Edge Resource Management for Real-Time Analytics
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
In IoT systems, edge devices face resource constraints such as limited CPU performance, memory, and storage, which hinder real-time analytics processing, leading to challenges in managing multiple real-time analysis processes and ensuring sufficient resources for efficient data processing.
Innovation Solution
A computer system and method that determines priority for real-time analysis processes and applies resource adjustments, such as memory adjustments, to selected processes based on priority, optimizing resource utilization and managing relationships between multiple real-time analysis processes and applications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If multiple real-time analysis processes are run simultaneously on edge devices, then analytics capability is improved, but resource consumption increases beyond available limits
Solution Approach 1:
The system dynamically adjusts memory allocation for real-time analysis processes based on their priority levels and current resource availability. High-priority processes receive guaranteed memory resources, while low-priority processes have their memory allocation reduced or suspended when resources are scarce, allowing multiple processes to run simultaneously without exceeding device memory limits
Solution Approach 2:
The system changes memory allocation parameters for different analysis processes based on priority. By adjusting the memory parameter dynamically according to process priority and resource constraints, the system enables multiple high-value analytics processes to run on resource-constrained edge devices without overwhelming the available memory
2Productivity
If resource allocation is increased for real-time analysis processes, then processing efficiency is improved, but resource constraints at edge devices are exceeded
Solution Approach 1:
The resource management system operates autonomously by automatically determining process priorities, allocating memory resources, and adjusting allocations in real-time based on system state. This self-service mechanism eliminates the need for complex manual resource management while maintaining high processing efficiency on constrained edge devices
Solution Approach 2:
The system dynamically changes resource allocation parameters based on process priority and available resources. By automatically adjusting memory allocation parameters without manual intervention, the system maintains high processing efficiency while adapting to device constraints, reducing the complexity of resource management
3Reliability
If memory allocation is reduced for low-priority processes, then resource availability for high-priority processes is improved, but overall system throughput decreases
Solution Approach 1:
The system dynamically adjusts memory allocation based on real-time resource availability and process priorities. When resources are abundant, low-priority processes receive more memory to maintain throughput. When resources are constrained, allocation shifts to high-priority processes while maintaining their reliability, thus balancing both concerns through dynamic adaptation
Data Source
AI summary
Example implementations described herein are directed to systems and methods for managing a relationship between real-time analysis processes and applications, where each of the applications are configured to utilize output from one or more of the corresponding real-time analysis processes. In an example implementation, resource adjustment is applied to the real-time analysis process based on a determined priority.