LLM Feedback Loop for Cloud Application Performance Remediation
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Solution Overview
Problem
Existing observability tools in cloud-native environments fail to bridge the gap between detection and resolution of performance issues, leading to inefficient and error-prone processes due to the lack of automated remediation capabilities.
Innovation Solution
An application performance automation (APA) system that autonomously detects performance inefficiencies, applies interventions in sandbox environments for validation, and leverages large language models (LLMs) to recommend code or configuration modifications, ensuring data sovereignty and human-in-the-loop review.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Difficulty of detecting and measuring
If existing observability tools are used to detect performance issues, then detection capability is improved, but automated remediation capability deteriorates
Solution Approach 1:
The patent introduces an intermediary automation layer that connects observability tools with remediation systems. This intermediary comprises orchestration logic that translates detected performance issues into automated remediation actions, bridging the gap between detection and resolution without requiring direct integration between disparate systems.
Solution Approach 2:
The system implements self-service capabilities where the automation layer autonomously responds to detected performance issues without human intervention. The system can automatically execute remediation actions, validate results, and iterate based on feedback, enabling the system to service itself and resolve issues independently.
2Device complexity
If manual remediation processes are used, then system complexity is reduced, but productivity deteriorates
Solution Approach 1:
The patent implements preliminary action by pre-configuring remediation playbooks and response protocols before issues occur. The automation layer has pre-defined actions and validation criteria ready to execute immediately upon detection, eliminating the need for manual analysis and decision-making during incident response.
Solution Approach 2:
The system dynamically changes operational parameters based on detected conditions, automatically adjusting system configurations, resource allocations, or operational modes to resolve performance issues. This parameter-driven approach enables rapid response while maintaining manageable system complexity through structured parameter management.
3Productivity
If automated interventions are deployed without validation, then productivity is improved, but reliability deteriorates
Solution Approach 1:
The patent implements a feedback loop where the automation layer monitors the results of deployed interventions and uses this information to validate effectiveness. The system continuously collects telemetry data, compares actual outcomes against expected improvements, and adjusts subsequent actions based on this feedback, ensuring reliable and accurate remediation.
Solution Approach 2:
The system employs prior cushioning by implementing validation mechanisms and safety checks before deploying interventions. The automation layer预先 establishes success criteria, rollback procedures, and validation gates to prevent harmful actions, cushioning against potential failures while maintaining rapid response capability.
Data Source
AI summary
A system for automated code modification improves application performance in cloud environments by integrating telemetry analysis with large language model (LLM)-driven reasoning. The system collects contextual information about a target application, including metadata, source code, and configuration files, and correlates it with real-time telemetry data related to application performance. Based on this data, the system constructs a structured LLM prompt using a predefined schema, which is transmitted to an LLM. The prompt instructs the LLM to recommend modifications to source or configuration files that may enhance performance, along with natural language explanations for those recommendations. In response to receiving the LLM's response, the system extracts the proposed code or configuration changes and associated rationale, and presents them to a user via a client device interface.