Cloud Application State Alignment for Operational Deviation Remediation
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Solution Overview
Problem
There is a disconnect between the operational stage and the design stage of software applications in cloud solutions, with operational deviations often going unnoticed due to underutilized and siloed data sources during the cloud lifecycle.
Innovation Solution
A system and method that utilizes a shift-left operational insight (SLOI) approach, employing large language models (LLMs) and a multi-agent framework to analyze operational states, identify deviations, and implement remediation actions by leveraging data from various data sources, including provisioning ticketing workflows, compliance and risk evaluation systems, and knowledge graphs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If data sources are kept separate and siloed during cloud lifecycle stages, then data management is simpler, but operational deviations cannot be effectively detected
Solution Approach 1:
The patent merges previously siloed data sources from different cloud lifecycle stages (Day 0 design data, Day 1 build data, and Day 2 operational data) into a unified data structure. This integration enables comprehensive deviation detection by allowing comparison between intended operational state (from design requirements) and actual operational state (from runtime data), thereby resolving the contradiction between detection accuracy and data management simplicity.
Solution Approach 2:
The patent introduces an intermediary component that standardizes and harmonizes data from multiple sources before analysis. This intermediary layer transforms heterogeneous data formats into a unified structure, enabling effective deviation detection without requiring complex point-to-point integrations between all data sources, thus balancing detection capability with implementation complexity.
2Reliability
If comprehensive data collection from all cloud lifecycle stages is implemented, then operational deviations can be detected, but system complexity increases
Solution Approach 1:
The patent segments the cloud lifecycle into distinct phases (Day 0 design, Day 1 build, Day 2 operate) and collects data specific to each phase separately. Each phase's data is processed and standardized independently before being integrated for deviation analysis. This segmentation approach maintains reliability by ensuring comprehensive data coverage while reducing system complexity through modular, phase-specific data collection strategies.
Solution Approach 2:
The patent creates a universal data structure and processing framework that can handle multiple types of data from different cloud lifecycle stages through a single integrated system. This multi-functional approach allows the same infrastructure to process design requirements, build artifacts, and operational metrics, thereby achieving comprehensive deviation detection without proportionally increasing system complexity.
3Productivity
If manual monitoring and analysis of operational state is used, then implementation is simpler, but deviation detection efficiency is lower
Solution Approach 1:
The patent implements an automated feedback loop that continuously monitors operational state data, compares it against intended operational state from design requirements, and identifies deviations automatically. This feedback mechanism dramatically increases deviation detection speed by replacing manual monitoring with continuous automated analysis, while the structured comparison framework keeps the automation complexity manageable through systematic processing rules.
Solution Approach 2:
The system enables self-service deviation detection by automatically gathering data from multiple sources, processing it through standardized frameworks, and generating deviation reports without requiring manual intervention. This self-service capability significantly improves detection productivity while maintaining reasonable complexity levels through automated, rule-based processing rather than complex human-in-the-loop systems.
Data Source
AI summary
Aspects of the subject disclosure may include, for example, obtaining a group of software application design requirements associated with a software application, generating an intended operational state of the software application based on the group of software application design requirements, and building the software application utilizing one of more cloud computing resources based on the group of software application design requirements. Further embodiments include obtaining actual operational state data associated with the building of the software application from a group of data stores, determining an actual operational state of the software application based on the actual operational state data, and identifying a group of deviations from the intended operational state and the actual operation state. Additional embodiments include determining a group of remediation actions associated with the group of deviations, and implementing a first portion of the group of remediation actions on the software application. Other embodiments are disclosed.