Capacity-Based Load Balancing for AI Model Endpoints
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Solution Overview
Problem
Transformer models consume significant memory and GPU resources, leading to inefficiencies in resource management and creating challenges with regional allocation of compute capacity, resulting in 'fragmented capacity' and uneven workload distribution across model endpoints.
Innovation Solution
A capacity-based load-balancing method that determines net resource consumption for workloads and allocates tasks across multiple model endpoints based on available resource capacity, ensuring equal saturation and efficient utilization of compute resources globally.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If transformer models are deployed across multiple model endpoints to serve cloud-based AI services, then service availability and scalability are improved, but resource capacity becomes fragmented and unevenly distributed across endpoints
Solution Approach 1:
The system dynamically determines the distribution of available resource capacity across model endpoints in real-time, rather than using static allocation. This allows the load balancer to adapt to changing resource availability and workload characteristics, optimizing task allocation as conditions change
Solution Approach 2:
The system changes the parameter of resource capacity distribution by calculating net resource consumption for each task based on model characteristics, input parameters, and workload size. This enables flexible adaptation of resource allocation to match actual consumption patterns rather than using fixed allocations
2Reliability
If workloads are distributed across multiple model endpoints, then service reliability is improved, but resource utilization becomes inefficient with unused capacity at some endpoints and overload at others
Solution Approach 1:
The system implements feedback by continuously determining the distribution of available resource capacity across model endpoints and using this information to adjust task allocation. The load balancer receives feedback on resource consumption and capacity availability, then optimizes subsequent task assignments to achieve equal saturation across endpoints
Solution Approach 2:
The system performs preliminary determination of net resource consumption for tasks before allocation. By calculating expected resource consumption in advance based on model characteristics and workload properties, the system can proactively balance the load to achieve equal saturation across endpoints rather than reacting to overload conditions
3Device complexity
If tasks are allocated based on equal distribution across model endpoints, then load balancing is simplified, but net resource consumption varies causing uneven saturation and wasted capacity
Solution Approach 1:
The system applies local quality by determining net resource consumption specific to each task based on its characteristics, the target model, and input parameters. Instead of uniform allocation, each task receives allocation tailored to its specific resource needs, optimizing overall resource utilization while maintaining manageable complexity through automated calculations
Data Source
AI summary
A system provides capacity-based load balancing across model endpoints of a cloud-based artificial intelligence (AI) model. The system includes a consumption determination engine executable to determine a net resource consumption for processing tasks in a workload generated by a client application for input to the trained machine learning model. The system also includes a load balancer that determines a distribution of available resource capacity in a shared resource pool comprising compute resources at each of the multiple model endpoints. The load balancer allocates parallelizable tasks of the workload among the compute resources at the multiple model endpoints based on the net resource consumption of the tasks and on the distribution of available resource capacity in the shared resource pool.