Rule Engine Task Allocation for Heterogeneous Edge Nodes
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Solution Overview
Problem
Military edge networks with heterogeneous devices face challenges in timely and efficient task allocation due to the complexity and scale of mission operations, surpassing human operators' capabilities.
Innovation Solution
A system comprising a control station, cloudlet, and nodes with a rule engine that utilizes policy-based mission customization and programmatic interfaces to optimize task allocation, considering platform capabilities and dynamic conditions, dividing high-level tasks into low-level tasks and assigning them to appropriate nodes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If human operators manage device coordination in military edge networks, then operational control is maintained, but the speed and scale of mission operations cannot be decided in a timely manner
Solution Approach 1:
The system enables autonomous task allocation where the rule engine automatically matches tasks to worker nodes based on capabilities and conditions without human intervention. The master node independently divides high-level tasks into low-level tasks and assigns them to appropriate worker nodes, allowing the network to self-manage coordination at scale and speed beyond human capability.
2Adaptability or versatility
If complex coordination is required between heterogeneous devices, then mission requirements are met, but the complexity of managing devices increases
Solution Approach 1:
The rule engine acts as an intermediary between task requirements and worker nodes. It maintains facts about worker node capabilities and conditions, applies rules to match tasks appropriately, and generates lists of suitable workers. This intermediary layer simplifies the complexity of coordinating heterogeneous devices by providing a standardized matching mechanism.
Solution Approach 2:
The system segments the complex coordination problem into distinct components: the master node divides high-level tasks into low-level executable tasks, the rule engine separately maintains facts and rules, and plugins handle specific task types. This segmentation allows each component to specialize in one aspect of coordination, reducing overall system complexity.
3Productivity
If high-level tasks are assigned to worker nodes, then mission objectives are achieved, but the tasks cannot be individually executed by single nodes
Solution Approach 1:
The master node automatically divides high-level tasks into multiple low-level subtasks that can be individually executed by worker nodes. This segmentation enables parallel execution across multiple nodes, improving productivity while the system manages the decomposition complexity through automated task analysis and splitting logic.
Data Source
AI summary
A stack is described. The stack includes a rule engine. The rule engine is accessed via application programming interface (API) from any plug-in mission service. The rule engine includes rules and facts. The rules and facts are loaded separately from the API and from a cloudlet monitor. The rules and facts do not require source code updates to change. The stack is executable on processors of a node within a cloudlet. The cloudlet includes multiple of the nodes. The stack divides high-level tasks into low-level tasks which are individually executable by the nodes in the cloudlet.