Storage Capacity Detection via Performance Data Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing storage systems face challenges in identifying underutilized capacity due to subtle changes in usage patterns, making it difficult to efficiently re-allocate storage resources.
Innovation Solution
A system comprising a data acquisition engine and a data analysis engine that collects and analyzes performance data from storage objects, using parameters set by users to identify underutilized storage capacity by calculating average performance over specified time periods and comparing it to threshold values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional storage monitoring methods are used, then storage capacity tracking is simple, but underutilized capacity cannot be effectively identified
Solution Approach 1:
The system segments storage monitoring into multiple specialized components: a data acquisition engine that collects performance metrics, a data analysis engine that processes the metrics, and a data warehouse that stores historical data. This segmentation allows each component to focus on specific tasks, improving detection accuracy while managing complexity through modular design.
Solution Approach 2:
The patent introduces intermediate processing layers between raw storage data and analysis results. The data acquisition engine acts as an intermediary that collects and standardizes performance metrics from multiple storage objects, while the data analysis engine serves as another intermediary that applies complex analysis algorithms. These intermediaries enable precise detection without exposing the full complexity of the analysis system to users.
2Productivity
If manual identification of underutilized storage is performed, then system complexity is low, but productivity and resource allocation efficiency are poor
Solution Approach 1:
The system implements self-service automation where the data acquisition engine automatically collects performance metrics from storage objects without manual intervention. The data analysis engine then automatically processes this data, identifies underutilized capacity, and generates reports. This automated self-service approach dramatically improves productivity and resource allocation efficiency compared to manual methods.
Solution Approach 2:
The system establishes a feedback loop where performance metrics are continuously collected from storage objects, analyzed to identify underutilized capacity, and the results are reported back to administrators. This feedback mechanism enables continuous optimization of storage resource allocation, improving productivity over time as the system learns from ongoing performance data.
3Reliability
If storage capacity is allocated to ensure availability, then reliability is improved, but underutilized capacity increases
Solution Approach 1:
The system applies dynamic analysis by collecting performance metrics over specified time periods and calculating average performance values. This dynamic approach allows the system to distinguish between temporarily low-utilization periods and genuinely underutilized storage capacity. By basing identification on averaged performance over time rather than instantaneous snapshots, the system maintains reliability while reducing false positives that would lead to resource waste.
Data Source
AI summary
Described herein is a system and method for detecting underutilized capacity within a storage system environment. The technique comprises collecting performance data of various storage objects within a storage system environment for various performance measures at designated time intervals. The collected performance data may be formatted and stored to a database. One or more parameters may be received specifying at least one performance measure, at least one threshold value, and/or at least one time period. The performance data for target storage objects may be analyzed according to the received parameters to determine any underutilized storage objects. A report may be generated according to the parameters listing the storage objects and address locations of any underutilized storage objects. The report may comprise various information corresponding to the underutilized storage object, such as the business units, tiers, data centers, and levels of service they are associated with.