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: An exemplary method for reducing complexity of at least one user task includes steps of calculating a complexity metric for the at least one user task; identifying one or more usability issues having a measurable impact on the complexity metric for the at least one user task; determining one or more recommendations for addressing at least one of the one or more usability issues; and displaying a representation of at least one of the one or more usability issues and of at least one of the one or more recommendations. In an illustrative embodiment, implementing any one of the one or more recommendations reduces the impact of the usability issue on the complexity metric of the at least one user task and thereby reduces a complexity of the at least one user task.

Abstract: Nonlinear optimization is applied to resource allocation, as for example, buffer pool optimization in computer database software where only the marginal utility is known. The method for allocating resources comprises the steps of starting from an initial allocation, calculating the marginal utility of the allocation, calculating the constraint functions of the allocation, and applying this information to obtain a next allocation and repeating these steps until a stopping criteria is satisfied, in which case a locally optimal allocation is returned.

Abstract: Techniques for constructing and using models in accordance with service level management are disclosed. In one aspect of the invention, a technique for constructing a model (e.g., a system model) representative of a resource for use in managing a service associated with the resource, comprises the following steps/operations. A set of resource metrics is obtained in accordance with a resource abstract model (RAM) associated with the resource. Then, a model representative of the resource is constructed based on at least a portion of the set of resource metrics obtained from the resource abstract model.

Abstract: A software development service offering of constructing and embedding feedback control based algorithms into computer systems management software applications is delivered by a provider entity to a client organization. The provider is contracted by the client to prepare a set of control modeling assets based on identified computing system management problems and goals of the diem and to create a set of control solutions. Bi-directional links between the problems, assets and solutions are used by the provider to develop feedback control based algorithms that solve the identified client computing management problems and meet the client's computing system goals.

Abstract: A method and system for controlling load admission rate of an application server is provided. In the method, actual heap utilization and load admission rate of the application server in current control cycle are detected; based on the detected actual heap utilization and load admission rate, load characteristics variation parameters of the application server are estimated; and control gain of a feedback controller is calculated based on the load characteristics variation parameters; the feedback controller calculates desired load admission rate of the application server in next control cycle by using the calculated control gain. The load admission rate of the application server can be adaptively controlled by tracking changes in workload characteristics of the application server, such that the actual heap utilization of the application server is maintained at or close to target heap utilization.

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 method and system for controlling load admission rate of an application server is provided. In the method, actual heap utilization and load admission rate of the application server in current control cycle are detected; based on the detected actual heap utilization and load admission rate, load characteristics variation parameters of the application server are estimated; and control gain of a feedback controller is calculated based on the load characteristics variation parameters; the feedback controller calculates desired load admission rate of the application server in next control cycle by using the calculated control gain. The load admission rate of the application server can be adaptively controlled by tracking changes in workload characteristics of the application server, such that the actual heap utilization of the application server is maintained at or close to target heap utilization.

Abstract: A method and system performs dynamic online multi-parameter optimization for autonomic computing systems. A simplex is maintained. The system's performance is measured for the particular setting of configuration parameters associated with each point in the simplex. A new sample point is determined using the geometric transformations of the simplex. A current best point in the simplex can be resampled to determine if the environment has changed. If a sufficiently different utility value is obtained from a previously sampled utility value for the point in the simplex, the simplex is expanded. If the difference is not sufficient enough, then contraction of the simplex is performed.

Abstract: The invention broadly and generally provides a method for calibrating the relationship between management-inherent complexity metrics deriving from the management structure and human perceived complexity of information technology management comprising: (a) obtaining a set of management-inherent complexity metrics; (b) obtaining a set of human-perceived complexity metrics; (c) constructing a control model identifying a set of dominant indicators selected from the aforesaid set of management-inherent complexity metrics; (d) establishing a value model mapping from the aforesaid set of dominant indicators to the aforesaid set of human-perceived complexity metrics.

Abstract: An exemplary method for reducing complexity of at least one user task includes steps of calculating a complexity metric for the at least one user task; identifying one or more usability issues having a measurable impact on the complexity metric for the at least one user task; determining one or more recommendations for addressing at least one of the one or more usability issues; and displaying a representation of at least one of the one or more usability issues and of at least one of the one or more recommendations. In an illustrative embodiment, implementing any one of the one or more recommendations reduces the impact of the usability issue on the complexity metric of the at least one user task and thereby reduces a complexity of the at least one user task.

Abstract: Improved management feedback-based controller techniques are disclosed for use in accordance with a computing system. For example, a method for managing a computing system includes the following steps. At least one measured performance metric is obtained from the computing system being managed. At least one control input value is generated based on parameters including a control error and a control deadzone such that uncertainty associated with the computing system is accounted for in accordance with the parameters. The control error is determined from a computation comprising the at least one measured performance metric and at least one performance metric goal. The control deadzone is defined such that the control input value is not generated when the control error is within the control deadzone. The control input value is sent to the computing system to effectuate achievement of the at least one performance metric goal associated with the computing system.

Abstract: Techniques for managing feedback control systems are provided. By way of example, a method of controlling performance of a managed system by a controller includes the following steps/operations. The controller issues a control value to the managed system to affect a performance of the managed system. The controller maintains a measurement time period having a variable start time within which the performance of the managed system is measured, such that the control value is given time to take effect on the managed system and a performance metric fed back to the controller from the managed system reflects the effect of the control value on the managed system.

Abstract: Techniques are disclosed for quantitatively estimating a value of a given process, such as an IT service management process. For example, a computer-implemented method for estimating a value based on a complexity analysis of a given process comprises a computer performing the following steps. A context for the given process is identified. A baseline for the given process is quantified. The process baseline comprises a key performance indicator and an underlying complexity measure. An improvement measure for the given process is determined. The improvement measure is achieved by at least one of a tooling deployment or a process transformation. A value derived for the given process is estimated based on the context identifying, baseline quantifying, and improvement determining steps.

Abstract: Techniques are provided for generically controlling one or more resources associated with at least one computing system. In one aspect of the invention, the technique comprises evaluating one or more performance metrics associated with the one or more resources given one or more configurations of the one or more resources. The technique then causes a change in the one or more configurations of the one or more resources based on the performance metric evaluating step. The one or more performance metrics and the one or more configurations are expressed in generic formats.

Abstract: A software development service offering of constructing and embedding feedback control based algorithms into computer systems management software applications is delivered by a provider entity to a client organization. The provider is contracted by the client to prepare a set of control modeling assets based on identified computing system management problems and goals of the diem and to create a set of control solutions. Bi-directional links between the problems, assets and solutions are used by the provider to develop feedback control based algorithms that solve the identified client computing management problems and meet the client's computing system goals.

Abstract: Nonlinear optimization is applied to resource allocation, as for example, buffer pool optimization in computer database software where only the marginal utility is known. The method for allocating resources comprises the steps of starting from an initial allocation, calculating the marginal utility of the allocation, calculating the constraint functions of the allocation, and applying this information to obtain a next allocation and repeating these steps until a stopping criteria is satisfied, in which case a locally optimal allocation is returned.

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: Nonlinear optimization is applied to resource allocation, as for example, buffer pool optimization in computer database software where only the marginal utility is known. The method for allocating resources comprises the steps of starting from an initial allocation, calculating the marginal utility of the allocation, calculating the constraint functions of the allocation, and applying this information to obtain a next allocation and repeating these steps until a stopping criteria is satisfied, in which case a locally optimal allocation is returned.

Abstract: An improved method and system for performing dynamic online multi-parameter optimization for autonomic computing systems are provided. With the method and system of the present invention, a simplex, i.e. a set of points in the parameter space that has been directly sampled, is maintained. The system's performance with regard to a particular utility value is measured for the particular setting of configuration parameters associated with each point in the simplex. A new sample point is determined using the geometric transformations of the simplex. The method and system provide mechanisms for limiting the size of the simplex that is generated through these geometric transformations so that the present invention may be implemented in noisy environments in which the same configuration settings may lead to different results with regard to the utility value. In addition, mechanisms are provided for resampling a current best point in the simplex to determine if the environment has changed.