Server Consolidation Tool with Granular Performance Data Segmentation
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Solution Overview
Problem
Existing server consolidation tools lack flexibility and comprehensive analysis capabilities, often relying on summarized performance data that fails to reflect the time-dynamic nature of system performance, leading to overly conservative consolidation plans and neglecting data confidentiality concerns.
Innovation Solution
A server consolidation tool that integrates data quality management, analysis, and results seamlessly, allowing users to conduct detailed analyses across various subsets of performance data, offering a graphical user interface for navigating consolidation scenarios and providing flexibility in defining server usability statuses and constraints, with direct access to data for further reporting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If summarized performance data (such as average or peak CPU utilization) is used for server consolidation analysis, then the analysis process is simplified, but the time-dynamic nature of system performance is not reflected, leading to overly conservative consolidation plans
Solution Approach 1:
The patent segments performance data by multiple dimensions including time periods, server groups, and resource types. Instead of using single summarized metrics, the system divides performance data into granular segments that can be analyzed separately and recombined to provide comprehensive insights while maintaining analytical simplicity.
Solution Approach 2:
The patent adds temporal dimensions to performance analysis by analyzing performance across different time periods (peak, off-peak, average periods). This multi-dimensional approach transforms single-point summarized data into time-series performance profiles, enabling more accurate consolidation planning without excessive complexity.
2Measurement precision
If detailed performance data analysis is conducted across multiple servers, then consolidation accuracy improves, but the amount of performance data involved becomes staggering and difficult to manage
Solution Approach 1:
The patent merges performance data from multiple servers into consolidated views organized by server groups, time periods, and resource types. By combining data aggregation with intelligent filtering and segmentation, the system manages large volumes of performance data without overwhelming complexity, presenting synthesized results that maintain accuracy while simplifying management.
3Ease of operation
If conventional consolidation tools with simple rules of thumb are used, then the ease of operation is improved, but consistency and thoroughness are lacking, and performance data issues are overlooked
Solution Approach 1:
The patent implements automated data quality assessment and validation mechanisms that operate without requiring manual intervention. The system automatically detects data quality issues, validates performance metrics, and ensures consistency across analyzed servers, maintaining high reliability while preserving ease of operation through automated self-service functionality.
4Measurement precision
If comprehensive performance data is collected and analyzed, then data-driven consolidation decisions are enabled, but data confidentiality concerns arise when transferring data to external sites
Solution Approach 1:
The patent introduces local data processing and analysis capabilities that act as intermediaries between raw performance data and consolidation recommendations. By enabling analysis to be performed locally or in controlled environments rather than requiring external data transfer, the system maintains data confidentiality while preserving data-driven analysis quality through intermediate processing layers.
Data Source
AI summary
A method for server consolidation is provided. The method includes collecting performance data of a plurality of source servers in a desired environment, selecting a group of one or more source servers from the plurality of source servers for consolidation, marking each source server in the with one of multiple usability statuses with one of such statuses indicates the marked source server is to be replaced or reused as necessary in the server consolidation, selecting a target platform for a new server, and performing a first server consolidation analysis of the first group based at least on the collected performance data, the initial usability status of each source server in the first group, and the first selected target platform.