Automated Data Assurance for Server Consolidation
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Solution Overview
Problem
Server consolidation is hindered by the inefficiency of manually processing large amounts of performance data to identify imperfections and ensure data quality, leading to inconsistent and time-consuming analysis, which can result in overlooked issues due to the tedious nature of manual checks.
Innovation Solution
A server consolidation tool with a data assurance module that automatically detects and corrects imperfections in performance data, providing a graphical user interface for users to navigate and analyze data, allowing for flexible data collection and categorization, and enabling the generation of clean performance data for improved consolidation analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual processing of performance data is used, then data quality can be assessed, but the process is time-consuming and tedious leading to overlooked issues
Solution Approach 1:
The system performs self-assessment of data quality through automated collection, validation, and cleaning processes. The data collection module automatically gathers performance data from multiple sources, the data validation module checks for completeness and consistency, and the data cleaning module removes duplicates and errors without human intervention, enabling the system to serve itself in ensuring data quality.
Solution Approach 2:
Manual mechanical processing of performance data is replaced with automated computational systems. The patent implements automated data collection modules that programmatically gather performance metrics, validation modules that algorithmically check data quality, and cleaning modules that use computational methods to remove errors, substituting human manual work with automated mechanical-computational processes.
2Measurement precision
If comprehensive performance data is collected from multiple sources, then data quality improves, but data imperfections and inconsistencies increase
Solution Approach 1:
The data collection system is segmented into multiple independent modules: a data collection module that gathers data from various sources, a data validation module that assesses quality, and a data cleaning module that corrects issues. Each module handles specific tasks separately, making the complex system manageable and maintainable while comprehensively collecting performance data from multiple sources.
Solution Approach 2:
The patent introduces intermediary processing layers between raw data collection and final analysis. The data validation module acts as an intermediary that mediates between collected data and usage, checking for quality issues. The data cleaning module serves as another intermediary that processes and corrects data before it reaches the analysis stage, thereby managing complexity through staged intermediary processing.
3Productivity
If automated data collection is implemented, then analysis speed improves, but data imperfections may be overlooked
Solution Approach 1:
The system implements feedback mechanisms where the data validation module continuously monitors collected data for quality issues and feeds this information back to the data collection and cleaning modules. This feedback loop ensures that automated processes maintain awareness of data quality status and can adjust their operations accordingly, preventing imperfections from being overlooked while maintaining high analysis speed.
Solution Approach 2:
The patent applies preliminary actions by performing data validation and cleaning operations before the main analysis process. The data validation module conducts preliminary quality checks on collected data, and the data cleaning module performs preliminary correction of identified issues, ensuring that the data is prepared and verified before being used in the main consolidation analysis, thereby maintaining both speed and accuracy.
Data Source
AI summary
A method for data assurance in server consolidation is provided. The method includes collecting an inventory of a plurality of source servers in a desired environment and performance data of such source servers, evaluating and checking a data structure of the performance data, applying predetermined time stamps, checks, and statistic computations to the performance data, and evaluating a data quality of the performance data.