Network Resource Allocation via Engagement Predictions
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Solution Overview
Problem
Information handling systems face challenges in dynamically allocating network resources to various workloads based on user engagement and priority, especially in multi-user, multi-environment scenarios like homes with different AV configurations and network availability, where existing systems struggle to seamlessly migrate sessions and manage resource distribution efficiently.
Innovation Solution
A method involving a hub device that receives workload information, determines engagement and dependency predictions, and assigns network resources based on these predictions to ensure optimal resource allocation across different environments and devices, allowing for seamless migration of gaming sessions and simultaneous multi-user gameplay.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If network resources are allocated based on traditional methods without engagement analysis, then resource allocation is simple, but resource distribution efficiency and user experience quality deteriorate
Solution Approach 1:
The system performs preliminary analysis of user engagement patterns and workload requirements before actual resource allocation occurs. By predicting engagement levels and resource needs in advance, the system can proactively allocate network resources to high-priority workloads, improving distribution efficiency without adding complexity during the allocation moment itself.
Solution Approach 2:
The system continuously monitors user engagement metrics and workload performance, using this feedback to refine future resource allocation decisions. The engagement analysis module processes user behavior data and adjusts resource assignments dynamically, creating a closed-loop system that improves efficiency over time while maintaining manageable complexity through automated learning.
2Measurement precision
If the system tracks detailed user engagement data, then workload priority determination improves, but system complexity and data processing requirements increase
Solution Approach 1:
The system applies different levels of engagement analysis to different workloads and users based on their importance and characteristics. High-priority workloads receive detailed engagement tracking, while lower-priority workloads use simplified metrics. This localized quality approach maintains measurement precision for critical resources while reducing overall system complexity.
Solution Approach 2:
The system dynamically adjusts the granularity and type of engagement parameters collected based on current network conditions, workload types, and user profiles. By changing which parameters are monitored and how they are processed, the system maintains accurate engagement measurement without consistently operating at maximum complexity.
3Adaptability or versatility
If the system supports multiple environments and devices simultaneously, then system versatility improves, but resource management complexity increases
Solution Approach 1:
The system segments network resource management into separate, specialized modules: engagement analysis, workload prioritization, and resource allocation. Each module handles specific aspects of multi-environment support independently, allowing the system to maintain versatility across different devices and networks while managing complexity through modular architecture.
Solution Approach 2:
The engagement analysis module is designed as a universal system that can process various types of user behavior data from different devices and environments using the same core principles. This multi-functional approach allows the system to adapt to new environments without proportionally increasing complexity, as the same engagement tracking mechanisms apply across all contexts.
4Productivity
If the system dynamically adjusts resource allocation in real-time, then resource optimization improves, but system response time and processing overhead increase
Solution Approach 1:
The system performs engagement analysis and priority determination in advance of actual resource allocation events. By pre-processing user behavior data and predicting workload requirements, the system reduces real-time processing needs during critical allocation moments, achieving continuous optimization without significant time penalties.
Solution Approach 2:
The system implements dynamic resource allocation that adapts to changing conditions, but uses intelligent batching and caching of engagement data to minimize processing frequency. The allocation algorithm dynamically adjusts based on predicted priorities while using historical data to reduce real-time calculations, balancing optimization with processing efficiency.
Data Source
AI summary
Systems and methods described herein may provide a system that enables the dynamic assignment of network resources between multiple workloads executing on a network. A computing device may receive workload information relating to a plurality of workloads executing within a network. The computing device may use the workload information to determine engagement and dependency predictions for the plurality of workloads and resource predictions for the plurality of workloads. Based on the workload information and the engagement and dependency predictions, the computing device may determine workload priority predictions for the plurality of workloads. The computing device may assign, based on the resource predictions and workload priority predictions, network resources of the network to the plurality of workloads.