Sparse Hash Function Sets for Network Message Identification
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current systems and methods for network monitoring and analytics are inadequate for providing comprehensive, real-time monitoring and visualization of complex networks, especially in mission-critical environments, as they are not scalable and cannot handle increased bandwidth or distributed networks effectively, leading to difficulties in assessing network quality and optimizing infrastructure.
Innovation Solution
The implementation of a computer-implemented method using sparse hash function sets to calculate hash values for network messages at multiple observation points, associating metadata with these values, and generating network analytics, which enables highly probable identification of related messages and real-time visualization of network states and flows.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional monitoring methods are used, then implementation is simple, but scalability is poor and they cannot handle distributed networks or increased bandwidth
Solution Approach 1:
The patent segments network monitoring into multiple distributed observation points that independently calculate hash values for messages. Each observation point processes local traffic separately, enabling the system to scale to distributed networks without requiring a centralized monitoring architecture. This segmentation allows the system to handle increased bandwidth and network complexity while maintaining manageable local processing at each node.
2Measurement precision
If comprehensive network monitoring is implemented, then network quality assessment improves, but processing time and computational resources increase
Solution Approach 1:
The patent changes the monitoring parameter from analyzing complete message content to calculating hash values of invariant fields. This parameter transformation maintains measurement precision for identifying related messages and assessing network quality while dramatically reducing processing time and computational resources required, as hash calculations are computationally efficient compared to full message analysis.
3Measurement precision
If hash values are calculated for all message fields, then message identification accuracy improves, but processing overhead increases
Solution Approach 1:
The patent extracts only the invariant fields from complete messages for hash value calculation, excluding variant fields that change during message transit. This extraction approach maintains message identification accuracy by focusing on stable identifying characteristics while reducing processing overhead and energy consumption by eliminating unnecessary computation on fields that would require recalculation anyway.
Data Source
AI summary
Methods, systems, and apparatus for network monitoring and analytics are disclosed. The methods, systems, and apparatus for network monitoring and analytics perform highly probable identification of related messages using one or more sparse hash function sets. Highly probable identification of related messages enables a network monitoring and analytics system to trace the trajectory of a message traversing the network and measure the delay for the message between observation points. The sparse hash function value, or identity, enables a network monitoring and analytics system to identify the transit path, transit time, entry point, exit point, and/or other information about individual packets and to identify bottlenecks, broken paths, lost data, and other network analytics by aggregating individual message data.