Radio-Aware Task Allocation Using Edge Network Topology
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Solution Overview
Problem
Existing systems face challenges in efficiently allocating tasks to network assets, such as unmanned aerial vehicles (UAVs) and car fleets, due to the complexity of managing network coordination and the time-consuming nature of human operator intervention.
Innovation Solution
A method and system that utilize network topology information to divide high-level tasks into low-level tasks, assign them to worker nodes based on a rule engine and task engine, considering the network's topology, capabilities, and dynamic conditions, enabling smart data routing and priority slice integration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human operators manually allocate tasks to network assets, then task allocation can be performed with full contextual understanding, but the process becomes time-consuming and difficult to scale
Solution Approach 1:
The system enables automated self-service task allocation where the network management system automatically divides high-level tasks into low-level tasks, evaluates worker node capabilities using rule engines, and assigns tasks without human intervention. This resolves the contradiction by eliminating manual operation time while maintaining allocation quality through systematic evaluation criteria.
Solution Approach 2:
The patent replaces the mechanical human operator decision-making process with an automated rule engine and task engine that evaluate network topology information, worker node capabilities, and task requirements. This substitution eliminates human time constraints while maintaining allocation accuracy through systematic rule-based evaluation.
2Productivity
If the system considers comprehensive network topology information for task allocation, then task distribution optimality is improved, but system complexity increases
Solution Approach 1:
The patent segments the complex task allocation problem into distinct functional components: a rule engine that evaluates worker node suitability based on network topology and capabilities, and a task engine that performs the actual task division and assignment. This segmentation manages complexity by organizing the system into modular, specialized subsystems that can be independently developed and maintained.
Solution Approach 2:
The rule engine acts as an intermediary between the raw network topology information and the task assignment decisions. It processes and evaluates multiple factors (network topology, worker node capabilities, task requirements) and produces structured recommendations that the task engine then implements, thereby managing complexity through layered processing.
3Reliability
If the system dynamically adjusts task allocation based on radio health and topology changes, then system availability is improved, but computational overhead increases
Solution Approach 1:
The system implements periodic monitoring and evaluation of network conditions, where the rule engine and task engine are triggered by specific events (task arrivals, topology changes, worker node status changes) rather than continuously. This event-driven periodic action maintains system availability through dynamic adaptation while reducing computational overhead by activating processing only when necessary.
Data Source
AI summary
A system includes a master node and at least one worker node connected over a network. The master node includes a stack executable on a node within a cloudlet within the system. The stack includes a rule engine that includes rules and facts. The rule engine is accessible via an application programming interface (API) from plugins. The rule engine includes rules and facts. The rule engine also has access to network topology information pulled from the network. The rule engine uses the network topology information in allocating low-level tasks for a high-level task to worker nodes. The network topology information optimizes the performance of tasks within the system.