Topology-Based Task Allocation for Heterogeneous Edge Networks
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Solution Overview
Problem
Existing systems face challenges in efficiently allocating tasks to network assets, particularly in edge networks, due to the complexity of managing heterogeneous devices and the time-consuming nature of manual coordination, which hinders task completion.
Innovation Solution
A method and system that utilize network topology information to allocate tasks by dividing high-level tasks into low-level tasks, using a rule engine to generate lists of worker nodes based on network facts and rules, and a task engine to assign tasks according to topology, incorporating a smart data routing mechanism for enhanced decision-making.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual coordination is used to allocate tasks to network assets, then task allocation can be performed with simple systems, but task completion becomes time-consuming and inefficient
Solution Approach 1:
The system performs preliminary actions by pre-establishing the task allocation framework with master nodes, worker nodes, and communication channels before tasks need to be executed. This allows the system to quickly respond to task allocation requests without manual coordination, improving productivity while reducing time loss through automated preliminary setup
Solution Approach 2:
The patent introduces intermediary components including master nodes that act as mediators between task sources and worker nodes. These intermediaries automate the coordination process, eliminating manual intervention and significantly reducing the time required for task allocation while maintaining efficient productivity
2Adaptability or versatility
If heterogeneous devices are managed in edge networks, then system versatility is improved, but device complexity and management difficulty increase
Solution Approach 1:
The system implements universality by creating a standardized task allocation framework that can manage diverse heterogeneous devices through common protocols and interfaces. Master nodes and worker nodes serve multiple functions including task reception, processing, coordination, and communication, allowing the system to handle various device types without increasing management complexity
Solution Approach 2:
The patent segments the network into hierarchical components with master nodes overseeing multiple worker nodes. This segmentation allows heterogeneous devices to be organized into manageable groups, reducing overall system complexity while maintaining versatility through the ability to add different types of worker nodes under unified master node control
3Productivity
If automated task allocation systems are implemented, then task completion efficiency is improved, but system complexity increases
Solution Approach 1:
The automated task allocation system is segmented into distinct functional components: master nodes for task distribution, worker nodes for execution, and communication channels for coordination. This segmentation reduces perceived complexity by organizing automation into manageable, specialized units that can operate independently yet cooperatively
Solution Approach 2:
Master nodes serve as intermediary components that simplify the automation process by handling task distribution, monitoring, and coordination functions. These intermediaries shield the complexity of automated allocation from individual worker nodes, allowing efficient automated task completion while managing system complexity through layered architecture
Data Source
Figure 1A~1B
Figure 2
Figure 3A
AI summary
A system includes a master node (102a) and at least one worker node (102b) connected over a network. The master node (102a) includes a stack executable on a node within a cloudlet (101) 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.