Predictive QoS Policy Adjustment for Multi-Client I/O Demands

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Solution Overview

Problem

Existing QoS policies in information handling systems are typically set manually, which is impractical for a large number of clients and static policies fail to adapt to changing resource demands over time.

Innovation Solution

Implementing dynamic QoS policies using reinforcement-learning-based techniques that adjust QoS levels based on predictions of resource utilization and I/O demands during specified time windows, leveraging ARIMA forecasting and stochastic gradient descent to optimize latency and I/O operations.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If manual QoS policy configuration is used, then policy implementation is simple, but it becomes impractical for a large number of clients and cannot adapt to changing demands

Engineering Contradiction:

ImproveQoS policy adaptabilityVSAvoidpolicy management complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The patent implements dynamic QoS policies that automatically adjust service levels based on changing client demands and system conditions. Instead of static manual configuration, the system continuously monitors resource utilization and I/O demands, then adapts QoS parameters in real-time to optimize performance across multiple clients

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The system employs machine learning models that enable automatic self-adjustment of QoS policies without manual intervention. The predictive models analyze historical and current data to autonomously determine optimal QoS settings, freeing administrators from manual policy management while maintaining adaptability to changing conditions

Inventive Principle:

Principle #25Self-service

2Productivity

If static QoS policies are used, then policy management is straightforward, but the policies fail to adapt to changing resource demands over time

Engineering Contradiction:

Improveresource utilization efficiencyVSAvoidautomatic policy adjustment

Core Design Contradiction:

ProductivityVSExtent of automation

Solution Approach 1:

The system uses predictive analytics to anticipate future resource demands and I/O patterns before they occur. By analyzing historical data and trends, the machine learning models forecast upcoming demands and proactively adjust QoS policies in advance, optimizing resource utilization before performance degradation occurs

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent implements continuous feedback loops where system performance metrics are monitored, analyzed, and used to automatically adjust QoS policies. The machine learning models learn from actual system behavior and client demands, continuously refining predictions and policy adjustments to maintain optimal resource utilization efficiency

Inventive Principle:

Principle #23Feedback

Data Source

PatentUS12580872B2Dynamic QoS changes

Publication Date: 2026.03.17 DELL PROD LP

AI summary

An information handling system may include at least one processor and a network interface adapter. The information handling system may be configured to: couple to a plurality of client systems via the network interface adapter; implement a quality of service (QoS) policy for each client system; adjust the QoS policies based on predictions regarding resource utilization for resources of the information handling system and input/output (I/O) demands associated with each client system during specified time windows; and service requests from the client systems in accordance with the adjusted QoS policies.