AI-Driven Autonomic Application Management Framework
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Solution Overview
Problem
Existing software orchestration frameworks struggle with managing application-specific events such as scaling requirements and replication needs, particularly in cloud-native applications, due to complexities in resource management, monitoring integration, and manual effort required for corrective actions.
Innovation Solution
An AI-driven autonomic application management framework that automatically manages applications by obtaining data from electronic devices and databases, determining semantics and structure of natural language texts in service level agreements, extracting service level objectives and metrics, and generating insights for actions based on real-time performance data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If conventional software orchestration frameworks are used for infrastructure management, then infrastructure provisioning can be automated, but application-specific event management (scaling, replication) remains complex and requires manual expertise
Solution Approach 1:
The patent creates a unified orchestration framework that combines both infrastructure management and application-specific event management into a single system. The framework uses a common domain-specific language (DSL) and orchestration engine to handle diverse workloads including infrastructure provisioning, scaling, replication, and service composition, eliminating the need for separate specialized tools and reducing operational complexity.
Solution Approach 2:
The patent introduces an intermediary orchestration layer that sits between the infrastructure management plane and application-specific event handling. This intermediary uses a domain-specific language and structured templates to translate high-level application requirements into concrete infrastructure actions, bridging the gap between automated infrastructure provisioning and complex application event management.
2Reliability
If manual translation of SLA to rules and metrics is performed using syntax-based languages, then service level agreements can be configured, but the process requires significant manual effort and expertise
Solution Approach 1:
The patent enables self-service SLA configuration through an intuitive web-based interface where users can define service level agreements using natural language or simplified forms rather than complex syntax-based languages. The system automatically translates these user-friendly definitions into executable monitoring rules and metrics, eliminating the need for manual translation by experts and significantly reducing configuration time.
Solution Approach 2:
The patent replaces the mechanical process of manual SLA translation with an automated system that uses natural language processing and template-based generation. Instead of requiring users to manually write and configure complex monitoring rules in specialized languages, the system automatically generates the appropriate rules and metrics from user-friendly inputs, substituting automated intelligence for manual labor.
3Ease of operation
If conventional orchestration frameworks are used, then infrastructure management APIs are available, but resource management and monitoring integration remain complex
Solution Approach 1:
The patent merges resource management, monitoring, and alerting functionalities into a unified orchestration platform. Rather than requiring separate tools and integrations for each function, the system combines these capabilities into a single coherent framework that automatically correlates events across infrastructure and application layers, simplifying the overall integration complexity while maintaining ease of API access.
Solution Approach 2:
The patent segments the complex orchestration functionality into modular, composable components that can be independently configured and combined. The system uses a domain-specific language with structured templates that allow users to build complex resource management and monitoring solutions by assembling predefined building blocks, reducing integration complexity through systematic decomposition.
4Productivity
If cloud-native applications are deployed for scalability, then dynamic management is improved, but autonomic framework requirements become more stringent
Solution Approach 1:
The patent implements dynamic autonomic management capabilities that automatically adapt to changing workload conditions. The orchestration system continuously monitors application performance and infrastructure metrics, dynamically adjusting resource allocation, scaling policies, and replication strategies without manual intervention. This dynamic behavior enables cloud-native applications to maintain high scalability while the system provides the required level of autonomic management.
Solution Approach 2:
The patent incorporates closed-loop feedback mechanisms where the orchestration system continuously monitors application performance and infrastructure state, compares actual conditions against desired targets defined in service level agreements, and automatically initiates corrective actions. This feedback-driven approach enables the system to provide the stringent autonomic management required by cloud-native applications, automatically responding to scaling requirements and replication needs based on real-time conditions.
Data Source
AI summary
A computer-implemented method for automatically managing applications in environments using AI driven autonomic application management framework is disclosed. The computer-implemented method includes obtaining items of data from electronic devices associated with users, and databases; determining semantics and structure of natural language texts associated with the service level agreements (SLAs) based on analysis of natural language texts, using a first AI model; extracting service level objectives (SLOs) and associated metrics corresponding to services specified in SLAs; obtaining first real-time data including actual performance levels, and service level indictors, of the services, from monitoring platforms; determining whether actual performance levels of services, are compliant with expected performance levels; automatically updating the SLOs and associated metrics based on deviations of actual performance levels of the services from expected performance levels; and generating insights associated with actions, to be applied to corresponding services, to be performed to automatically manage the applications.