Webhook Response Correlation for Automated Compute Resource Actions
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Solution Overview
Problem
Current solutions lack the ability to analyze customer requests subsequent to webhook notifications to identify correlations and improve the operational health and efficiency of compute infrastructure.
Innovation Solution
A compute management platform that stores information on historical webhook events and corresponding REST requests, correlates them, and automates actions based on these correlations to enhance resource utilization and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If webhook notifications are sent to customers for events, then customers can respond to events, but the system cannot analyze the correlation between notifications and customer actions
Solution Approach 1:
The system implements feedback by capturing customer actions (REST API requests) following webhook notifications, analyzing the correlation between notifications and actions, and using this analysis to generate automated responses. This closed-loop feedback mechanism recovers the lost correlation information without requiring complex manual tracking systems.
Solution Approach 2:
The system introduces an intermediary analytics component that sits between the webhook notification system and customer actions. This intermediary captures, correlates, and analyzes the relationship data, enabling correlation analysis without adding significant complexity to the core webhook infrastructure.
2Reliability
If multiple REST requests are sent in response to a webhook notification, then comprehensive actions can be taken, but resource utilization efficiency decreases
Solution Approach 1:
The system merges multiple correlated REST requests into a single automated response operation. By analyzing patterns in customer actions following webhook notifications, the system combines similar requests and executes them as unified operations, maintaining comprehensive action coverage while significantly improving resource utilization efficiency.
Solution Approach 2:
The system performs preliminary analysis of customer actions to identify correlated request patterns before automated responses are triggered. This advance preparation enables the system to pre-configure optimized response operations that maintain reliability while improving efficiency during actual event responses.
3Reliability
If no analysis is performed on customer requests, then system complexity remains low, but opportunities to improve operational health are lost
Solution Approach 1:
The system implements self-service by automatically analyzing customer actions, identifying correlations with webhook notifications, and generating optimized automated responses without requiring external intervention. This self-analyzing capability improves operational health while keeping the system architecture relatively simple through automated rather than manually-managed complexity.
Data Source
AI summary
A computer receives, from a network device which communicates using a web-based protocol, a webhook requesting a notification for a first event associated with a first resource monitored by the computer. The computer detects an occurrence of the first event and transmits the notification to the network device. The computer stores, in a data structure, information associated with the notification. The computer monitors requests received from the network device using the web-based protocol subsequent to transmitting the notification to the network device and stores, in the data structure, information associated with a respective monitored request. The computer queries the data structure for requests associated with the first resource and a predefined period of time. The computer correlates the webhook and the queried requests. The computer improves efficiency associated with operating the first resource by performing an action based on the correlation.