Dynamic storage device system configuration adjustment
Embodiments of the present disclosure relate to dynamically adjusting storage device system configurations. Information from one or more storage devices is collected. Dynamic adjustment of each storage device's system configuration is enabled based on the information. The information can include performance data, telemetry data, system configuration data, component configuration data, change events/commands, and/or alerts. Each storage device's components can be monitored with at least one daemon. Information patterns can be identified. The patterns can be matched to spikes in data traffic and/or storage device performance. The matched patterns and spikes can be correlated with each storage device's performance and errors. Storage device system configuration adjustment options can be determined. Rules for each option can be generated. Dynamic adjustment of each storage device's system configuration based on the generated one or more rules can be enabled.
1. An apparatus comprising a memory and at least one processor configured to:
collect information from at least one component of one or more storage devices;
determine performance changes of the at least one component using the collected information;
correlate performance changes of components with uncollected information by using the collected information from one or more components neighboring the components with uncollected information; identify patterns within the collected information; and match the patterns to spikes in one or more of data traffic and storage device performance; and enable dynamic adjustment of each storage device's system configuration based on the collected information and the correlated performance changes.
2. The apparatus of claim 1 , wherein the information includes one or more of: performance data, telemetry data, system configuration data, component configuration data, change events/commands, and alerts.
3. The apparatus of claim 2 , further configured to configure each of the storage devices with at least one daemon to monitor one or more components of each storage device.
4. The apparatus of claim 3 , wherein the one or more components include one or more of: memory, cache, processors, amongst other storage device components.
5. The apparatus of claim 1 further configured to:
correlate the matched patterns and the spikes with one or more of performance and errors of each of the storage devices;
determine options to adjust system configurations of each of the storage devices;
generate one or more rules for each of the determined options; and
enable dynamic adjustment of each storage device's system configuration based on the generated one or more rules.
6. The apparatus of claim 1 , wherein identifying patterns within the information includes normalizing the information.
7. The apparatus of claim 6 , wherein normalizing the information includes:
identifying each data type included in the information; and
transforming the information corresponding to each data type into a predetermined format.
8. The apparatus of claim 6 further configured to perform one or more machine learning techniques to the normalized information.
9. The apparatus of claim 1 , wherein identifying the patterns within the information includes segmenting the information based on one or more of: time windows, industry of customers associated with each storage device, configuration changes to any storage device component, event notifications, data transfer spikes, and alert notifications, amongst other segmentation parameters.
10. A method comprising:
collecting information from at least one component of one or more storage devices;
determining performance changes of the at least one component using the collected information;
correlating performance changes of components with uncollected information by using the collected information from one or more components neighboring the components with uncollected information; identifying patterns within the collected information; and matching the patterns to spikes in one or more of data traffic and storage device performance; and enabling dynamic adjustment of each storage device's system configuration based on the collected information and the correlated performance changes.
11. The method of claim 10 , wherein the information includes one or more of: performance data, telemetry data, system configuration data, component configuration data, change events/commands, and alerts.
12. The method of claim 11 , further comprising configuring each of the storage devices with at least one daemon to monitor one or more components of each storage device.
13. The method of claim 12 , wherein the one or more components include one or more of: memory, cache, processors, amongst other storage device components.
14. The method of claim 10 further comprising:
correlating the matched patterns and the spikes with one or more of performance and errors of each of the storage devices;
determining options to adjust system configurations of each of the storage devices;
generating one or more rules for each of the determined options; and
enabling dynamic adjustment of each storage device's system configuration based on the generated one or more rules.
15. The method of claim 10 , wherein identifying patterns within the information includes normalizing the information.
16. The method of claim 15 , wherein normalizing the information includes:
identifying each data type included in the formation; and
transforming the information corresponding to each data type into a predetermined format.
17. The method of claim 15 further comprising performing one or more machine learning techniques to the normalized information.
18. The method of claim 10 , wherein identifying the patterns within the information includes segmenting the information based on one or more of: time windows, industry of customers associated with each storage device, configuration changes to any storage device component, event notifications, data transfer spikes, and alert notifications, amongst other segmentation parameters.