Application Orchestration for ML-Based Fault Remediation
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Solution Overview
Problem
Existing orchestration systems struggle to efficiently manage and remediate faults in distributed computing environments that rely on multiple cloud and local/on-premises components, as they lack effective mechanisms for monitoring, analyzing, and responding to application dependencies and resource utilization across diverse infrastructure.
Innovation Solution
A system and method for application orchestration that includes event recording, trace information collection, and machine learning-based fault detection and remediation, utilizing an orchestration manager to monitor and manage dependencies across cloud-based and local/on-premises components, and employing machine learning models to identify and address faults.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional orchestration systems are used to manage distributed computing environments, then basic resource allocation is possible, but fault detection and remediation capabilities are insufficient
Solution Approach 1:
The patent introduces an orchestration manager as an intermediary component that sits between the distributed computing environment and the user/system administrators. This mediator consolidates fault detection, analysis, and remediation capabilities in a centralized location, enabling enhanced reliability without proportionally increasing the complexity of individual system components. The orchestration manager acts as a coordinating layer that manages dependencies across cloud and on-premises resources.
Solution Approach 2:
The system implements continuous feedback mechanisms through event recording, trace information collection, and dependency mapping. The orchestration manager monitors system state, detects faults, analyzes their impact on application dependencies, and executes remediation actions. This closed-loop feedback system enables automatic fault response, improving reliability while keeping the system manageable through standardized monitoring and response protocols.
2Measurement precision
If comprehensive monitoring of application dependencies across cloud and on-premises components is implemented, then fault detection accuracy improves, but data processing complexity increases
Solution Approach 1:
The patent segments the monitoring and analysis functions into distinct modular components: event recording modules, trace information collection modules, dependency mapping modules, and fault analysis modules. Each component handles a specific aspect of data collection and processing, which reduces the complexity of individual data processing tasks while maintaining comprehensive monitoring capability. The segmented architecture allows parallel processing of different data streams.
Solution Approach 2:
The orchestration manager serves as an intermediary that standardizes and consolidates data from multiple sources (cloud components, on-premises components, event logs, trace information). By providing a unified interface and standardized data processing pipeline, the mediator reduces the complexity of integrating and analyzing data from heterogeneous sources while maintaining high fault detection accuracy through comprehensive dependency analysis.
3Productivity
If automated remediation strategies are implemented, then system efficiency improves, but control over remediation actions decreases
Solution Approach 1:
The remediation system is designed to be dynamic and adaptable. The orchestration manager can automatically execute pre-defined remediation strategies based on detected faults, improving efficiency. Simultaneously, the system allows administrators to modify remediation actions, add custom strategies, and adjust automation levels based on specific situations. This dynamic flexibility ensures both high productivity and maintained control over remediation processes.
Solution Approach 2:
The automated remediation system incorporates feedback loops that allow administrators to review detected faults, approve or modify remediation actions, and monitor the results. This feedback mechanism ensures that while automation improves efficiency, human operators retain control and can intervene when necessary. The system learns from feedback to improve future automated responses.
Data Source
AI summary
A method to manage a distributed computing machine comprising: configuring a cluster to provide compute and memory resources to run one or more containers that include computer program code to perform operations of the distributed computing machine; configuring one or more cloud-based components to interact with the distributed computing machine; recording in memory storage media, cloud-based component information that indicates events in which the distributed computing machine and the one or more cloud-based components interact; recording in memory storage media, activity metrics information indicating activity level within the distributed computing machine; using the cloud-based component information and the activity metrics information to train one or more machine learning models to represent behavior of the distributed computing machine.