Queue Engine Prioritized De-queueing for Critical Data
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Solution Overview
Problem
Existing communication systems struggle to prioritize critical data over non-critical data in complex networking environments, leading to delays and inefficiencies in data transmission, particularly in telemetry applications where security notifications and other critical information are often stuck behind non-critical data flows.
Innovation Solution
A communication system with queue engines and queue trees that automatically prioritize data based on Quality of Service (QoS) identifiers and differentiated services code points (DSCP), allowing for proportional bandwidth allocation and de-queuing of data streams across different traffic classes, enabling critical data to be transmitted promptly without manual administrator intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data is transmitted through existing queueing systems without prioritization, then network simplicity is maintained, but critical data (such as security notifications) is delayed behind non-critical data flows
Solution Approach 1:
The system segments data flows into different queues based on QoS identifiers and DSCP values. Multiple queues are created within the network element, each handling different traffic classes (e.g., critical vs. non-critical data). This segmentation allows critical data to be isolated in dedicated queues that are processed independently, ensuring timely delivery without overwhelming the entire system.
Solution Approach 2:
Different quality treatments are applied to different data flows based on their QoS identifiers. The system implements local prioritization where critical data flows receive preferential treatment (higher priority dequeuing) while non-critical flows use standard queueing. This local differentiation of service quality resolves the contradiction by providing tailored handling to different data types without requiring complete system redesign.
2Productivity
If manual administrator intervention is used to configure queue priorities, then precise control is achieved, but system response time increases and scalability decreases
Solution Approach 1:
The system performs self-configuration by automatically parsing QoS identifiers and DSCP values from incoming data flows. The queue engine autonomously determines priority levels and assigns appropriate queues without requiring manual administrator intervention. This self-service capability maintains precise control over data prioritization while eliminating configuration delays and improving scalability.
Solution Approach 2:
The system pre-configures queue structures and priority frameworks before data flows arrive. Queue tables and priority mappings are established in advance, allowing the system to immediately begin prioritizing data based on QoS identifiers without requiring real-time administrator configuration. This preliminary preparation enables fast response times while maintaining operational simplicity.
3Loss of time
If all data flows are treated equally in the queue, then system simplicity is maintained, but latency for critical data increases due to contention with non-critical data
Solution Approach 1:
The queue engine dynamically adjusts prioritization based on the QoS identifiers and DSCP values present in incoming data flows. Rather than using static, fixed priority assignments, the system adapts its queue selection and prioritization in real-time based on the actual data characteristics. This dynamic approach reduces latency for critical data while managing complexity through algorithmic automation.
Solution Approach 2:
The system implements a mechanism where critical data flows are identified and routed through expedited paths that bypass standard queueing contention. By recognizing QoS identifiers and DSCP markers, the system can skip directly to appropriate high-priority queues or processing paths, rushing critical data through the network element without being blocked by non-critical data flows.
Data Source
AI summary
A method is provided in one example and includes allocating a first queue, allocating at least two default queues, where the at least two default queues depend from the first queue, allocating a plurality of local queues that each depend from one of the at least two defaults queues, receiving data in a data stream, determining a quality of service (QoS) associated with the data, and assigning the data to one of the plurality of local queues based on the determined QoS. In an example, the QoS is a differentiated services code point.