Abstract: Techniques are disclosed for combining multiple benchmarks for use in assessing characteristics of a computing system. For example, a method for configuring and running multiple benchmarks includes the following steps. A multiple benchmark specification is obtained. The multiple benchmark specification includes multiple individual benchmark specifications and a multiple benchmark workflow describing an ordering according to which the multiple individual benchmarks are to be configured and run. The multiple benchmarks are configured and run according to the ordering identified in the multiple benchmark workflow. Results of the multiple benchmark runs are recorded. One or more specifications or results associated with at least one of the multiple benchmarks are utilized as part of a benchmark specification for at least another of the multiple benchmarks so as to provide consistency between at least the one and the other of the multiple benchmarks.

Abstract: Methods and systems are provided for executing a simulation of an operation in a data processing system. In one implementation, the method includes executing an operation under a first set of conditions, determining a characteristic associated with the execution of the operation under the first set of conditions, and executing a simulation of the operation under a second set of conditions different from the first set of conditions. The simulation of the operation is constrained by the determined characteristic. The method can further include determining a cost/benefit of executing the operation under a set of conditions different from the first set of conditions based at least in part on the simulation.

Abstract: In one embodiment, functional system elements are added to an autonomic manager to enable automatic online sample interval selection. In another embodiment, a method for determining the sample interval by continually characterizing the system workload behavior includes monitoring the system data and analyzing the degree to which the workload is stationary. This makes the online optimization method less sensitive to system noise and capable of being adapted to handle different workloads. The effectiveness of the autonomic optimizer is thereby improved, making it easier to manage a wide range of systems.

Abstract: In one embodiment, functional system elements are added to an autonomic manager to enable automatic online sample interval selection. In another embodiment, a method for determining the sample interval by continually characterizing the system workload behavior includes monitoring the system data and analyzing the degree to which the workload is stationary. This makes the online optimization method less sensitive to system noise and capable of being adapted to handle different workloads. The effectiveness of the autonomic optimizer is thereby improved, making it easier to manage a wide range of systems.

Abstract: Disclosed is a data processing system implemented method, a data processing system and an article of manufacture for improving execution efficiency of a database workload to be executed against a database. The database includes database tables, and the database workload identifies at least one of the database tables. The data processing system includes an identification module for identifying candidate database tables being identifiable in the database workload, the identified candidate database tables being eligible for organization under a clustering schema, a selection module for selecting the identified candidate tables according to whether execution of the database workload is improved if the selected identified candidate table is organized according to the clustering scheme, and an organization module for organizing the clustering schema of the selected organized identified candidate tables prior to the database workload being execution against the database.

Abstract: There is disclosed a data processing system implemented method, a data processing system, and an article of manufacture for directing a data processing system to maintain a database table associated with an initial maintenance scheduling interval. The data processing system implemented method includes selecting a randomizing factor, and selecting a new maintenance scheduling interval for the database table based on the initial maintenance scheduling interval and the selected randomizing factor.

Abstract: Methods and systems are provided for tuning memory allocated among a plurality of applications in a data processing system. In one implementation, the method includes generating memory benefit data for the plurality of applications, comparing the generated memory benefit data associated with each of the plurality of applications, and dynamically reallocating memory from one or more of the plurality of applications to one or more other of the plurality of applications based on the comparison. A method and system is also provided for tuning memory allocated among a plurality of individual memory consumers for a given application.

Abstract: A storage requirements estimating system estimates the storage required for a proposed multidimensional clustering data by modeling wasted space. The amount of wasted space is modeled by calculating the cardinality of the unique value of the clustering key for the proposed configuration. Cardinality may be determined by estimation techniques. Specific values for wasted space and total space may be determined in response to the determined cardinality. Comparison of estimates for different proposed clustering configurations facilitate a selection among proposed multidimensional clustering data configurations.

Abstract: There are disclosed systems and computer program products for dynamically resizing memory pools used by database management systems. In one aspect, if a decrease in allocation to the memory pool is required, at least one page grouping that may be freed from the memory pool is identified as a candidate based on its position in a list of page groupings. If the page grouping contains any used memory blocks, the used memory blocks may be copied from a candidate page grouping to another page grouping in the list in order to free the candidate page grouping. Once the candidate page grouping is free of used memory blocks, the candidate page grouping may be freed from the memory pool. As an example, this system or computer program product may be used for dynamically resizing locklists or lock memory.

Abstract: The present invention relates to collecting statistics automatically for data in a database. There is provided a method for automated statistics collection comprising determining a likelihood that statistics for data have changed; and collecting statistics for data in response to the likelihood. Indicators of the likelihood that statistics have changed may be useful to trigger automated statistics collection. Tables having statistics that change significantly may be collected more often than statistics of tables that are stable. A preferred model is provided to facilitate the collection of statistics that are more relevant: a table is scheduled for collection in accordance with observed patterns of table activity; a table is considered for collection if it meets a threshold level of activity; and a table is sampled to predict whether the statistics to be collected have changed. When collecting statistics, throttling and lock contention can minimize impact on a database user's response experience.

Abstract: There are disclosed systems, computer program products, and methods for self-tuning memory. In an embodiment, a method for self-tuning memory comprises setting a tuning interval and a target range for free memory for a memory pool. If memory consumption falls outside of the set target range for free memory during a tuning interval, a new target size for the memory pool is set based on the target range for free memory. Memory allocation for the memory pool is increased or decreased for the next tuning interval, such that the new target size for the memory pool is reached. A decrement rate may be used to provide a controlled decrease of the memory pool over a plurality of tuning intervals if necessary, until the new target size for the memory pool is reached.

Abstract: A method, apparatus, and computer instructions for selecting and recommending of clustering indexes are provided. Baseline run time cost estimates are calculated based on database designs. Workload benefit of a candidate clustering design is calculated and compared to the baseline costs. If the workload benefit outweighs the baseline costs, clustering dimension solutions originated from the record identifier (RID) based index are identified. A clustering design is selected based on the identified clustering dimension solutions and the total number of recommended clustering dimensions for a given table. Based on the number of dimensions observed, either a multidimensional clustering solution or a RID based clustering index is recommended.

Abstract: Disclosed is a data processing system implemented method, a data processing system and an article of manufacture for improving execution efficiency of a database workload to be executed against a database. The database includes database tables, and the database workload identifies at least one of the database tables. The data processing system includes an identification module for identifying candidate database tables being identifiable in the database workload, the identified candidate database tables being eligible for organization under a clustering schema, a selection module for selecting the identified candidate tables according to whether execution of the database workload is improved if the selected identified candidate table is organized according to the clustering scheme, and an organization module for organizing the clustering schema of the selected organized identified candidate tables prior to the database workload being execution against the database.

Abstract: Providing cost model data for tuning query cache memory size in database systems. In one aspect, a query is received, and a query cache is checked for a stored query matching the received query. In response to a cache miss occurring, a token representing the received query is generated and checked for matching a token stored in a simulation area, each stored token representing a query object previously evicted from the query cache. A processing time saved by an avoidance of the cache miss is determined, and a cache size needed to avoid the cache miss is determined only when the generated token matches a stored token, by summing sizes of all query objects represented by the stored tokens. Each stored token is included in a stub that also includes the size of the represented query object and an identification number indicating a stored order.

Abstract: There are disclosed systems and computer program products for dynamically resizing memory pools used by database management systems. In one aspect, if a decrease in allocation to the memory pool is required, at least one page grouping that may be freed from the memory pool is identified as a candidate based on its position in a list of page groupings. If the page grouping contains any used memory blocks, the used memory blocks may be copied from a candidate page grouping to another page grouping in the list in order to free the candidate page grouping. Once the candidate page grouping is free of used memory blocks, the candidate page grouping may be freed from the memory pool. As an example, this system or computer program product may be used for dynamically resizing locklists or lock memory.

Abstract: Disclosed is a data processing system implemented method, a data processing system and an article of manufacture for improving execution efficiency of a database workload to be executed against a database. The database includes database tables, and the database workload identifies at least one of the database tables. The data processing system includes an identification module for identifying candidate database tables being identifiable in the database workload, the identified candidate database tables being eligible for organization under a clustering schema, a selection module for selecting the identified candidate tables according to whether execution of the database workload is improved if the selected identified candidate table is organized according to the clustering scheme, and an organization module for organizing the clustering schema of the selected organized identified candidate tables prior to the database workload being execution against the database.

Abstract: A storage requirements estimating system estimates the storage required for a proposed multidimensional clustering data by modeling wasted space. The amount of wasted space is modeled by calculating the cardinality of the unique value of the clustering key for the proposed configuration. Cardinality may be determined by estimation techniques. Specific values for wasted space and total space may be determined in response to the determined cardinality. Comparison of estimates for different proposed clustering configurations facilitate a selection among proposed multidimensional clustering data configurations.

Abstract: There are disclosed methods for dynamically resizing memory pools used by database management systems. In one aspect, if a decrease in allocation to the memory pool is required, at least one page grouping that may be freed from the memory pool is identified as a candidate based on its position in a list of page groupings. If the page grouping contains any used memory blocks, the used memory blocks may be copied from a candidate page grouping to another page grouping in the list in order to free the candidate page grouping. Once the candidate page grouping is free of used memory blocks, the candidate page grouping may be freed from the memory pool. As an example, this method may be used for dynamically resizing locklists or lock memory.

Abstract: Disclosed is a method for directing a data processing system, the data processing system and an article of manufacture. The data processing system has memory for storing a database. The method directs the data processing system to configuring the database based on a database workload having a performance metric. The method includes selecting a plurality of database characteristics based upon the database workload, each of the database characteristics having an associated estimated performance metric for the database workload, selecting a set of database characteristics from the plurality of database characteristics for which the associated estimated performance metric is lower than the database workload performance metric, and configuring the database using the selected set of database characteristics.

Abstract: In one embodiment, functional system elements are added to an autonomic manager to enable automatic online sample interval selection. In another embodiment, a method for determining the sample interval by continually characterizing the system workload behavior includes monitoring the system data and analyzing the degree to which the workload is stationary. This makes the online optimization method less sensitive to system noise and capable of being adapted to handle different workloads. The effectiveness of the autonomic optimizer is thereby improved, making it easier to manage a wide range of systems.