Transaction Processor Outage Detection via Machine Learning
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Solution Overview
Problem
Existing systems fail to effectively detect and manage outages in transaction processors for online retail websites, leading to prolonged wait times and frustrated users due to undetected failures in processing transactions.
Innovation Solution
A machine-learning-based outage management engine with a detection module that identifies potential outages by monitoring transaction processing metrics and spins up computing agents to track and mitigate outages by redirecting transactions to alternative processors and notifying administrators.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional monitoring methods are used to detect transaction processor outages, then system simplicity is maintained, but outage detection reliability and timeliness deteriorate
Solution Approach 1:
The patent introduces a machine learning model as an intermediary component that analyzes transaction processing metrics and identifies potential outages. This intermediary layer processes raw data from the transaction processor and transforms it into actionable outage predictions, improving detection reliability while keeping the core transaction processing system unchanged
Solution Approach 2:
The system performs preliminary analysis of transaction metrics using machine learning to detect potential outages before they fully impact users. By continuously monitoring and analyzing patterns in real-time, the system can identify early signs of processor failures and trigger alerts or automatic rerouting before complete system failure occurs
2Productivity
If no outage detection system is implemented, then system complexity remains low, but user experience and productivity deteriorate due to prolonged wait times
Solution Approach 1:
The system implements automatic self-service capabilities where the machine learning model autonomously detects outages and triggers remediation actions without human intervention. The system can automatically reroute transactions to alternative processors, send notifications to administrators, and log outage events, maintaining high productivity while managing complexity through automation
Solution Approach 2:
The patent establishes a feedback loop where transaction processing metrics are continuously monitored, analyzed by the machine learning model, and used to generate alerts or automatic responses. This closed-loop feedback system enables rapid detection and response to outages, maintaining productivity by quickly restoring normal operations through automatic rerouting or administrator notification
3Loss of time
If manual monitoring of transaction processors is used, then system complexity is low, but response time and user experience worsen due to delayed outage detection
Solution Approach 1:
The patent replaces manual monitoring mechanisms with an automated machine learning-based system. Instead of human operators manually checking transaction processor status, the system uses automated machine learning models to continuously analyze metrics, detect anomalies, and identify outages in real-time, dramatically reducing detection time while accepting the complexity of automated systems
Data Source
AI summary
System and methods are provided for detecting, tracking, and managing outages of transaction processors. An indication is received indicating a potential outage associated with a transaction processor computer configured to process transactions of an online retail website. The indication can be received from a threshold monitoring service and/or from a machine-learning detection system. A computing service can be initiated to confirm and track the outage over time. An outage may include a number of situations in which the transaction processor fails to process transactions according to a set of predefined processing parameters. If the outage spans a particular time period, the service can perform a number of remedial actions (e.g., notifying an administrator of the outage, etc.).