Deep Learning Optimization Framework for Heterogeneous Distributed Systems
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Solution Overview
Problem
Distributed systems face inefficiencies in resource utilization and privacy protection, particularly in hierarchical highly heterogeneous systems, where network delays and hardware limitations impact performance and user data security.
Innovation Solution
A deep learning application optimization framework that includes a running preparation stage for training deep neural networks tailored to each node's characteristics and a data encryption module for privacy protection, with a task scheduling algorithm to optimize resource usage and reduce response time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If deep learning applications are deployed to hierarchical highly heterogeneous distributed systems, then resource utilization efficiency is improved, but system complexity increases
Solution Approach 1:
The system segments computing tasks into different types (training tasks and inference tasks) and assigns them to different node types accordingly. Training tasks are directed to cloud nodes with strong computing power, while inference tasks are distributed to fog and terminal nodes closer to users. This segmentation resolves the contradiction by organizing the heterogeneous system into manageable functional segments that optimize resource utilization without overwhelming system complexity.
Solution Approach 2:
The patent implements dynamic task scheduling that adapts to real-time system conditions. The scheduling algorithm dynamically selects appropriate nodes based on current resource availability, task requirements, and network conditions. This dynamic approach allows the system to efficiently utilize available resources across the heterogeneous architecture while managing complexity through adaptive rather than static resource allocation.
2Power
If cloud nodes are used for computing, then centralized computing capability is improved, but network delay increases
Solution Approach 1:
The patent segments the computing architecture into multiple layers: cloud nodes for centralized training, fog nodes for regional processing, and terminal nodes for local inference. This segmentation allows heavy training computations to remain centralized at cloud nodes while inference tasks are pushed to lower layers closer to users, thereby maintaining centralized computing capability for training while reducing network delay for inference operations.
Solution Approach 2:
Fog nodes serve as intermediaries between cloud nodes and terminal nodes. They receive trained models from cloud nodes and distribute them to terminal nodes for local inference, or handle inference tasks locally when appropriate. This intermediary layer reduces the need for direct communication between terminal nodes and distant cloud nodes, thereby reducing network delay while maintaining access to centralized computing resources.
3Loss of time
If fog nodes are used for computing, then network delay is reduced, but computing capability resources are limited
Solution Approach 1:
The patent merges the capabilities of different node types into a unified hierarchical system. Fog nodes are combined with terminal nodes and cloud nodes to form an integrated computing architecture. When fog nodes encounter tasks exceeding their local computing capabilities, the system automatically routes these tasks to cloud nodes for processing. This merging allows the system to achieve low network delay through local fog node processing while maintaining access to strong computing capabilities via cloud nodes when needed.
4Loss of time
If terminal nodes are used for computing, then network delay is minimized, but hardware resource limitations increase
Solution Approach 1:
Instead of requiring terminal nodes to have sufficient hardware resources to handle all computations independently, the patent inverts the approach by having terminal nodes offload computationally intensive tasks to fog and cloud nodes. Terminal nodes focus on lightweight preprocessing and final inference, while heavier computations are performed remotely. This inversion allows terminal nodes to achieve minimal network delay for local operations while accessing greater hardware resources through the network when needed.
5Power
If user data is transmitted to cloud computing centers, then computing power is improved, but privacy protection deteriorates
Solution Approach 1:
The patent applies local quality by allowing different processing locations based on task type and data sensitivity. Sensitive user data can be processed locally at terminal or fog nodes using lightweight models, while non-sensitive data or heavy computations are processed at cloud nodes. This localized processing approach maintains privacy protection for sensitive data by keeping it local, while still utilizing cloud computing power when appropriate and secure.
Solution Approach 2:
Fog nodes act as intermediaries that can process user data locally or relay it to cloud nodes with appropriate security measures. For sensitive data, fog nodes perform processing locally without transmitting to distant cloud centers, thereby maintaining privacy protection. For non-sensitive data, fog nodes can efficiently transmit to cloud nodes for heavy computation. This intermediary approach balances computing power access with privacy protection based on data characteristics.
Data Source
AI summary
The present invention discloses a hierarchical highly heterogeneous distributed system based deep learning application optimization framework and relates to the field of deep learning in the direction of computational science. The hierarchical highly heterogeneous distributed system based deep learning application optimization framework comprises a running preparation stage and a running stage. The running preparation stage is used for performing deep neural network training. The running stage performs task assignment to all kinds of devices in the distributed system and uses a data encryption module to perform privacy protection to user sensitive data. Due to heterogeneous characteristics of a system task of the present invention, on the premise that the overall performance is guaranteed, the system response time is reduced, the user experience is guaranteed, the data encryption module based on the neural network can perform privacy protection to user sensitive data at a lower computing cost and storage cost, and the user data security is guaranteed.