AI BriefingServing Fairness
Key point
Cohere introduced an LLM serving fairness solution that prevents a specific user's traffic spikes from affecting other users in multi-tenant environments.
Details
When operating LLMs on a multi-tenant SaaS platform, a sudden traffic spike from a specific customer can cause the Noisy Neighbor problem, increasing latency for other customers. To solve this, Cohere introduced a new solution that combines architectural patterns with scheduling algorithms.
Cohere's solution operates through four mechanisms working hierarchically, with each stage as follows:
- Rate Limiter: Before a request enters the queue, it limits the number of requests per minute/month for each endpoint to prevent system overload. When latency targets cannot be met, requests are rejected early to maintain predictable performance.
- Performance Tier: Determines the priority of compute resources according to the SLA (Service Level Agreement). Customers on higher-paying tiers are granted faster queue processing rights.
- Deficit Round Robin (DRR): The core algorithm for fairly distributing resources within the same tier. It allocates a work budget called a Quantum to each tenant and processes tasks in turn, preventing any specific tenant from monopolizing GPU resources.
This hierarchical approach maintains batching efficiency while ensuring that each tenant is allocated fair compute resources within their own priority and deadlines.
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