Abstract:
A product demand forecasting system includes a profile extractor that generates a demand profile of a new product yet to be introduced based on demand profiles of similar products already introduced. The profile extractor normalizes and averages the demand profiles of the similar products to obtain the demand profile of the new product. The forecasting system also includes a life-cycle demand predictor that generates a total life-cycle demand of the new product based on historical demand data of the similar products. A forecast creator is then coupled to the profile extractor and the demand predictor to generate a life-cycle demand forecast for the new product based on the demand profile and total life-cycle demand of the new product. A method of providing a life-cycle product demand forecast for a new product yet to be introduced is also described.
Abstract:
A method for server consolidation is provided. The method includes accessing performance data of a plurality of source servers, receiving multiple consolidation parameters for a desired target server, receiving selected configurations for a new target server, computing a minimum number of target servers required to consolidate the plurality of source servers based at least on the performance data, the selected configurations for the desired target server, and the multiple consolidation parameters, and deriving a bin-packing solution to the server consolidation based at least on the performance data, the selected configurations for the new target server, and the minimum number for the one or more performance metrics.
Abstract:
User input regarding a target system on which a software application is to be deployed is received. A benchmark system from plural candidate benchmark systems is matched to the target system. An estimated performance of the software application on the target system or an estimated utilization of resources of the target system by the software application is computed based on information relating to the matched benchmark system.
Abstract:
A tool receives parameters relating to target enterprise objective of an enterprise, the cost of an enterprise resource associated with the enterprise, and an enterprise resource capacity. The tool calculates an amount of an enterprise resource to be assigned in an enterprise based on the received parameters relating to the target enterprise objective, the enterprise resource cost, and the enterprise resource capacity.
Abstract:
A method for server consolidation is provided. The method includes collecting performance data of a plurality of source servers in a desired environment, selecting a group of one or more source servers from the plurality of source servers for consolidation, marking each source server in the with one of multiple usability statuses with one of such statuses indicates the marked source server is to be replaced or reused as necessary in the server consolidation, selecting a target platform for a new server, and performing a first server consolidation analysis of the first group based at least on the collected performance data, the initial usability status of each source server in the first group, and the first selected target platform.
Abstract:
In at least some embodiments, a method comprises obtaining a state description associated with a system having a component. The method further comprises automatically obtaining a substantially optimal parameterization for the component based on one or more operant characteristics of the component predicted by a behavior prediction model using combinations of the system's state description and a set of possible parameterizations for the component.
Abstract:
A system and method employing an allocation process for determining the number of server machines at each tier of a multiple tiered server system. The allocation process evaluates the number of server machines at each tier sufficient to achieve an average response time of a transaction request to be processed by the server system in response to changes in the average number of transaction requests. The allocation process also identifies shadow pricing enabling analysis of the cost associated with incremental changes in the average response time or other critical system resources.
Abstract:
A method for action selection based upon an objective of an outcome relative to a subject. In one embodiment, a training set is obtained that contains attributes of a subject. In the present embodiment, a best behavioral model for predicting an outcome when a subject has an action applied is calculated. The training set is mapped to the best behavioral model. The mapping provides a base from which a random sub-sample is acquired. In the present embodiment, a random sub-sample of the training set and the best behavioral model is then selected. This random sub-sample reduces the computational requirements when determining an optimized strategy. The optimized strategy provides an optimal action relative to the subject for the objective of the outcome. In one embodiment, the subject is a customer of a business entity, enabled to interact with the customer, and an action is a promotion offered by the business entity.
Abstract:
A tool receives parameters relating to target enterprise objective of an enterprise, the cost of an enterprise resource associated with the enterprise, and an enterprise resource capacity. The tool calculates an amount of an enterprise resource to be assigned in an enterprise based on the received parameters relating to the target enterprise objective, the enterprise resource cost, and the enterprise resource capacity.
Abstract:
A computer-executable method provides plural constraints representing corresponding objectives of an enterprise, where the objectives are related to members of the enterprise's portfolio. A model is provided for selecting the members of the portfolio, where the model contains the plural constraints. The model is solved to select the members of the portfolio.