Dynamic Inference Model Reassignment for Distributed Availability
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Solution Overview
Problem
Existing systems face challenges in efficiently managing inference models across distributed environments due to resource constraints and adverse conditions, leading to potential termination and diminished functionality of data processing systems, which can disrupt the continuity and compliance of inference generation.
Innovation Solution
The system partitions inference models into portions and distributes them across multiple data processing systems, employing a management framework that dynamically reassigns and redistributes these portions based on an execution plan to ensure compliance with downstream consumer needs, even in the face of system failures or changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If inference models are distributed across multiple data processing systems, then system availability and continuity are improved, but device complexity increases
Solution Approach 1:
The inference model is divided into multiple portions that can be independently distributed across different data processing systems. Each portion can be executed separately, allowing the system to maintain functionality even if some systems fail, thus improving reliability while managing complexity through modular decomposition.
Solution Approach 2:
Multiple data processing systems are equipped with the capability to execute inference model portions, creating a universal platform where any system can potentially host any model portion. This multi-functionality approach improves system availability by allowing flexible redistribution while the standardized interface manages the complexity.
2Reliability
If inference model portions are redundantly deployed, then reliability is improved, but resource consumption increases
Solution Approach 1:
Instead of fully redundant deployment of all model portions on all systems, the patent deploys only the necessary portions strategically. Systems host only the specific inference model portions they need to execute, avoiding unnecessary resource consumption while maintaining sufficient reliability through targeted redundancy of critical portions.
Solution Approach 2:
Different data processing systems are assigned different portions of the inference model based on their local capabilities, resource availability, and operational requirements. This local quality approach ensures that redundancy is applied where most needed while conserving resources on systems with different operational contexts.
3Adaptability or versatility
If dynamic reassignment is implemented, then adaptability is improved, but system complexity increases
Solution Approach 1:
The system implements dynamic reassignment of inference model portions to data processing systems based on changing operational conditions, resource availability, and failure states. This dynamic approach improves adaptability by allowing the system to respond to various scenarios while the automated management framework handles the complexity of coordination and redistribution.
Solution Approach 2:
The system continuously monitors the state of data processing systems and adjusts the distribution of inference model portions accordingly. This feedback mechanism enables automatic adaptation to failures and changing conditions, improving versatility while the automated feedback loop manages the complexity of dynamic reassignment without requiring manual intervention.
Data Source
AI summary
Methods and systems for inference generation are disclosed. To manage inference generation, a system may include an inference model manager and any number of data processing systems. The inference model manager may partition an inference model into portions. Portions of the inference model may be distributed to data processing systems in accordance with an execution plan. The execution plan may include instructions for timely execution of the inference model with respect to the needs of a downstream consumer. The inference model manager may manage execution of the inference model by monitoring the functionality of the data processing systems and dynamically re-assigning and/or re-locating data processing systems in the event that one or more data processing systems becomes unable to execute a portion of the inference model.