Autonomous Hybrid Cloud Integration via Self-Healing APIs
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Solution Overview
Problem
Hybrid cloud integration poses challenges such as system instability, reliability issues, and the need for continuous monitoring and scalability between on-premises and cloud applications, along with rapid error-free security configurations, which existing technologies struggle to address effectively.
Innovation Solution
A comprehensive, autonomous intelligent cloud integration solution is deployed using machine learning algorithms to automatically select and deploy APIs, monitor operation states, and execute healing actions in response to errors, providing a customized and self-healing hybrid cloud environment with minimal human intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If cloud integration services are deployed to provide dynamic scaling and on-demand resources, then productivity and adaptability are improved, but system stability and reliability deteriorate due to diverse ever-changing business process environments
Solution Approach 1:
The patent implements continuous monitoring of integration objects, operations, and business processes with automated feedback loops that detect errors and trigger healing actions. This feedback mechanism maintains reliability by constantly comparing actual system state against expected states and automatically correcting deviations, enabling the system to handle dynamic business processes while maintaining stability.
Solution Approach 2:
The system employs self-healing capabilities where the integration platform automatically detects, diagnoses, and repairs errors without human intervention. The automated error healing processes include self-diagnosis, self-correction, and self-verification mechanisms that enable the system to maintain its own stability while providing dynamic scaling services.
2Measurement precision
If comprehensive monitoring is implemented to ensure continuous visibility and reliability, then measurement precision is improved, but device complexity and loss of time increase
Solution Approach 1:
The monitoring system is designed as a universal platform that handles multiple functions including error detection, diagnosis, healing, and verification through a single integrated architecture. This multi-functional approach reduces overall system complexity while maintaining comprehensive monitoring visibility across diverse integration objects and business processes.
Solution Approach 2:
The monitoring system automatically performs self-diagnosis and self-healing without requiring complex external intervention protocols. This self-service capability simplifies the monitoring architecture by eliminating the need for separate complex diagnostic and repair systems, while maintaining high measurement precision through automated continuous monitoring.
3Reliability
If automated error healing is executed to maintain system reliability, then reliability is improved, but device complexity increases due to additional monitoring and intervention mechanisms
Solution Approach 1:
The system implements self-healing mechanisms where the integration platform automatically detects errors, diagnoses root causes, executes appropriate healing actions, and verifies recovery without external intervention. This self-service approach maintains reliability while minimizing the complexity of healing mechanisms by embedding diagnostic and repair capabilities directly within the existing system architecture rather than adding separate complex healing systems.
Solution Approach 2:
The automated healing process uses continuous feedback from monitoring mechanisms to trigger appropriate healing actions based on detected error states. The feedback loop includes error detection, state evaluation, healing action selection, execution, and verification, creating a streamlined process that maintains reliability without requiring overly complex healing mechanisms.
Data Source
AI summary
A system and method of deploying and managing an integration for a hybrid computing environment is disclosed. The proposed systems and methods provide an intelligent hybrid cloud architecture and environment that offers reduced deployment times, and little to no errors. The system incorporates an artificial intelligence (AI) powered solution that is API-enabled and pre-integrated with system chatbots, as well as providing a secure, accelerated integration with available cloud ecosystems. The proposed solution is able to analyze business processes and derive and build deep insights toward the enterprise cloud integration, improving security, design, and performance of the hybrid architecture.