Network Node Throughput Monitoring for Failure Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Detecting failures in the complex network architecture of cloud-based computing platforms is challenging due to the vastness and complexity of the network nodes, making it difficult to identify issues promptly and efficiently.
Innovation Solution
A method and device are provided to monitor data throughput at network nodes, comparing current throughput to historical averages to detect deviations, and report potential failures, allowing for automated detection and mitigation of issues across various layers of the network.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If manual monitoring methods are used in complex network architectures, then detection accuracy may be maintained, but detection time and operational effort increase significantly
Solution Approach 1:
The system implements self-service monitoring where the network nodes automatically generate and report their own performance metrics and status information. The monitoring application autonomously collects data from multiple nodes, performs analysis, and generates reports without requiring manual intervention, thereby reducing both detection time and operational effort while maintaining high detection accuracy
Solution Approach 2:
The monitoring application serves multiple functions simultaneously: it collects performance metrics from various network nodes, analyzes the data for anomalies, generates failure predictions, and produces comprehensive reports. This multi-functional approach consolidates what would otherwise require multiple separate tools and manual processes into a single automated system
2Measurement precision
If comprehensive monitoring of all network nodes is implemented, then detection accuracy improves, but system complexity and resource consumption increase
Solution Approach 1:
The monitoring system divides the complex network into manageable segments by monitoring individual network nodes separately. Each node's metrics are collected and analyzed independently, allowing the system to maintain high detection accuracy across the entire network without being overwhelmed by the complexity of monitoring all nodes simultaneously as a monolithic system
Solution Approach 2:
The monitoring application acts as an intermediary between the network nodes and the analysis system. It collects data from multiple nodes, processes and normalizes the information, and presents it in a standardized format for analysis, thereby simplifying the overall system architecture while enabling comprehensive monitoring
3Measurement precision
If historical data analysis is performed for each network node, then failure detection accuracy improves, but processing time and computational resources increase
Solution Approach 1:
The system applies partial analysis by focusing computational resources on nodes that show anomalous behavior or are at higher risk of failure. Rather than performing exhaustive historical data analysis on every node equally, the system identifies and prioritizes nodes requiring detailed analysis, thereby maintaining high detection accuracy while reducing overall computational resource consumption
Data Source
AI summary
Described are examples for detecting possible failure in network nodes by monitoring, by a monitoring application, a data throughput for at least one of data sent or received at a network node over a time period, detecting a possible failure of the network node based on comparing the data throughput to an average data throughput for the network node measured over one or more historical time periods of a similar duration as the time period, and/or reporting the possible failure of the network node.