In the area of storage management, service automation can be realized through the use of “MAPE” loop(s). A Planner (P) interacts with the Monitoring (M), Analysis (A) and Execution (E) components in a closed loop. For each new option or potential planning action the Planner (P) invokes the Analysis (A) component. The correctness, as well as effectiveness, of the planning decision is dependent on the Analysis (A) component. Embodiments can utilize an adaptive Analysis (A) component (i.e., an analysis component that can be retrained) that also associates a value of confidence and a corresponding error in the evaluation along with a predicted impact. The Planner (P) component uses this additional information for quoting the final impact of a particular planning action as part of an adaptive MAPE loop to provide improved resource utilization and resource management.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method comprising: receiving, via a network, a request to generate an evaluation of an impact of a planning action on a storage system; generating an evaluation of the impact of the planning action on the storage system, wherein the evaluation is based on multiple models of the storage system each returning an indication of reliability of an ability of one of the multiple models to predict the impact of implementing the planning action on the storage system; and determining, using a processor, whether to execute the planning action based on the evaluation of the impact of the planning action on the storage system. 2. The method of claim 1, further comprising monitoring resource utilization of the storage system over a period of time before receiving the request to generate the evaluation. 3. The method of claim 2, wherein the at least one of the multiple models uses information about the resource utilizations monitored over the period of time to predict the impact of implementing the planning action. 4. The method of claim 1, wherein determining whether to execute the planning action includes determining if the indication of reliability for one of the multiple models meets a threshold requirement. 5. The method of claim 1, wherein the evaluation is based on the topography of the storage system. 6. The method of claim 1, wherein the planning action includes new workload provisioning or existing workload migration. 7. The method of claim 1, further comprising: receiving, via the network, external reports regarding performance of the storage system after the planning action has been implemented; and retraining one or more of the multiple models of the storage system when one or more of the multiple models fail to predict the impact of the planning action on the storage system. 8. The method of claim 1, wherein the indication of reliability includes model confidence indicator or a model error indicator represented as a percentage, a fraction, or a fuzzy operator. 9. A resource manager comprising: one or more processors, a monitoring engine, running on the one or more processors, to monitor performance characteristics of a storage system; an analysis engine, running on the processor, to receive a request to evaluate multiple planning actions and generate predicted responses of the storage system to the multiple planning actions and associated indications of reliability, wherein the predicted responses and the associated indications of reliability are generated using multiple models of the storage system and the performance characteristics determined by the monitoring engine; and a planning engine, running on the one or more processors, to receive the predicted responses of the storage system to the multiple planning actions and the associated indications of reliability and to determine whether to implement at least one of the multiple planning actions based on the predicted responses of the storage system and the associated indications of reliability of the predicted responses. 10. The resource manager of claim 9, wherein the performance characteristics include latency, utilization, or data throughput. 11. The resource manager of claim 9, wherein at least one of the multiple planning actions include data migration, adding an application, or provisioning new storage. 12. The resource manager of claim 9, wherein the multiple models include at least one of a black box model, a queuing theory model, or a white box model. 13. The resource manager of claim 9, further comprising an execution engine to execute at least one of the multiple planning actions on the storage server. 14. The resource manager of claim 9, further comprising an evaluation module to generate an adjusted service level objective evaluation. 15. The resource manager of claim 9, the analysis engine and the planning engine are aware of at least part of the storage system topography. 16. A method comprising: requesting, from an analysis engine, data representing a first prediction of a first impact on a storage system of a first planning action and a second prediction of a second impact on the storage system of a second planning action on the storage system; receiving the data representing the first prediction of the first impact of the first planning action and the second prediction of the second impact of the second planning action, wherein the data includes a first model error of the first planning action and a second model error of the second planning action; generating, using a processor, a first adjusted evaluation value based on the first model error and a second adjusted evaluation value based on the second model error; and determining, based on the first adjusted evaluation value and the second adjusted evaluation value, whether to implement the first planning action or the second planning action. 17. The method of claim 16, further comprising: monitoring behavior of the storage system under various loads; and updating a model of the storage system based on the behavior monitored. 18. The method of claim 16, further comprising: executing either the first planning action or the second planning action on the storage system; receiving, from a monitoring engine, a system performance characteristic of the storage system with the planning action executed; determining that the prediction of the impact of the planning action that was executed on the storage system does not adequately predict the system performance characteristic received from the monitoring engine; and requesting the model of the storage system be retrained. 19. The method of claim 16, further comprising ignoring the first prediction of the first impact of the first planning action when the first model error meets a first threshold requirement or ignoring the second prediction of the second impact of the second planning action when the second model error is meets a second threshold requirement. 20. The method of claim 16, further comprising: collecting live data from a storage system; and refreshing at least one of the first model or the second model based on the live data.