IoT Edge Computing Resource Scheduling for Latency Reduction
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Solution Overview
Problem
In IoT systems, conventional centralized computing leads to high data transmission latency, especially in industrial scenarios, where efficient management of computing resources at edge devices is crucial for improving processing efficiency and reducing latency.
Innovation Solution
A computing resource scheduling method that determines processing priority based on data fluctuation amplitude and predicts required resources, scheduling edge computing devices accordingly to optimize resource allocation and reduce latency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If centralized computing is used in IoT systems, then data processing can be performed with sufficient computing resources, but data transmission latency increases significantly
Solution Approach 1:
The patent divides the centralized computing system into distributed edge computing nodes. Data processing tasks are segmented and assigned to multiple edge devices located closer to data sources, reducing transmission distance and latency while maintaining processing capability through distributed architecture
Solution Approach 2:
The patent introduces spatial dimension by deploying computing resources at edge locations rather than concentrating them in a single cloud center. This dimensional shift from centralized to distributed architecture reduces the physical distance data must travel, thereby reducing latency
2Ease of operation
If computing resources are allocated to all data equally, then resource allocation is simple, but high-priority data with large fluctuations cannot be processed timely
Solution Approach 1:
The patent implements differentiated resource allocation where different computing resources are allocated based on local data characteristics. High-priority data with large fluctuations receives more computing resources, while stable data receives fewer resources, optimizing both timeliness and resource efficiency through quality-based differentiation
Solution Approach 2:
The patent dynamically changes resource allocation parameters based on data characteristics such as fluctuation amplitude and priority levels. The system adjusts computing resource distribution in real-time according to varying data requirements, improving processing timeliness for critical data while maintaining operational efficiency
3Ease of operation
If computing resources are allocated in advance without prediction, then resource allocation is straightforward, but resource utilization efficiency decreases
Solution Approach 1:
The patent performs preliminary prediction of data characteristics and resource requirements before actual data arrives. By predicting fluctuation patterns and priority levels in advance, the system pre-allocates appropriate computing resources, avoiding both over-provisioning and under-provisioning, thereby improving resource utilization efficiency
Data Source
AI summary
Various embodiments of the teachings herein include a resource scheduling method comprising: receiving data to be processed collected by a sensor in an Internet of Things system; determining a processing priority of the data to be processed; predicting, according to the determined processing priority, a computing resource amount and duration required for processing the data to be processed; and scheduling a computing resource of an edge computing device in the IoT system according to the predicted computing resource amount and duration to process the data to be processed.