Semantic Priority Bandwidth Control for Network Congestion
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Solution Overview
Problem
In computer systems, managing network bandwidth effectively is challenging due to the lack of real-time monitoring and prioritization of resource usage, leading to network congestion and poor performance, especially in multi-user environments where activities with varying semantic importance coexist.
Innovation Solution
A computer-implemented method that identifies current resource usage by software processes, determines their execution platforms, and applies mitigation actions based on semantic context using natural language processing of metadata, such as reducing video stream resolution or converting video calls to audio calls, to optimize bandwidth allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If bandwidth management and control are implemented, then network performance and utilization are optimized, but system complexity and monitoring requirements increase
Solution Approach 1:
The system automatically monitors bandwidth usage, identifies semantic context of processes, and executes mitigation actions without human intervention. The bandwidth management system serves itself by autonomously detecting congestion, analyzing process metadata, determining priority contexts, and applying control measures, eliminating the need for manual configuration and reducing operational complexity.
Solution Approach 2:
The system dynamically changes bandwidth allocation parameters based on real-time monitoring and semantic context analysis. When congestion is detected, the system adjusts bandwidth parameters for different processes based on their identified context (work, school, entertainment), allowing flexible optimization of network utilization without fixed rigid configurations.
2Reliability
If real-time bandwidth monitoring and control are implemented, then network congestion is reduced, but processing overhead and resource consumption increase
Solution Approach 1:
The system applies differentiated monitoring and control strategies to different processes based on their semantic context. Instead of uniformly monitoring all processes with equal intensity, the system identifies the context of each process (work, school, entertainment) and applies appropriate bandwidth management actions only where necessary, reducing overall processing overhead while maintaining network performance.
Solution Approach 2:
The system performs selective monitoring and analysis only when bandwidth congestion is detected, rather than continuously analyzing all processes at full depth. When congestion occurs, the system partially processes metadata for active processes to identify semantic context, applying just enough processing to resolve the congestion without excessive continuous analysis of all system processes.
3Measurement precision
If semantic context analysis using NLP is applied to all processes, then prioritization accuracy is improved, but processing time and computational resources increase
Solution Approach 1:
The system extracts and analyzes metadata from processes in advance to identify their semantic context before bandwidth allocation decisions are made. By performing NLP analysis on process metadata (such as process names, descriptions, and attributes) beforehand, the system prepares prioritization information that can be quickly applied when congestion occurs, reducing real-time processing delays.
Solution Approach 2:
The system extracts only the essential metadata elements from processes that are relevant for semantic context identification, rather than analyzing complete process information. By selecting and analyzing only key metadata fields (such as process type, application category, or predefined attributes), the system achieves adequate prioritization accuracy with reduced processing time and computational resources.
Data Source
AI summary
Disclosed embodiments provide techniques for analyzing a semantic priority of an activity utilizing computing resources, and provides mitigation actions to resolve resource shortages in real-time. The computing resources can include network bandwidth usage, as well as processing cycles, memory usage, and/or other shared computing resources. Disclosed embodiments perform a semantic priority analysis of user activities. The semantic priority analysis can include utilizing natural language processing (NLP), analysis of a user calendar, and/or additional application data to infer a semantic priority. When computing resources such as network bandwidth exceed a predetermined level, then a mitigation action is executed, enabling the computing resources to be reduced while still allowing the higher priority activities (e.g., work and school) to continue.