system includes collecting periodically data samples concerning a plurality of performance counters and determining periodically one or more data points representing a statistical characteristic of said data samples over a first time period. Based on a plurality of said data points, trend points for each of a plurality of performance counter components are determined, said trend points representing a statistical characteristic determined on basis of the respective data points. Each performance counter component's current trend, determined based on the trend points over a second time period, is compared to a determined baseline trend of the same performance counter component. Any significant deviations determined based on comparisons of the current trends and the baseline trends are classified into severity classes, and an early warning of a possible future problem in the DBMS system is given.

Abstract: A capacity planning method for Always On Availability Group, AG, cluster renewal includes selecting a source AG cluster to be replaced with a target AG cluster, selecting at least one performance monitor and monitoring performance of instances and databases to obtain time series. Trends of the time series are defined and at least one benchmark value is obtained for source and target nodes and calculating at least one benchmark ratio. The time series are adjusted based on the defined trends and the at least one benchmark ratio. A logical grouping of instances and databases is constituted, and workloads of the logical groups are calculated for each node on basis of the adjusted time series. A required capacity of the target AG cluster nodes is predicted. Finally, the required capacity of the target AG cluster nodes is compared to verify, whether the target node has sufficient capacity.

Abstract: A method for predicting future performance of a database query plan includes performing a plurality of time series analyses for time series associated with a plurality of selected database queries and a selected query, wherein each analyzed time series is generated based on stored query performance counter (QPC) data, and wherein the plurality of time series represents a plurality of different time dimensions, and wherein each time series analysis is performed by forming a time series corresponding to the selected time dimensions and covering the time span, wherein the time series includes data points representing a portion of stored QPC data that is associated with the selected database query and the selected QPC.

Abstract: A method for capacity planning of a DBSM network, includes selecting at least one RESOURCE against which the TARGET_SYSTEM will be optimized, selecting at least one performance counter for which a planning engine forecast is created, collecting said performance counter MONITORING_DATA, defining rules for each performance counter against which a predicted TIME_SERIES is validated, adjusting SOURCE_SYSTEM OBJECT CONSTRAINTS and TIME_SERIES data, defining TARGET_SYSTEM LOGICAL_TOPOLOGY with desired OBJECTS setup, preferred configurations, maximum count for each setup OBJECT type and possible processing order, adjusting TARGET_SYSTEM OBJECT CONSTRAINTS, benchmarks and TIME_SERIES data, evaluating and comparing all needed refactored SOURCE_SYSTEM OBJECT TIME_SERIES against given TARGET_SYSTEM with preconfigured LOGICAL_TOPOLOGY and processing order of OBJECT SETUP_DATA to have minimum amount of primary target RESOURCE defined in total for all TARGET_SYSTEM host servers meeting their respective CONSTRAINTS and outp

Abstract: A capacity planning method for Always On Availability Group, AG, cluster renewal includes selecting a source AG cluster to be replaced with a target AG cluster, selecting at least one performance monitor and monitoring performance of instances and databases to obtain time series. Trends of the time series are defined and at least one benchmark value is obtained for source and target nodes and calculating at least one benchmark ratio. The time series are adjusted based on the defined trends and the at least one benchmark ratio. A logical grouping of instances and databases is constituted, and workloads of the logical groups are calculated for each node on basis of the adjusted time series. A required capacity of the target AG cluster nodes is predicted. Finally, the required capacity of the target AG cluster nodes is compared to verify, whether the target node has sufficient capacity.

Abstract: A method for capacity planning of a DBSM network, includes selecting at least one RESOURCE against which the TARGET_SYSTEM will be optimized, selecting at least one performance counter for which a planning engine forecast is created, collecting said performance counter MONITORING_DATA, defining rules for each performance counter against which a predicted TIME_SERIES is validated, adjusting SOURCE_SYSTEM OBJECT CONSTRAINTS and TIME_SERIES data, defining TARGET_SYSTEM LOGICAL_TOPOLOGY with desired OBJECTS setup, preferred configurations, maximum count for each setup OBJECT type and possible processing order, adjusting TARGET_SYSTEM OBJECT CONSTRAINTS, benchmarks and TIME_SERIES data, evaluating and comparing all needed refactored SOURCE_SYSTEM OBJECT TIME_SERIES against given TARGET_SYSTEM with preconfigured LOGICAL_TOPOLOGY and processing order of OBJECT SETUP_DATA to have minimum amount of primary target RESOURCE defined in total for all TARGET_SYSTEM host servers meeting their respective CONSTRAINTS and outp