Wireless Network AI Task Balancing Across Unequal Node Capacities
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Solution Overview
Problem
Modern wireless communication networks face bottlenecks in processing computational AI tasks due to unequal computational capacities among nodes, necessitating a method to balance workload and prevent bottlenecks.
Innovation Solution
A load balancing method that determines load parameters for computational resources across nodes and redistributes AI tasks based on these parameters, using direct communication between nodes to allocate tasks efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If computational AI tasks are processed by individual nodes with unequal computational capacities, then each node can independently perform its assigned tasks, but bottlenecks occur in processing due to unequal capacities
Solution Approach 1:
The system implements feedback by having nodes exchange load parameter information about their computational workloads. This feedback mechanism allows the network to dynamically adjust task allocation based on current node states, preventing bottlenecks and ensuring smooth operation while maintaining high processing speed.
Solution Approach 2:
The patent applies dynamics by making the task allocation system adaptive and flexible. Instead of static assignment, nodes dynamically adjust their workload based on real-time load parameters and available computational resources, allowing the system to respond to changing conditions and avoid bottlenecks.
2Productivity
If AI tasks are allocated based on load parameters and available resources, then bottlenecks are avoided and processing efficiency is improved, but additional communication and coordination overhead is introduced
Solution Approach 1:
The patent applies universality by designing a multi-functional communication mechanism that serves both information exchange and task coordination purposes. The load parameter communication serves dual functions: monitoring network state and enabling dynamic task allocation, thereby improving efficiency without proportionally increasing communication overhead.
3Reliability
If nodes with significant headroom receive additional tasks from overloaded nodes, then workload is balanced and bottlenecks are prevented, but task reallocation complexity increases
Solution Approach 1:
The patent applies parameter changes by using load parameters as the basis for task reallocation decisions. Nodes exchange information about their computational resource utilization, and tasks are reallocated based on changes in these parameters. This approach achieves workload balance through systematic parameter monitoring and adjustment rather than complex ad-hoc reallocation processes.
Data Source
AI summary
A load balancing method of balancing computational workload in a wireless communication network is described. The wireless communication network includes at least two nodes being configured to communicate with each other. The at least two nodes include a first node and a second node. Further, a wireless communication network is described.