Inference Service Group Allocation Using Flow Weights
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Solution Overview
Problem
Existing artificial intelligence inference services face challenges in efficiently managing multiple versions of models across different service groups, leading to high user workload and low efficiency due to complex task allocation and system complexity.
Innovation Solution
A layered inference service platform structure is introduced, comprising a service layer, version layer, and module layer, with flow weights determining task distribution among different service groups, and direct communication between modules to perform subtasks, reducing the need for central scheduling and interface adaptation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple versions of inference service groups are deployed to handle diverse service applications, then the adaptability and optimization capability of AI models are improved, but the system complexity and task allocation difficulty increase
Solution Approach 1:
The inference service platform is segmented into multiple independent inference service groups, each handling specific service scenarios. This segmentation allows different AI model versions to be deployed in isolated groups, improving adaptability to diverse applications while managing complexity through modular organization rather than monolithic system design.
Solution Approach 2:
The system implements dynamic task allocation where the allocation proportion of inference tasks to different inference service groups can be adjusted in real-time based on service requirements and model performance. This dynamic mechanism allows the system to adapt to changing demands without requiring complex static configuration, resolving the contradiction between versatility and complexity.
2Ease of operation
If manual task allocation is used across different inference service groups, then flexibility in task distribution is achieved, but user workload increases and efficiency decreases
Solution Approach 1:
The system implements automated task allocation that performs self-service by automatically distributing inference tasks across different inference service groups based on pre-configured allocation proportions. This eliminates manual intervention, reducing user workload while maintaining flexible task distribution through programmable allocation rules, thus improving both ease of operation and productivity.
Solution Approach 2:
The system uses allocation proportion parameters to control task distribution automatically. By changing these parameters, the system can flexibly adjust task allocation without manual reconfiguration, achieving both operational flexibility and high efficiency through parameter-driven automated decision-making rather than manual task assignment.
3Productivity
If centralized scheduling is implemented to manage task allocation, then task distribution control is improved, but system complexity and transformation costs increase
Solution Approach 1:
The patent extracts the complex centralized scheduling function and replaces it with a simplified allocation mechanism based on pre-configured proportions. Instead of implementing a full-featured centralized scheduler, the system takes out only the essential task distribution function and implements it through straightforward proportional allocation, reducing scheduling system complexity while maintaining effective task distribution control.
Solution Approach 2:
The system controls task distribution by changing allocation proportion parameters rather than through complex scheduling algorithms. This parameter-based approach simplifies the control mechanism while maintaining effective task distribution, avoiding the need for elaborate centralized scheduling systems and reducing transformation costs associated with implementing complex scheduling infrastructure.
4Adaptability or versatility
If interface adaptation layers are added to enable communication between different service groups, then system interoperability is improved, but device complexity and transformation costs increase
Solution Approach 1:
The system implements a unified task allocation mechanism that serves multiple inference service groups with different AI model versions through a common interface. This universal allocation framework enables interoperability between diverse service groups without requiring separate adaptation layers for each group, reducing interface complexity while maintaining system versatility through a single multi-functional allocation system.
Data Source
AI summary
A method for running an inference service platform, includes: determining inference tasks to be allocated for the inference service platform, in which the inference service platform includes two or more inference service groups, versions of the inference service groups are different, and the inference service groups are configured to perform a same type of inference services; determining a flow weight of each of the inference service groups, in which the flow weight is configured to indicate a proportion of a number of inference tasks to which the corresponding inference service group need to be allocated in a total number of inference tasks; and allocating the corresponding number of inference tasks in the inference tasks to be allocated to each of the inference service groups based on the flow weight of each of the inference service groups; and performing the inference tasks by the inference service group.