Intelligent Request Refusal for Network Deficiency Detection
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Solution Overview
Problem
Distributed network systems face challenges in predicting and managing network traffic spikes and deficiencies, often leading to drastic measures like cutting client access, as existing solutions lack effective methods for determining degradation points and anticipating future issues.
Innovation Solution
A system and method that utilize a policy engine with deficiency detection, request filtering, and feature degradation processors to analyze network metrics, enabling intelligent request filtering and feature degradation policies to proactively manage network deficiencies and traffic spikes by aggregating utilization metrics across multiple nodes and enforcing policies to prevent overload.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If network traffic is monitored at the network level to detect deficiencies, then network stability can be maintained, but the detection is lagging and does not provide early warning
Solution Approach 1:
The patent applies preliminary action by monitoring service-level utilization metrics (CPU, memory, connections) before network-level symptoms appear. The system proactively detects degradation trends at the service layer and triggers filtering policies in advance, preventing network overload before it occurs. This resolves the contradiction by enabling early detection without waiting for network-level indicators.
Solution Approach 2:
The patent segments the monitoring system into multiple layers: service-level monitoring (application layer metrics) and network-level monitoring (traffic metrics). By segmenting detection across these layers, the system achieves early warning through service-level metrics while maintaining overall network stability through coordinated response policies.
2Reliability
If drastic measures like cutting client access are taken to stabilize the system, then network overload is prevented, but client service is disrupted
Solution Approach 1:
The patent applies local quality by implementing granular filtering policies that selectively block specific traffic patterns or clients causing degradation, rather than applying blanket access denial. The system identifies and filters only the problematic requests while allowing legitimate client access to continue, thus maintaining system stability without disrupting overall client service.
Solution Approach 2:
The patent applies partial action by implementing progressive filtering - starting with selective request blocking based on utilization thresholds and escalating only when necessary. This partial filtering approach prevents complete system overload while minimizing disruption to client access, resolving the contradiction between stability and ease of operation.
3Loss of time
If network capacity degradation is analyzed to predict future situations, then proactive management is possible, but the analysis capability is substantially lacking
Solution Approach 1:
The patent applies feedback by continuously monitoring service-level metrics and comparing them against defined thresholds and trends. The system uses this feedback loop to automatically adjust filtering policies in real-time, enabling proactive prediction and response to capacity degradation without requiring complex external analysis systems.
Solution Approach 2:
The patent applies self-service by enabling the network system to autonomously detect degradation, predict future capacity issues, and implement corrective filtering policies without external intervention. The system serves its own monitoring and management needs through integrated service-level metrics collection and automated policy enforcement, reducing the need for complex external analysis infrastructure.
Data Source
AI summary
A system for intelligent request refusal in response to a network deficiency detection, in one example embodiment, comprises an aggregator to aggregate revenue generated by a requesting entity with a revenue generated by requesting entities homogenous to the requesting entity, and a filtering module to filter a response to a service request when an aggregated revenue-to-network-resource-utilization ratio is below a second threshold unless utilization of a plurality of network resources drops below a first threshold.