Network Host Inference System Agent Health Filtering
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Complex computer networks face challenges in monitoring and detecting network performance and failures due to the influence of third-party host processes on agent functionality, which can impair data accuracy and network health assessment.
Innovation Solution
A network monitoring and failure detection system with distributed agents that transmit and receive network probes, assess agent health, and process data to filter out unhealthy agent data, using algorithms and data aggregation to evaluate network health autonomously without relying on external services.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If distributed agents are deployed throughout the network to monitor performance, then network monitoring coverage and data collection capability are improved, but the system becomes vulnerable to third-party host processes that can impair agent functionality and data accuracy
Solution Approach 1:
The system implements a health assessment mechanism that continuously monitors agent status and provides feedback to the data processing component. Unhealthy agents are identified through multiple indicators (process status, data quality metrics, communication reliability) and their data is excluded from network health calculations, creating a feedback loop that maintains measurement accuracy despite the presence of compromised agents
Solution Approach 2:
The system performs self-diagnosis by automatically assessing agent health status through internal monitoring mechanisms. The data processing component autonomously evaluates data quality metrics and agent behavior patterns to identify compromised agents, eliminating the need for external manual verification and enabling the system to self-correct measurement accuracy issues
2Adaptability or versatility
If third-party host processes are allowed to run on hosts with monitoring agents, then host functionality and resource utilization are improved, but these processes can overload shared resources and impair agent performance
Solution Approach 1:
The system dynamically changes monitoring parameters based on agent health status. When an agent is identified as unhealthy, the system adjusts data collection frequency, modifies health assessment thresholds, and changes the weightings applied to different data sources. This adaptive parameter adjustment allows the system to maintain measurement precision even when agents operate in environments with varying levels of host process interference
Solution Approach 2:
The health assessment component acts as an intermediary between raw agent data and network health calculations. It filters and validates data from agents that coexist with third-party processes, using multiple indicators to distinguish between legitimate network variations and data corruption caused by host process interference, thereby preserving measurement accuracy
3Quantity of substance
If the system processes data from all agents to maintain comprehensive network coverage, then monitoring completeness is improved, but impaired data from unhealthy agents can skew network health assessment
Solution Approach 1:
The system applies different processing quality levels to different data sources based on their health status. Healthy agents have their data processed with standard validation and aggregation, while unhealthy agents undergo enhanced scrutiny with multiple verification checks. The data processing component selectively excludes or down-weights data from compromised agents, ensuring that local data quality issues do not propagate to overall network health assessments
Solution Approach 2:
The system performs partial processing of data from potentially unhealthy agents by applying selective validation rules. Instead of completely discarding data from agents with minor issues, the system processes only the portions of data that meet quality thresholds, while excluding problematic data points. This partial action approach maintains comprehensive monitoring coverage while filtering out skewing influences from impaired agents
Data Source
AI summary
A system and methods for monitoring and determining agent and network health, having a network monitoring and failure detection system that collects data reports and accumulates a set of data defined in terms of a time window. The network monitoring and failure detection system makes a determination of the agent health during the time window. The network monitoring and failure detection system then processes the collected data based on the determined health. The processing of the collected data can include disregarding the data, weighing the data, filtering the data, using the data in a feedback loop, or processing the data using another method or algorithm.