AI Model State Map Load Balancing Across Regions
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Solution Overview
Problem
Network services utilizing AI models face inefficiencies and service failures due to varying network traffic and load, which can be exacerbated by differences in AI model updates and varying client request volumes, leading to potential overburdening and decreased user experience.
Innovation Solution
A state map-based approach for AI model instance load balancing that tracks AI model instances across regions, prioritizes proximity, and dynamically updates based on instance status and model-specific requirements, ensuring efficient distribution of client requests.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If AI model instances are distributed across various regions to improve service quality, then client device service efficiency is improved, but system complexity increases due to tracking and managing instances across multiple regions
Solution Approach 1:
The patent introduces a load balancer as an intermediary component that manages the complexity of distributing and tracking AI model instances across multiple regions. The load balancer maintains a state map of instance locations and handles the routing logic, isolating the complexity from the rest of the system while enabling efficient multi-region service delivery.
Solution Approach 2:
The system segments the AI model instances across different geographical regions, allowing local servicing of client devices. Each region maintains its own instances, and the load balancer segments the management task by maintaining separate state information for each region in the state map, making the overall system more manageable.
2Reliability
If multiple AI model instances are deployed across regions to handle varying traffic, then service availability is improved, but load monitoring and instance selection complexity increases
Solution Approach 1:
The load balancer implements feedback mechanisms by continuously monitoring the load state of each AI model instance and updating the state map accordingly. This feedback loop enables dynamic adjustment of instance selection based on current load conditions, maintaining service availability while automating the monitoring complexity.
Solution Approach 2:
The state map automatically tracks and updates the status of each AI model instance without requiring manual intervention. The system self-manages the complexity of load monitoring by maintaining real-time state information, allowing the load balancer to make intelligent routing decisions based on current conditions.
3Adaptability or versatility
If AI model instances are updated differently across regions, then local optimization is improved, but determining which instance to use becomes more complex
Solution Approach 1:
The patent allows each region to have AI model instances with locally optimized characteristics and updates. The state map captures the specific properties of each instance, enabling the load balancer to select instances based on both location and instance-specific qualities, thus maintaining local optimization while managing selection complexity.
4Reliability
If the system tracks the state of each AI model instance to prevent overburdening, then service reliability is improved, but computational overhead increases
Solution Approach 1:
The load balancer implements partial monitoring by tracking only the essential state information needed for load balancing decisions (such as current load level and availability status) rather than comprehensively monitoring all possible instance parameters. This selective tracking maintains service reliability while reducing unnecessary computational overhead.
Data Source
AI summary
Disclosed herein are a system, a method and a device for providing a state map based model instance load balancing. A server can receive a request from a device in a region to access an instance of an AI model of a plurality of AI models deployed across regions. The server can maintain an AI model map of AI models based at least on the type of AI model. The server can identify, based at least on the request, the region of the request and the type of AI model requested. The server can determine, using the AI model map, the instance of the type of AI model deployed in the region from the plurality of AI models deployed in the region. The server can provide, based at least on the determination, a response to the request providing access to the instance of the type of AI model.