Network Node Queue Management via Proactive Data Redistribution
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Solution Overview
Problem
Current queuing systems operate reactively, leading to sudden interruptions and data stagnation due to lack of proactive error detection and rebalancing, resulting in incomplete or slow data processing.
Innovation Solution
The system proactively monitors network node queues to detect performance degradation by measuring data processing rates and redistributes remaining data to other nodes when thresholds are met, ensuring continuous data processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If current queuing systems operate reactively without proactive monitoring, then system complexity is reduced, but service reliability deteriorates due to sudden interruptions and complete stoppage of data processing
Solution Approach 1:
The system performs preliminary actions by proactively monitoring queue depth and data processing rates before complete system failure occurs. The coordinator module continuously tracks performance metrics and redistributes data proactively when degradation is detected, preventing complete stoppage of data processing and avoiding reactive emergency responses
Solution Approach 2:
The system implements feedback mechanisms where the coordinator module continuously monitors queue depth and data processing rates from network nodes, compares these metrics against thresholds, and automatically triggers rebalancing actions when performance degradation is detected, creating a closed-loop control system that maintains service reliability
2Productivity
If data is left in the queue without active rebalancing, then system complexity is reduced, but productivity deteriorates due to data stagnation and extremely slow processing
Solution Approach 1:
The system applies dynamics by implementing adaptive rebalancing where the coordinator module dynamically adjusts data distribution based on real-time monitoring of network node performance. When a node's processing rate falls below thresholds, the system dynamically redistributes its queue data to other nodes, and continues monitoring to detect when the original node recovers, creating a flexible response that maximizes data processing throughput
Solution Approach 2:
The system enables self-service through automated monitoring and rebalancing operations. The coordinator module autonomously detects performance degradation, triggers data redistribution, monitors node recovery, and restores normal queue operations without external intervention, allowing the system to self-correct productivity issues while managing the complexity of the rebalancing mechanism
3Reliability
If proactive monitoring and rebalancing is implemented, then service reliability is improved, but device complexity increases due to additional monitoring and coordination mechanisms
Solution Approach 1:
The coordinator module serves multiple functions: it distributes initial data to network nodes, monitors queue depth and processing rates, detects performance degradation, triggers rebalancing operations, and restores normal operations. This multi-functionality consolidates the complexity into a single universal component rather than requiring separate specialized systems for each function
Solution Approach 2:
The system uses feedback loops where the coordinator module monitors performance metrics and automatically adjusts data distribution based on detected conditions. The continuous monitoring of queue depth and processing rates provides feedback that triggers appropriate responses, enabling reliable error detection and recovery while managing complexity through automated control logic
Data Source
AI summary
A system and method for queue management is disclosed. The system and method includes at least: (a) monitoring, by one or more computing devices, a network node queue to determine a value indicating how much data is processed by the network node over a period of time; (b) determining, by the one or more computing devices, whether the value is below a threshold value, wherein the value being below the threshold value indicates a performance degradation of the network node; (c) based on determining the value is below the threshold value, removing, by the one or more computing devices, a remaining data from the network node queue; and (d) transmitting, by the one or more computing devices, the remaining data removed in (c) to a coordinator module for redistribution to one or more other network nodes to process the remaining data.