Dynamic QoS Controller Using Time Series Prediction
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Solution Overview
Problem
Current network management systems are unable to timely respond to rapid changes in Quality of Service (QoS) levels due to factors beyond network capacity, requiring manual decisions for capacity upgrades which are not efficient in maintaining service level agreements (SLAs).
Innovation Solution
An automated QoS controller system that retrieves QoS data, processes it using time series prediction algorithms, and generates network configuration commands to dynamically adjust router interface queue bandwidth, predicting future utilization levels and ensuring continued service delivery according to SLA requirements, with a graphical user interface for monitoring and operator visibility.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If manual monitoring and decision-making processes are used for network capacity management, then operational simplicity is maintained, but the response time to QoS changes is insufficient and service level agreements cannot be timely maintained
Solution Approach 1:
The QoS controller automatically monitors network performance metrics, predicts future QoS levels using time series algorithms, and executes configuration changes without human intervention. The system serves itself by autonomously detecting QoS degradation trends and applying corrective actions to router interfaces, eliminating the need for manual monitoring and decision-making while maintaining operational simplicity
Solution Approach 2:
The system uses time series prediction algorithms to forecast future QoS levels before actual degradation occurs. By predicting queue utilization trends and identifying potential SLA violations in advance, the controller proactively adjusts network configurations preemptively, enabling faster response times by acting before problems manifest rather than reacting after they occur
2Measurement precision
If broad network monitoring is used to maintain aggregate service levels, then overall network stability is maintained, but rapid changes in customer-experienced QoS cannot be timely detected or responded to
Solution Approach 1:
The system transitions from broad aggregate network monitoring to localized, interface-specific QoS monitoring. By implementing prediction models at individual router interface levels and tracking queue utilization metrics for specific service flows, the system achieves precise measurement of customer-experienced QoS at granular locations, enabling targeted responses to local degradation without requiring comprehensive monitoring of entire network aggregates
Solution Approach 2:
The QoS controller establishes continuous feedback loops by monitoring network performance metrics, comparing actual QoS levels against predicted values and SLA thresholds, and automatically executing configuration changes when deviations are detected. This closed-loop feedback mechanism enables rapid detection and response to QoS changes by continuously measuring performance and immediately acting on measured deviations, significantly improving both measurement precision and response efficiency
3Reliability
If automated QoS control with prediction algorithms is implemented, then response time to QoS changes is improved and SLA compliance is enhanced, but system complexity and computational requirements increase
Solution Approach 1:
The system employs time series prediction algorithms that analyze historical QoS parameters (queue utilization, traffic patterns, service levels) to forecast future states. By transforming historical parameter data into predictive insights, the system enhances SLA compliance through data-driven decision-making while managing complexity through standardized statistical methods rather than requiring complex machine learning models
Solution Approach 2:
The QoS control system is segmented into modular functional components: data collection modules that gather network metrics, prediction modules that apply time series algorithms to forecast QoS levels, decision modules that compare predictions against SLA thresholds, and execution modules that apply configuration changes. This segmentation isolates complexity into discrete, manageable functions, allowing automated control and prediction capabilities to be implemented without overwhelming system complexity
Data Source
AI summary
Various embodiments comprise systems, methods, architectures, mechanisms and apparatus for controlling Quality of Service (QoS) within a service provider network by retrieving from the network QoS related data, processing the retrieved QoS related data via one or more time series prediction algorithm to determine QoS prediction data, and responsively generating network management or configuration commands adapted to ensure continued services delivery in accordance with QoS requirements.