Abstract: A system and methods for capacity planning of electronic systems storage are provided. Features include a non-invasive data discovery and collection mechanism, an adaptive grouping mechanism, a flexible analysis mechanism, predictive modeling and forecasting mechanisms, and a business metric and correlation mechanism. A discovery engine that ascertains the availability of collectable entities. A collection engine in turn gathers information from and about entities discovered by the discovery engine. A grouping module groups together entities according to parameters specified by the user. An analysis and forecasting module allows a user to create, modify and save forecast scenarios from which a model is generated by a modeling module. Correlations can be identified between specified key business metrics and historical data. Once a model has been created, a forecast is generated and can then be analyzed using Bold Freehand Extrapolation, time-series analysis, and business trend-based forecasting.

Abstract: A system and methods for capacity planning of electronic systems storage are provided. Features include a non-invasive data discovery and collection mechanism, an adaptive grouping mechanism, a flexible analysis mechanism, predictive modeling and forecasting mechanisms, and a business metric and correlation mechanism. A discovery engine that ascertains the availability of collectable entities. A collection engine in turn gathers information from and about entities discovered by the discovery engine. A grouping module groups together entities according to parameters specified by the user. An analysis and forecasting module allows a user to create, modify and save forecast scenarios from which a model is generated by a modeling module. Correlations can be identified between specified key business metrics and historical data. Once a model has been created, a forecast is generated and can then be analyzed using Bold Freehand Extrapolation, time-series analysis, and business trend-based forecasting.

Abstract: In a shared storage infrastructure accessible by more than one customer, the shared storage infrastructure is separated into a plurality of logical storage areas. A customer is associated with at least one logical storage area within the shared storage infrastructure. The customer and its logical storage areas are associated with at least one port in the shared storage infrastructure. Each customer has access only to the logical storage areas associated with the customer and cannot access the logical storage areas of any other customer. The shared storage infrastructure provides policy based storage management to each customer, that is, each customer appears to have access to a non-shared storage subsystem. By providing a virtualization layer, a customer may be given the ability to manage its own storage needs.

Abstract: A secure shared storage infrastructure accessible by more than one customer is separated into a plurality of logical storage areas. A customer is associated with at least one logical storage area within the shared storage infrastructure. The customer and its logical storage areas are associated with at least one port in the shared storage infrastructure. Each customer has access only to the logical storage areas associated with the customer and cannot access the logical storage areas of any other customer.

Abstract: A system and methods for capacity planning of electronic systems storage are provided. Features include a non-invasive data discovery and collection mechanism, an adaptive grouping mechanism, a flexible analysis mechanism, predictive modeling and forecasting mechanisms, and a business metric and correlation mechanism. A discovery engine that ascertains the availability of collectable entities. A collection engine in turn gathers information from and about entities discovered by the discovery engine. A grouping module groups together entities according to parameters specified by the user. An analysis and forecasting module allows a user to create, modify and save forecast scenarios from which a model is generated by a modeling module. Correlations can be identified between specified key business metrics and historical data. Once a model has been created, a forecast is generated and can then be analyzed using Bold Freehand Extrapolation, time-series analysis, and business trend-based forecasting.

Abstract: In a shared storage infrastructure accessible by more than one customer, the shared storage infrastructure is separated into a plurality of logical storage areas. A customer is associated with at least one logical storage area within the shared storage infrastructure. The customer and its logical storage areas are associated with at least one port in the shared storage infrastructure. Each customer has access only to the logical storage areas associated with the customer and cannot access the logical storage areas of any other customer. The shared storage infrastructure provides policy based storage management to each customer, that is, each customer appears to have access to a non-shared storage subsystem. By providing a virtualization layer, a customer may be given the ability to manage its own storage needs.

Abstract: A secure shared storage infrastructure accessible by more than one customer is separated into a plurality of logical storage areas. A customer is associated with at least one logical storage area within the shared storage infrastructure. The customer and its logical storage areas are associated with at least one port in the shared storage infrastructure. Each customer has access only to the logical storage areas associated with the customer and cannot access the logical storage areas of any other customer.