IoT Task Migration Between Edge Devices
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Solution Overview
Problem
IoT devices face processing inefficiencies due to limited processing power, especially when handling large data tasks, as they often rely on local processing without effective distribution strategies between IoT devices and edge devices.
Innovation Solution
Implementing a task migration strategy that divides tasks into subtasks of equal data size and migrates them between edge devices to optimize processing time, using a neural network to predict space complexity and determine the optimal offloading factor between IoT and edge devices, thereby minimizing total processing time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If tasks are processed locally at IoT devices, then processing autonomy is maintained, but processing efficiency deteriorates due to limited processing power
Solution Approach 1:
The patent segments tasks into multiple subtasks and distributes them across multiple edge devices for parallel processing. The task management server divides incoming tasks into subtasks, assigns them to different edge devices, and aggregates results, thereby improving processing efficiency while maintaining manageable system complexity through structured division of work.
Solution Approach 2:
The patent transitions from single-device sequential processing to multi-device parallel processing by adding the spatial dimension of distribution. Tasks are processed across multiple edge devices simultaneously rather than sequentially on one device, significantly improving productivity without proportionally increasing complexity due to centralized coordination.
2Productivity
If tasks are migrated to edge devices, then processing efficiency improves, but communication overhead increases
Solution Approach 1:
The patent applies partial action by migrating only specific subtasks to edge devices rather than entire tasks, and by selectively choosing which tasks to migrate based on criteria such as computational requirements and edge device availability. This optimization reduces unnecessary communication overhead while maintaining processing speed benefits for suitable tasks.
Solution Approach 2:
The patent changes the parameter of task granularity by dividing tasks into subtasks of appropriate size, and adjusts the parameter of migration decisions based on dynamic conditions such as edge device workload, network status, and task characteristics. This allows optimization of the balance between processing speed improvement and communication time consumption.
3Productivity
If tasks are divided into multiple subtasks and distributed, then processing parallelism increases, but task management complexity increases
Solution Approach 1:
The task management server performs multiple functions: task division, subtask assignment, result aggregation, and coordination. This universal coordinator handles all aspects of distributed task management, enabling parallel processing capability while containing management complexity within a single multi-functional component rather than distributing complexity across all devices.
Solution Approach 2:
The task management server acts as an intermediary between the task source and multiple edge devices. It mediates the distribution of subtasks and collection of results, simplifying the management complexity by providing a centralized coordination point that handles all interactions between tasks and edge devices rather than requiring direct peer-to-peer coordination.
4Productivity
If edge devices have higher processing power, then task handling capability improves, but device cost increases
Solution Approach 1:
The patent merges the processing capabilities of multiple edge devices to handle tasks that would require excessive resources on a single device. By distributing subtasks across multiple devices with moderate capabilities, the system achieves high task handling capability without requiring each individual device to have high-end hardware resources.
Solution Approach 2:
The patent segments large computational tasks into smaller subtasks that can be handled by edge devices with moderate processing power. This segmentation allows tasks requiring substantial computational resources to be distributed across multiple devices, avoiding the need for any single device to have excessive hardware resources while maintaining high overall task handling capability.
Data Source
AI summary
Methods and systems are disclosed for migrating tasks between edge devices in an Internet of Things (IoT) system in a manner to generally minimize the total amount of time to execute and migrate the tasks. At least one embodiment includes a computer-implemented method for receiving a task for execution at an edge device; detecting whether a task migration condition exists at the edge device; dividing the task into a plurality of subtasks of equal data size b; and migrating at least some of the subtasks of data size b to a further edge device for execution. At least one embodiment designates 1˜n subtasks of size b for execution by the edge device and n+1˜N subtasks of data size b for execution by the further edge device and optimizes the total time for execution of the subtasks is optimized as a function of n.