Application Portfolio Deployment Optimization via Scoring and Classification
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Solution Overview
Problem
Organizations face inefficiencies in computing resource consumption due to obsolete, outdated, or redundant applications, leading to decreased capability, increased costs, and difficulty in optimizing deployment across hundreds or thousands of applications.
Innovation Solution
An application analysis platform that uses a metrics module, scoring module, classification module, and recommendation module to analyze application data and historical consumption patterns, providing scores, classifications, and recommendations for optimizing deployment, such as updating, decommissioning, or re-platforming applications to improve resource efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If applications are deployed across multiple devices and systems, then application availability and accessibility are improved, but computing resource consumption and deployment complexity increase
Solution Approach 1:
The patent segments the application portfolio into discrete analyzable units and breaks down the optimization process into distinct modules (metrics collection, scoring, classification, recommendation generation). This segmentation allows complex deployment scenarios to be managed through systematic analysis of individual application components rather than treating the entire portfolio as a monolithic system.
Solution Approach 2:
The patent introduces multiple parameters for characterizing applications including efficiency scores, classification categories, and resource consumption metrics. By changing and analyzing these parameters systematically, the system can identify optimization opportunities and transform deployment configurations to improve availability while reducing resource consumption.
2Adaptability or versatility
If obsolete and redundant applications are maintained in the portfolio, then organizational processes and user needs are preserved, but computing resource consumption increases and efficiency decreases
Solution Approach 1:
The patent implements a feedback mechanism where applications are continuously evaluated against efficiency metrics and organizational importance criteria. The scoring and classification modules provide feedback about application performance and value, enabling data-driven decisions about which applications to maintain, optimize, or decommission while preserving those that support critical organizational processes.
Solution Approach 2:
The patent systematically identifies obsolete and redundant applications through scoring and classification, then recommends their decommissioning or consolidation. This process discards low-value applications that consume computing resources while preserving or recovering value from high-value applications that support essential organizational functions.
3Measurement precision
If manual analysis and optimization of each application is performed, then deployment optimization accuracy is improved, but time consumption and operational effort increase
Solution Approach 1:
The patent implements self-service automation where the system automatically collects metrics, calculates scores, performs classifications, and generates recommendations without requiring manual analysis of each application. The automated portfolio analysis service handles the time-consuming tasks of evaluating hundreds or thousands of applications, providing accurate optimization guidance while minimizing operational effort and time investment.
Solution Approach 2:
The patent introduces an intermediary automated analysis platform that acts as a mediator between raw application data and optimization decisions. This intermediary service systematically processes application metrics, applies scoring and classification algorithms, and translates complex data into actionable recommendations, achieving high measurement precision without requiring direct manual intervention for each application.
4Stability of the object's composition
If computing resources are allocated to maintain legacy applications, then backward compatibility and process continuity are ensured, but resource efficiency and cost-effectiveness decrease
Solution Approach 1:
The patent changes the parameters used to evaluate applications from traditional metrics to efficiency-focused metrics including resource consumption rates, modernization readiness, and cost-effectiveness indicators. This parameter transformation enables the identification of legacy applications that consume disproportionate resources, allowing organizations to optimize resource allocation while maintaining process continuity for critical systems.
Solution Approach 2:
The patent identifies legacy applications that are no longer cost-effective to maintain through systematic scoring and classification. It recommends decommissioning or modernizing these applications to recover computing resources, while preserving or migrating critical legacy functions to ensure process continuity. This selective discarding and recovering approach improves resource efficiency without sacrificing operational stability.
Data Source
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AI summary
A device may process application data and historical data to identify a set of metrics to be used to analyze a set of applications and to identify baseline values for the set of metrics. The device may determine a score, for each application of the set of applications, based on values for the set of metrics for the each application of the set of applications. The device may determine a refined classification for the each application of the set of applications based on the historical data and information related to the classification and the score. The device may generate a set of recommendations related to optimizing a current deployment of the set of applications. The device may perform an action to implement the set of recommendations related to optimizing the current deployment of the set of applications.