Inference Model Manager Bottleneck Prevention
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Solution Overview
Problem
Inference model bottlenecks in distributed environments can lead to unreliable inference generation due to the failure of data processing systems hosting multiple redundant instances of the inference model, resulting in decreased reliability and potential timely execution issues.
Innovation Solution
An inference model manager monitors the risk of unsuccessful execution and proactively re-deploys the inference model to ensure each data processing system hosts only one redundant copy, using deployment and execution plans to prevent bottlenecks and ensure continued inference generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If multiple redundant instances of the inference model are hosted by a single data processing system, then resource utilization is improved, but system reliability deteriorates due to bottleneck formation and single point of failure
Solution Approach 1:
The patent segments the inference model instances across multiple data processing systems rather than concentrating them on a single system. Each system hosts a portion of the redundant instances, distributing the computational load and eliminating the bottleneck. This segmentation maintains resource utilization efficiency while improving reliability through geographic and architectural distribution.
Solution Approach 2:
The patent transitions from a single-dimension concentration model (all instances on one system) to a multi-dimensional distribution model across the data center infrastructure. By utilizing multiple dimensions of the computing environment (different systems, locations, or clusters), the patent achieves both efficient resource utilization and enhanced reliability through diversified placement.
2Reliability
If redundant instances of the inference model are distributed across multiple data processing systems, then system reliability is improved, but device complexity increases due to coordination and management overhead
Solution Approach 1:
The patent implements a universal management approach where a single coordination mechanism handles multiple functions: monitoring instance distribution, detecting bottlenecks, initiating re-deployment, and managing failover. This multi-functional system reduces the need for separate specialized components for each management task, thereby limiting the increase in overall device complexity while achieving improved reliability.
Solution Approach 2:
The system incorporates self-service capabilities through automated monitoring and self-healing mechanisms. The coordination mechanism automatically detects when instances are concentrated on a single system and triggers re-deployment without human intervention. This automation reduces operational complexity by eliminating manual management tasks and enabling the system to self-correct distribution imbalances.
3Reliability
If the inference model is re-deployed to prevent bottlenecks, then inference generation reliability is improved, but execution time increases due to re-deployment operations
Solution Approach 1:
The patent implements preliminary monitoring and proactive re-deployment strategies. The system continuously monitors instance distribution and identifies potential bottleneck conditions before they critically impact inference generation. By initiating re-deployment actions in advance, during low-utilization periods or between inference requests, the patent prevents bottlenecks from forming while minimizing the time loss associated with re-deployment operations.
Solution Approach 2:
The system employs periodic monitoring and scheduled re-deployment operations. Instead of continuous active re-deployment that would constantly interrupt inference generation, the patent uses periodic checks of instance distribution and schedules re-deployment operations at appropriate intervals. This periodic approach maintains reliability by regularly correcting distribution imbalances while minimizing execution time loss by concentrating re-deployment activities in controlled time windows.
Data Source
AI summary
Methods and systems for managing execution of an inference model hosted by data processing systems are disclosed. To manage execution of the inference model hosted by the data processing systems, a system may include an inference model manager and any number of data processing systems. The inference model manager may monitor the risk of unsuccessful execution of the inference model by the data processing systems and may proactively take action to support inference generation in the event of reduced functionality of one or more of the data processing systems. The inference model manager may distribute multiple redundant instances of the inference model so that each data processing system only hosts one instance of the inference model. Inference model manager may also obtain an execution plan to respond to a failure of one or more data processing systems to ensure no inference model bottlenecks occur during re-deployment of the inference model.