Fog Node Distributed Learning Model for Metered Training
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Solution Overview
Problem
Fog nodes in fog computing are constrained devices with limited performance, making it difficult to perform optional tasks without interfering with normal operations, and relying on cloud resources for training machine learning models is inefficient as it requires significant resources and does not account for contextual data.
Innovation Solution
Fog nodes can identify spare resources during normal operations to train machine learning models locally without cloud intervention, using a system performance monitor to allocate resources for metered training, and share trained models with similar nodes to enhance training efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If fog nodes rely on cloud resources to train machine learning models, then model training can be performed, but it requires significant cloud resources and network bandwidth, and does not account for local contextual data
Solution Approach 1:
Fog nodes train machine learning models using their own local resources rather than relying on cloud resources. The system monitors spare resources at each fog node and autonomously performs model training when resources are available, eliminating the need for cloud-based training infrastructure and reducing network dependency.
Solution Approach 2:
The model training process is segmented and distributed across multiple fog nodes instead of being centralized in the cloud. Each fog node independently trains models using its own spare resources, allowing parallel training operations and reducing the burden on any single node or the cloud infrastructure.
2Adaptability or versatility
If fog nodes use spare resources for model training during normal operations, then continuous model updating is achieved, but resource allocation must be carefully managed to maintain normal fog node functionality
Solution Approach 1:
The system implements a feedback mechanism where fog nodes continuously monitor their resource usage during normal operations. When spare resources are identified (resources not needed for normal fog node functionality), the system automatically initiates model training. This feedback loop ensures that training only occurs when it will not interfere with normal operations, simplifying resource management decisions.
Solution Approach 2:
The resource allocation for model training is dynamic rather than static. The system adjusts training operations based on real-time resource availability, scaling training intensity or pausing training when resources are needed for normal fog node operations. This dynamic approach allows the system to adapt to changing operational requirements without complex pre-planning.
3Loss of energy
If fog nodes train models independently using local resources, then cloud traffic is reduced, but training efficiency may be limited by individual node resource constraints
Solution Approach 1:
The system merges the training capabilities of multiple fog nodes by having them collaboratively train the same machine learning model. Each node contributes its local spare resources to the training process, effectively combining computational power across the fog network. This approach reduces cloud traffic while achieving training speeds that exceed what any single node could provide alone.
Solution Approach 2:
A coordination mechanism acts as an intermediary between fog nodes during collaborative training. This intermediary manages resource allocation, coordinates training schedules, and aggregates results from multiple nodes, enabling efficient parallel training without requiring centralized cloud control. The intermediary optimizes the use of distributed resources to maximize training productivity.
Data Source
AI summary
The disclosed technology relates to a process for metered training of fog nodes within the fog layer. The metered training allows the fog nodes to be continually trained within the fog layer without the need for the cloud. Furthermore, the metered training allows the fog node to operate normally as the training is performed only when spare resources are available at the fog node. The disclosed technology also relates to a process of sharing better trained machine learning models of a fog node with other similar fog nodes thereby speeding up the training process for other fog nodes within the fog layer.