Definer Module Edge Task Flow Scheduling
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Solution Overview
Problem
In existing edge computing environments, the management of task flows is often bottlenecked by the edge agent's workload and unstable connections between the edge agent and edge devices, leading to potential system failures and inefficiencies.
Innovation Solution
A definer module dynamically determines a cluster of edge devices to execute task flows based on metadata information, allowing the task flow to be managed and scheduled within the edge device layer, reducing the workload on the edge agent and stabilizing data communication.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the edge agent manages task flows centrally, then task flow coordination is achieved, but the edge agent's workload increases and connection stability deteriorates
Solution Approach 1:
The patent segments the task flow management function by introducing a definer module that operates independently from the edge agent. The definer module determines device clusters and generates metadata information locally at the edge device layer, while the edge agent only needs to execute pre-defined task flows. This segmentation reduces the edge agent's workload and minimizes connection dependencies.
Solution Approach 2:
The definer module performs preliminary actions by pre-determining device clusters and generating metadata information before task flow execution. This includes pre-defining device relationships, data flow paths, and execution sequences in the metadata, so that when the edge agent receives the task flow, it can execute directly without complex real-time decisions, reducing workload and connection requirements.
2Productivity
If task flow management is centralized at the edge agent, then coordination is simplified, but system efficiency decreases due to workload bottlenecks
Solution Approach 1:
The patent divides the management functions between the definer module (handling complex cluster determination and metadata generation) and the edge agent (handling simple task flow execution). This segmentation allows the edge agent to focus solely on execution, improving productivity while avoiding workload bottlenecks.
Solution Approach 2:
The definer module creates a metadata information copy that contains all necessary execution details (device assignments, data flow paths, execution sequences). This metadata copy enables the edge agent to execute task flows independently without needing to query the definer module during runtime, improving execution efficiency while reducing the edge agent's computational burden.
3Reliability
If the edge agent manages all task flow decisions, then centralized control is maintained, but connection stability deteriorates due to frequent communications
Solution Approach 1:
The definer module performs all complex decision-making actions preliminarily by determining device clusters and generating comprehensive metadata information before task execution. This metadata includes pre-defined execution sequences and device assignments, eliminating the need for frequent real-time communications during task flow execution, thus improving both connection stability and management efficiency.
Solution Approach 2:
The edge device layer becomes self-sufficient through the definer module by generating and utilizing metadata information locally. The metadata contains all necessary execution instructions, allowing edge devices to autonomously execute task flows without continuous edge agent intervention, reducing communication frequency and improving connection stability while maintaining management efficiency.
Data Source
AI summary
The present disclosure provides a computer-implemented method, computer system and computer program product for managing a task flow. According to the computer-implemented method, a definer module may receive a request for executing a task flow. The definer module may determine a cluster of edge devices to execute the task flow from a set of edge devices. The definer module may retrieve metadata information for the task flow and edge devices in the cluster, wherein the metadata information is used to schedule the task flow in the cluster. Then the edge devices in the cluster may execute the task flow according to the metadata information.