API Traffic Monitoring with Machine Learning for SLA Compliance

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Solution Overview

Problem

Existing systems for monitoring and managing API functions are inefficient, often failing to detect compliance issues with service level agreements (SLAs) until API failures occur, leading to wasted resources and increased latency.

Innovation Solution

A system that utilizes a machine learning model to monitor API function traffic, predict potential failures, and adjust API scaling based on traffic patterns, thereby ensuring compliance with SLA requirements and preventing abuse.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If traditional monitoring systems are used to detect API compliance issues, then detection can only occur after failures happen, but this leads to wasted processing resources and increased latency

Engineering Contradiction:

ImproveAPI compliance detection reliabilityVSAvoidTime to detect compliance issues

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The system performs preliminary actions by continuously analyzing API traffic patterns and performance metrics before failures occur. The monitoring system proactively identifies compliance violations and potential failure conditions, allowing administrators to take corrective action before actual API failures happen, thus preventing waste of processing resources and reducing latency.

Inventive Principle:

Principle #10Preliminary action

2Productivity

If API scaling is adjusted reactively after failures, then resource allocation can be optimized, but this causes increased latency and service disruption

Engineering Contradiction:

ImproveAPI resource allocation efficiencyVSAvoidService latency

Core Design Contradiction:

ProductivityVSLoss of time

Solution Approach 1:

The system implements continuous feedback loops that monitor API performance metrics, traffic patterns, and compliance status in real-time. This feedback mechanism enables dynamic adjustment of API scaling and resource allocation based on current system state, allowing the system to respond proactively to changing conditions without service disruption or increased latency.

Inventive Principle:

Principle #23Feedback

3Reliability

If comprehensive API monitoring is implemented to ensure SLA compliance, then service reliability improves, but processing resource consumption increases

Engineering Contradiction:

ImproveSLA compliance assuranceVSAvoidProcessing resource consumption

Core Design Contradiction:

ReliabilityVSUse of energy by moving object

Solution Approach 1:

The system applies partial monitoring actions by focusing computational resources on analyzing only the most critical API metrics and traffic patterns that directly impact SLA compliance. Rather than monitoring every possible parameter, the system identifies and prioritizes key performance indicators, achieving effective compliance monitoring with reduced processing overhead and resource consumption.

Inventive Principle:

Principle #16Partial or excessive action

Data Source

PatentUS20250156249A1Monitoring an application programming interface function and adjusting the same

Publication Date: 2025.05.15 CAPITAL ONE SERVICES LLC

AI summary

In some implementations, an application programming interface (API) monitor may provide traffic information associated with an API function to a machine learning model. The API monitor may determine, based on output from the machine learning model, whether the API function complies with one or more requirements in a service level agreement associated with the API function. Accordingly, the API monitor may transmit, to an administrator device, a report indicating whether the API function complies with the one or more requirements.