Storage capacity planning
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.
1. A computer-implemented method for forecasting electronic data storage requirements, the method comprising:
determining whether to use existing control data or new control data;
if it is determined to use new control data, obtaining criteria for the new control data, at least in part by receiving a user selection of at least one of a range of internet protocol addresses and a set of host names;
if it is determined to use existing control data, providing a user options to select the existing control data to be used and to edit the existing control data in order to add or change criteria specified by the existing control data;
identifying from among a first plurality of computing device entities located on a network a second plurality of computing device entities that satisfies the user selection;
collecting with an agentless protocol interface storage-related information from each of the second plurality of computing device entities, wherein said collecting comprises querying each of the second plurality of computing device entities using a network protocol, and wherein the storage-related information comprises disk usage, disk utilization, performance and failure rate;
upon user initiation of a forecasting operation:
when determining to use a new forecast scenario, invoking a process for creating a new forecast scenario, wherein the process comprises (1) receiving a user selection of a scope of historical data, the scope defining a set of elements, entities, or groups against which a model and resulting forecast are created, (2) obtaining modeling parameters from a user, and (3) generating a new scenario, wherein obtaining the modeling parameters comprises:
receiving a user selection of a data aggregation, wherein receiving the user selection includes presenting to the user a choice of at least the following data aggregations: (1) peak collected usage and utilization data for the current day, (2) average collected usage and utilization data for the current day, (3) last usage and utilization data for the current day, and (4) standard deviation from average, for usage and utilization data, for the current day;
receiving a user selection of a view to apply to the chosen groups, entities, or elements, wherein receiving the user selection includes presenting to the user a choice of at least generating a model for each individual member of the group or generating a model based on a rollup of the group data;
receiving a user selection of a historical time period including a starting date and an ending date;
receiving a user selection of a data series type to analyze, wherein receiving the user selection includes presenting to the user a choice of at least the following data series types: (1) usage, (2) utilization as a percentage of available storage, (3) costs, (4) power consumption, (5) performance, and (6) failure rates;
receiving a user selection of a confidence level and risk factors; and receiving a user selection of a model methodology, wherein receiving the user selection includes presenting to the user a choice of at least one methodology using a single algorithm, at least one methodology using a tournament, and at least one methodology using multiple algorithms;
when determining to use an existing forecast scenario, outputting to a display a list of available predetermined scenarios for modeling the collected storage-related information and receiving a user-selected scenario from the list of available predetermined scenarios;
forecasting, based on the new scenario or the user-selected scenario, and the collected storage-related information, storage requirements of the second plurality of computing device entities for a future time period; and
outputting the forecasted storage requirements to a graphical user interface.
2. The method of claim 1 , wherein the network protocol comprises at least one of: a secure shell (SSH) protocol, a simple network management protocol (SNMP) and windows management instrumentation (WMI).
3. The method of claim 1 , further comprising receiving a user selection of a modeling parameter, and wherein said forecasting is further based at least in part on the user-selected modeling parameter.
4. The method of claim 1 , wherein said collecting is performed without installation or persistent residence of a process dedicated to a collection task on any of the second plurality of computing device entities.
5. The method of claim 1 , wherein the user-selected scenario comprises at least one of: a model, statistical relevance data, business trends and correlation data.
6. The method of claim 1 , further comprising following said collecting, receiving a user-defined historical collection of the storage-related information for use in said forecasting.
7. The method of claim 1 , wherein collecting the storage-related information from each of the plurality of identified second plurality of computing device entities further comprises:
receiving criteria for the storage-related information to be collected;
collecting the storage-related information from the identified second plurality of computing device entities and a set of entities consuming storage capacity provided by the second plurality of computing device entities;
correlating the storage-related information from the set of entities with the storage-related information from the second plurality of computing device entities to determine a mapping; and
wherein forecasting storage requirements for the future time period further comprises forecasting an amount of storage capacity of the second plurality of computing device entities that will be consumed during the future time period by the set of entities.
8. The method of claim 7 , further comprising displaying to the user an adjustable graph segment that represents the collected storage-related information over a past period of time.
9. The method of claim 8 , further comprising receiving a user-defined length of the adjustable graph segment that defines the user-defined historical collection of the storage-related information.
10. The method of claim 9 , wherein the user-selected modeling parameter comprises at least one of, for each of the second plurality of computing device entities: (i) peak collected usage data, (ii) average collected usage data, and (iii) last usage data.
11. A computer program product for forecasting electronic data storage requirements, the computer program product stored on a non-transitory computer readable storage medium and configured to cause a computer processor to execute the steps of:
determining whether to use existing control data or new control data;
if it is determined to use new control data, obtaining criteria for the new control data, at least in part by receiving a user selection of at least one of a range of internet protocol addresses and a set of host names;
if it is determined to use existing control data, providing a user options to select the existing control data to be used and to edit the existing control data in order to add or change criteria specified by the existing control data;
identifying from among a first plurality of computing device entities located on a network a second plurality of computing device entities that satisfies the user selection;
collecting with a non-invasive protocol interface storage-related information from each of the second plurality of computing device entities, wherein said collecting comprising querying each of the second plurality of computing device entities from a remote location, and wherein the storage-related information comprises information regarding disk usage, disk utilization, performance and failure rate;
upon user initiation of a forecasting operation:
when determining to use a new forecast scenario, invoking a process for creating a new forecast scenario, wherein the process comprises (1) receiving a user selection of a scope of historical data, the scope defining a set of elements, entities, or groups against which a model and resulting forecast are created, (2) obtaining modeling parameters from a user, and (3) generating a new scenario, wherein obtaining the modeling parameters comprises:
receiving a user selection of a data aggregation, wherein receiving the user selection includes presenting to the user a choice of at least the following data aggregations: (1) peak collected usage and utilization data for the current day, (2) average collected usage and utilization data for the current day, (3) last usage and utilization data for the current day, and (4) standard deviation from average, for usage and utilization data, for the current day;
receiving a user selection of a view to apply to the chosen groups, entities, or elements, wherein receiving the user selection includes presenting to the user a choice of at least generating a model for each individual member of the group or generating a model based on a rollup of the group data;
receiving a user selection of a historical time period including a starting date and an ending date;
receiving a user selection of a data series type to analyze, wherein receiving the user selection includes presenting to the user a choice of at least the following data series types: (1) usage, (2) utilization as a percentage of available storage, (3) costs, (4) power consumption, (5) performance, and (6) failure rates;
receiving a user selection of a confidence level and risk factors; and
receiving a user selection of a model methodology, wherein receiving the user selection includes presenting to the user a choice of at least one methodology using a single algorithm, at least one methodology using a tournament, and at least one methodology using multiple algorithms;
when determining to use an existing forecast scenario, outputting a list of available scenarios for analyzing the collected storage-related information and receiving a user-selected scenario from the list of available scenarios;
forecasting, based on the new scenario or the user-selected scenario, and the collected storage-related information, storage requirements of the second plurality of computing device entities for a future time period; and
outputting the forecasted storage requirements to a display.
12. The computer program product of claim 11 , wherein collecting the storage-related information from each of the plurality of identified second plurality of computing device entities further comprises:
receiving criteria for the storage-related information to be collected;
collecting the storage-related information from the identified second plurality of computing device entities and a set of entities consuming storage capacity provided by the second plurality of computing device entities;
correlating the storage-related information from the set of entities with the storage-related information from the second plurality of computing device entities to determine a mapping; and
wherein forecasting storage requirements for the future time period further comprises forecasting an amount of storage capacity of the second plurality of computing device entities that will be consumed during the future time period by the set of entities.
13. The computer program product of claim 11 , wherein said collecting is performed without installation or persistent residence of a process dedicated to a collection task on any of the second plurality of computing device entities.
14. The computer program product of claim 11 , wherein the second plurality of computing device entities comprises different types of storage entities.