Dynamic Task Assignment in Campus Network Load Balancing
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Solution Overview
Problem
Traditional network systems statically assign tasks to access points and network controllers without considering dynamic load conditions or task preferences, leading to potential bottlenecks and inefficiencies in resource utilization.
Innovation Solution
A method for dynamically assigning tasks to access points and network controllers based on preference scores and load thresholds, using a combination of greedy and 0-1 Multi-constrained Knapsack approaches to optimize task distribution and alleviate over-utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If tasks are statically assigned to access points and network controllers, then device complexity is reduced and operation is simplified, but network efficiency deteriorates due to bottlenecks and poor resource utilization
Solution Approach 1:
The patent implements dynamic task assignment by continuously monitoring device load conditions and reassigning tasks based on real-time preferences. The system transitions from static to dynamic task distribution, allowing access points and network controllers to exchange tasks according to changing network conditions, thereby improving productivity without excessive complexity
Solution Approach 2:
The system changes assignment parameters based on load conditions and task preferences. By adjusting assignment decisions according to monitored parameters (load thresholds, preference scores), the system optimizes resource utilization while maintaining manageable complexity through automated parameter-based decision making
2Quantity of substance
If more access points and network controllers are deployed to handle clients in various areas, then network coverage and capacity are improved, but device complexity and management overhead increase
Solution Approach 1:
The patent enables self-service through automated task exchange between access points and network controllers. Devices autonomously monitor their own load conditions and initiate task transfers without centralized intervention, reducing management overhead while scaling the number of devices in the network
Solution Approach 2:
The system implements feedback mechanisms where devices continuously report load conditions and preferences. This feedback loop enables automatic adjustment of task assignments as devices are deployed or removed, maintaining optimal performance without increasing management complexity
3Productivity
If tasks are reassigned dynamically based on load conditions, then resource utilization is optimized, but system stability may be affected by frequent changes
Solution Approach 1:
The system uses feedback from load monitoring to trigger task reassignments only when necessary. By basing changes on actual load condition feedback rather than continuous adjustment, the system optimizes resource utilization while maintaining stability through condition-based decision making
Solution Approach 2:
The patent applies dynamics by enabling flexible task assignment that adapts to changing conditions. The system dynamically adjusts assignments in response to load thresholds and preferences while maintaining stability through structured reassignment protocols that prevent excessive volatility
Data Source
AI summary
A method is described for dynamically assigning tasks to entities of different types within a network system based on preferences to perform the tasks on particular entities and/or network/device conditions. This ability to dynamically assign processing of tasks between disparate devices in a network system provides a more efficient network configuration and utilization of resources while not compromising throughput, overall network security, and/or network flexibility.