Multi-Agent Inference Model Switching for QoS and Resource Balance
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Solution Overview
Problem
Existing inference models in computing environments face inefficiencies in resource allocation and flexibility due to limited resource availability and changing workloads, making it difficult to balance quality of service, latency, and resource consumption.
Innovation Solution
A master node determines model update information based on workload and resource capacity, distributing suggestions to inference agents for upgrading or downgrading inference models to optimize performance using a plurality of agents.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If inference models are upgraded to improve quality of service, then quality of service is improved, but resource consumption increases
Solution Approach 1:
The system dynamically adjusts the version of inference models deployed on edge devices based on real-time resource availability and service requirements. Instead of using a fixed model version, the system can switch between different model versions (e.g., from version 1.0 to 2.0) to optimize the balance between quality of service and resource consumption, allowing the system to adapt to changing conditions without being locked into a single model configuration
Solution Approach 2:
The system changes key parameters such as model version, precision (e.g., floating-point vs. integer), and architecture configuration to adjust the trade-off between quality of service and resource consumption. By modifying these parameters, the system can select appropriate model versions that meet current service requirements while staying within resource constraints, effectively resolving the contradiction between service quality and resource usage
2Adaptability or versatility
If multiple inference models are deployed to handle varying workloads, then adaptability is improved, but device complexity increases
Solution Approach 1:
The system segments the inference model portfolio into multiple versions with different characteristics (e.g., lightweight vs. high-precision models). Each model version is optimized for specific workload conditions, allowing the system to divide and conquer the complexity by managing separate, specialized models rather than one monolithic complex model. This segmentation enables selective deployment based on current needs
Solution Approach 2:
The master node and edge device system is designed to universally handle multiple inference model versions through a common framework. The system can load, manage, and execute different model versions using the same infrastructure and control mechanisms, reducing the effective complexity by providing a unified interface for managing diverse models rather than requiring separate systems for each model version
3Productivity
If model updates are performed frequently to optimize performance, then productivity is improved, but loss of time increases due to update overhead
Solution Approach 1:
The system performs preliminary actions by pre-downloading and caching multiple inference model versions in advance on edge devices. Instead of downloading models on-demand when updates are needed, the system proactively prepares model versions ahead of time during periods of low resource utilization or overnight, so that when performance optimization is needed, the models are already available for immediate deployment, minimizing update time and maximizing productivity
Data Source
AI summary
A computer-implemented method includes determining, by a master node, model update information at least based on a workload related to a task and a resource capacity of a computing environment. The model update information indicates respective model update suggestions for a plurality of inference models configured to perform the task. The method further includes distributing, by the master node, the model update information to a plurality of inference agents in the computing environment. The plurality of inference agents has a plurality of instances of the plurality of inference models executed thereon.