Federated Gateway Query Planning for Low-Latency Network Routing
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing network configurations face inefficiencies due to performance bottlenecks, latency issues, and increased latency and bottlenecking as the network scales, leading to exceeding service level agreements and inefficiencies in query fulfillment.
Innovation Solution
Implementing a federated gateway with machine learning models for query plan generation, caching strategies, and anomaly detection to optimize network routing and reduce latency, leveraging asynchronous and reactive patterns to manage orchestration of nodes and services.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If traditional network routing is used to fulfill queries, then service coverage is maintained, but latency increases and throughput decreases as the network scales
Solution Approach 1:
The system performs preliminary actions by generating query plans in advance using machine learning models, caching validated schemas and route data, and pre-establishing service invocation patterns. This allows the gateway to quickly execute pre-planned routing decisions without real-time computation delays, thereby reducing query fulfillment latency while maintaining service coverage.
2Adaptability or versatility
If more services are added to the network to improve functionality, then service capability increases, but network complexity and operational costs increase
Solution Approach 1:
The federated gateway acts as an intermediary between users and the complex network of services. It maintains cached schemas and route data that abstract away the underlying network complexity, allowing users to access diverse service capabilities through a simplified interface. The gateway's machine learning model automatically optimizes service invocation patterns, reducing the operational burden of managing complex service networks.
3Manufacturing precision
If query validation is performed against published schemas, then data accuracy is ensured, but query processing time increases
Solution Approach 1:
The system performs schema validation in advance by caching validated schemas from published services. When queries arrive, the gateway compares them against pre-validated schemas rather than performing full validation processing, significantly reducing query processing time while maintaining data accuracy requirements.
4Productivity
If network scaling is implemented to handle increased load, then service capacity increases, but latency and bottlenecks worsen
Solution Approach 1:
The system dynamically adapts to varying network conditions and service capacities by using machine learning models to generate optimized query plans in real-time. The gateway monitors service performance metrics and adjusts routing decisions dynamically, allowing the network to efficiently handle increased load without proportionally increasing latency, thereby improving service capacity while maintaining response speed.
Data Source
AI summary
Various systems, computer-implemented methods, and computer program products are disclosed that use improved machine learning models to provide optimized network configurations to send, receive, and process data based on a request for data from upstream services. A device may receive a first query defining a request for data, analyze the first query, and determine one or more query parts. A device may generate, via a machine learning model, a query plan for fulfilling the first query using one or more nodes and transmit a second query to one or more services based on the query plan. A device may obtain a dataset from the one or more services as a response to the first query and the second query. A device may detect, via the machine learning model, for an anomaly based on fulfilling the first query.