Abstract: A technique is disclosed for managing a workload distributed across multiple data processing systems to enhance shared resource access to meet a common performance standard. The technique includes on at least one system, measuring performance of the work units on the system to create local performance data, and on at least some of the systems sending the local performance data to at least one other system of the multiple data processing systems. The method further includes on at least one of the systems, receiving the performance data from the sending systems to create remote performance data, and adjusting at least one control parameter for accessing shared resources in response to the local and remote performance data to modify the performance of the work units distributed across the data processing systems to achieve the common performance standard. A dynamic resource clustering process is also employed to enhance the shared resource management.

Abstract: Disclosed is a method and computer program device for dynamically managing the assignment of alias addresses to base addresses referencing an input/output (I/O) device, such as a direct access storage device (DASD). Two distinct methods are disclosed. In one method, alias addresses are assigned based on the performance of the I/O devices. In this method, alias addresses are assigned to highly utilized devices, as indicated by device performance data, in order to maximize the efficient utilization of I/O device resources. In a second method, workload management principles are utilized to assign alias addresses. In this method, a correlation is made between each I/O device and the service classes utilizing each device. As in the first method, performance data is generated for each I/O device. Alias addresses are assigned to I/O devices experiencing queue delays as indicated by their performance data, if the device is associated with a service class that has failed to meet one or more processing goals.

Abstract: Central processing unit (CPU) resources are managed within a computing environment. When the allocation of CPU resources to a partition of the computing environment is to be adjusted, the allocation is adjusted dynamically. This dynamic adjustment is across at least two partitions of the computing environment. The adjusting includes modifying processor weights associated with the partitions.

Abstract: A technique is disclosed for managing a workload distributed across multiple data processing systems to enhance shared resource access to meet a common performance standard. The technique includes on at least one system, measuring performance of the work units on the system to create local performance data, and on at least some of the systems sending the local performance data to at least one other system of the multiple data processing systems. The method further includes on at least one of the systems, receiving the performance data from the sending systems to create remote performance data, and adjusting at least one control parameter for accessing shared resources in response to the local and remote performance data to modify the performance of the work units distributed across the data processing systems to achieve the common performance standard. A dynamic resource clustering process is also employed to enhance the shared resource management.

Abstract: A technique is disclosed for managing a workload distributed across multiple data processing systems to enhance shared resource access to meet a common performance standard. The technique includes on at least one system, measuring performance of the work units on the system to create local performance data, and on at least some of the systems sending the local performance data to at least one other system of the multiple data processing systems. The method further includes on at least one of the systems, receiving the performance data from the sending systems to create remote performance data, and adjusting at least one control parameter for accessing shared resources in response to the local and remote performance data to modify the performance of the work units distributed across the data processing systems to achieve the common performance standard. A dynamic resource clustering process is also employed to enhance the shared resource management.

Abstract: A method and apparatus for controlling the number of servers in a multisystem cluster. Incoming work requests are organized into service classes, each of which has a queue serviced by servers across the cluster. Each service class has defined for it a local performance index for each particular system of the cluster and a multisystem performance index for the cluster as a whole. Each system selects one service class as a donor class for donating system resources and another service class as a receiver class for receiving system resources, based upon how well the service classes are meeting their goals. Each system then determines the resource bottleneck causing the receiver class to miss its goals.

Abstract: A method and apparatus for controlling the number of servers in an information handling system in which incoming work requests belonging to a first service class are placed in a queue for processing by one or more servers. The system also has units of work assigned to a second service class that acts as a donor of system resources. In accordance with the invention, a performance measure is defined for the first service class as well as for the second service class. Before adding servers to the first service class, there is determined not only the positive effect on the performance measure for the first service class, but also the negative effect on the performance measure for the second service class. Servers are added to the first service class only if the positive effect on the performance measure for the first service class outweighs the negative effect on the performance measure for the second service class.

Abstract: A workload manager creates an in storage representation of a set of performance goals, each goal associated with a class of clients (e.g., client transactions) in a client/server data processing system. A set of servers, providing service to the clients, are managed to bring the clients into conformity with the class performance goals by: calculating performance indexes for each class to determine the target class(es) which are farthest behind their class performance goals; analyzing the relationship among servers and client classes to determine which servers serve which classes; determining which resource(s) are impacting the service provided to the key servers (that is, those on which the target class(es) are most heavily reliant), and projecting the effect of making more of these resources available to those servers; and, finally, making the changes to those resources which are projected to most favorably indirectly affect the performance of the target class(es).

Abstract: A workload manager creates goal control data, defining two or more classes of system work units, in response to specification of goals of two or more goal types for the classes, and specification of importance values for each of the goal types. A system resource manager causes the goals to be met by periodically sampling work unit status; calculating a performance index for each class; selecting a receiver class to receive improved service based on the relative performance indexes and goal importance; a system bottleneck impacting achievement of goal by the receiver class is identified; and one or more system control data elements are identified and adjusted to cause the goal to be met for the receiver class.

Abstract: Paging data is used to manage address space working set size, system multiprogramming level, and job mix, to improve system productive CPU utilization. System paging characteristics are monitored to determine when to perform management functions. When needed, address space paging data is collected for monitored address spaces, and target working set sizes set for managed address spaces. If necessary, address spaces are "forced" to exhibit characteristic paging patterns. Reallocation of working set pages among active address spaces, and swap in-swap out actions, are assessed, and impacts on productive CPU utilization is estimated.

Abstract: A linear data set is mapped to one or more non-main storage virtual data spaces. Portions of this data space are then selectively mapped to a "window" in an address space in which an application is executing, and changes made in this "window" are temporarily saved in the data space. After completion of processing, the application may permanently save changed data from the data space to the linear data set.The technique for mapping the data space to the address space may be used to map between two address spaces, and may be extended to encompass third and subsequent spaces, so that a reference to a mapped address in the first space will ultimately be interpreted as a reference to an address in the last mapped space.