Micro-Processing Groups for Fault-Tolerant Distributed ML
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Solution Overview
Problem
Current distributed machine learning platforms fail to handle node errors gracefully, leading to task failures and significant downtime during inference tasks, which negatively impact performance and user satisfaction.
Innovation Solution
Implementing micro-processing groups (micro-worlds) within a distributed machine learning system, allowing dynamic addition and removal of nodes without stopping the training or inference task, using micro-group managers to manage these groups.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If an all-or-nothing approach is used for distributed machine learning tasks, then system simplicity is maintained, but system reliability deteriorates when node errors occur
Solution Approach 1:
The patent divides the distributed machine learning system into multiple independent processing groups instead of using a single all-or-nothing system. Each processing group can operate independently, so when one group fails due to node errors, other groups continue to function. This segmentation isolates failures and prevents system-wide collapse, thereby improving reliability without requiring complete system redesign.
2Reliability
If the system is reconfigured when nodes fail, then system reliability is maintained, but task completion time increases significantly
Solution Approach 1:
The patent pre-configures multiple processing groups before the distributed machine learning task begins. These processing groups are prepared in advance with redundant node assignments, so when node failures occur during task execution, the system can immediately switch to pre-prepared alternative groups without requiring time-consuming reconfiguration. This preliminary preparation eliminates downtime and maintains task completion schedules.
3Adaptability or versatility
If micro-processing groups are implemented for dynamic node adjustment, then system adaptability improves, but device complexity increases
Solution Approach 1:
The patent implements micro-processing groups that can dynamically adjust their composition during task execution. Nodes can be added to or removed from processing groups based on real-time system conditions, node availability, and task requirements. This dynamic structure allows the system to adapt to changing conditions without requiring complete reconfiguration, balancing adaptability gains with manageable complexity through automated group management.
Data Source
AI summary
In one implementation, a device maintains a set of processing groups of which the device is a member in a distributed machine learning system. The device performs a machine learning task with respect to a portion of a machine learning model distributed across the distributed machine learning system. The device receives an indication of a change in the distributed machine learning system. The device adjusts, based on the indication, the set of processing groups of which the device is a member in the distributed machine learning system.