AI Pipeline Logging With Decoupled Broker Failover
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Solution Overview
Problem
Real-time AI applications face significant challenges in maintaining log data integrity when central logging systems become unavailable due to network issues, hardware failures, or other disruptions, leading to potential data loss and increased latency.
Innovation Solution
A fault-tolerant logging system that dynamically determines the optimal host location for logging, utilizing a decoupled message broker and integrated fallback logger to ensure log data is captured and stored reliably, even in the presence of system failures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a central logging system is used for storing log messages, then log data management is centralized and simplified, but the system becomes vulnerable to failures and network issues causing data loss
Solution Approach 1:
The patent divides the centralized logging system into distributed logging agents deployed at multiple locations (server hosts, cloud environments, edge devices). Each agent independently captures and stores log messages locally, eliminating single-point-of-failure vulnerability while maintaining centralized management capabilities through the logging coordinator.
Solution Approach 2:
The system pre-deploys logging agents at multiple locations before failures occur, creating redundant log storage capacity. When network issues or host failures happen, the pre-positioned agents continue capturing logs locally, cushioning against data loss during the transition period before central system recovery.
2Loss of time
If the logging system is deployed at the server host, then log data can be captured immediately with minimal latency, but system resources and load capacity are consumed
Solution Approach 1:
The patent implements dynamic deployment decisions where the logging coordinator evaluates server host load attributes and pipeline attributes in real-time to determine optimal logging system placement. The system can dynamically switch between local and remote host deployment based on current resource availability and failure conditions.
Solution Approach 2:
The system changes the deployment parameter (local vs remote host) based on monitored conditions such as server load, network status, and pipeline characteristics. This parameter flexibility allows optimization of the trade-off between latency and resource consumption under different operational scenarios.
3Productivity
If the logging system is deployed at a remote host, then server host load is reduced, but network dependency increases and may cause data loss during network failures
Solution Approach 1:
The patent introduces a logging coordinator as an intermediary component that manages communication between distributed logging agents and the central logging system. The coordinator handles message queuing, retry logic, and failure recovery, ensuring reliable log data transmission even when direct network connections fail.
Solution Approach 2:
The system pre-configures multiple logging agents at different locations (server hosts, cloud, edge) before network failures occur. When network issues arise, these pre-positioned agents continue capturing logs independently, providing redundant data paths and cushioning against information loss during network disruption.
4Reliability
If a fallback logging mechanism is implemented, then system resilience improves during failures, but device complexity and implementation overhead increase
Solution Approach 1:
The patent designs the logging coordinator and distributed agents with multi-functional capabilities that handle both normal operation and failure scenarios through the same component architecture. The logging agents perform both local capture and remote transmission functions, eliminating the need for separate dedicated failure-handling components.
Solution Approach 2:
The system implements feedback mechanisms where the logging coordinator continuously monitors the operational status of logging agents and network connections. Based on this feedback, the system dynamically adjusts deployment strategies and switches between operational modes (local vs remote) to maintain resilience without requiring complex manual intervention.
Data Source
AI summary
An example operation may include one or more of executing a worker on a server host, wherein the worker comprises a log messenger and at least one processing thread, executing a message broker on the server host, wherein the message broker is decoupled from the worker and comprises a queue, executing at least one artificial intelligence (AI) pipeline via the at least one processing thread of the worker, wherein the at least one AI pipeline is configured to generate log messages, transferring the log messages from the log messenger to the message broker, storing the log messages within the queue of the message broker, determining, by the message broker, that a log message from among the log messages has reached an end of the queue, and in response to a determination that the log message has reached the end of the queue, writing, by the message broker, the log message to a log database.