Distributed Machine Learning Framework for Heterogeneous Data Platforms
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Solution Overview
Problem
Existing machine learning technologies face challenges in efficiently accessing and processing large-scale data across heterogeneous data platforms without moving data to a single platform, which is resource-intensive and inefficient.
Innovation Solution
A distributed machine learning framework is implemented, comprising a configuration component, a persistency component, a proxy repository, and a machine learning execution component, which allows for communication and data aggregation across different data platforms, enabling computation and model training without data relocation. This framework includes a bottom work node layer for computations, a middle server node layer for partial result aggregation and parameter updates, and an upper central server node layer for coordinating jobs and convergence determination across platforms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data is moved to a single data platform for machine learning processing, then data access and model training can be performed, but resource consumption increases significantly
Solution Approach 1:
The system segments the machine learning workload across multiple data platforms rather than consolidating all data to a single platform. Each data platform processes local data independently, dividing the overall computational task into distributed segments that can be executed in parallel, thereby reducing the resource burden on any single platform while maintaining overall processing capability.
Solution Approach 2:
The system introduces intermediary components including a distributed file system layer and coordination services that enable different data platforms to communicate and share model parameters without requiring data movement. These intermediaries facilitate collaborative training by transmitting only essential model updates rather than raw data, significantly reducing resource consumption.
2Ease of operation
If data is moved to a single data platform, then centralized machine learning can be performed, but the process becomes resource-consuming and inefficient
Solution Approach 1:
Instead of moving data to a centralized platform for processing, the system inverts the approach by bringing computation to where the data resides. Multiple data platforms perform local processing independently, and only model parameters are aggregated centrally, eliminating the time-consuming data movement process while maintaining centralized coordination.
Solution Approach 2:
The system transitions from a single-platform centralized processing model to a multi-platform distributed model, adding the dimension of spatial distribution across multiple platforms. This dimensional change enables parallel processing and eliminates data movement bottlenecks by allowing simultaneous local computations across different platforms.
3Use of energy by moving object
If a distributed framework is implemented across heterogeneous data platforms, then resource efficiency improves, but system complexity increases
Solution Approach 1:
The system implements a universal interface layer and standardized communication protocols that enable heterogeneous data platforms to interact through common mechanisms. This multi-functionality allows the same framework to work across different platform types without requiring platform-specific customizations, managing complexity while maintaining resource efficiency.
Solution Approach 2:
The system manages complexity by abstracting platform heterogeneity into configurable parameters and metadata that describe data formats, storage structures, and access patterns. By parameterizing platform-specific characteristics, the framework can adapt to different platforms without increasing structural complexity, maintaining resource efficiency through standardized configuration rather than complex architecture.
Data Source
AI summary
A distributed machine learning framework implemented with heterogeneous data platforms reduces data copying and exploits memory/computation resources of the different data platforms. A configuration component includes information to set up the system. A persistency component manages storage of data and a model trained by machine learning. A proxy repository includes predefined proxies for communication between heterogeneous data platform nodes and execution of the machine learning procedure. A machine learning execution component comprises three layers. A bottom work node layer within the data platform performs computations of the machine learning procedure. A middle server node layer comprising one server node per data platform, communicates with the work nodes to coordinate jobs on that data platform. An upper layer comprises a central server node communicating with server nodes and coordinating jobs of the different platforms. The system can extend to additional external data platforms and external machine learning libraries with predefined proxies.