Ren-Queue: An Intelligent Inference Task Scheduling System for Distributed Machine Clusters

Ren-Queue is a priority-based inference task queue system designed for distributed machine learning clusters. Its core features include intelligent routing between local models and free cloud APIs, automatic rate limit tracking, and cascading degradation strategies, aiming to address cost control and resource scheduling challenges in distributed AI inference.

With the explosion of large language models and generative AI applications, cost control of inference services has become a core challenge for enterprises. Local GPU clusters are high-cost and have limited capacity, while cloud APIs are flexible but their large-scale use incurs staggering costs. Different tasks have varying requirements for model capabilities, and the lack of intelligent scheduling easily leads to resource waste or service quality degradation.

Ren-Queue provides solutions to the above challenges. Its core design concept is "intelligent routing"—automatically selecting the optimal inference backend based on task urgency, complexity requirements, and cost constraints. It supports seamless switching between locally deployed models and free cloud APIs, achieving the best balance between cost and performance.

Priority-based Task Scheduling: Supports multi-level priority queues. High-priority tasks can preempt resources, and there are priority inheritance and aging mechanisms to prevent low-priority tasks from being starved. Intelligent Routing Decision: Selects backends based on latency, cost, and model capability matching. Automatic Rate Limit Tracking: Monitors API quotas in real time to avoid over-limiting. Cascading Degradation Strategy: Automatically tries alternative solutions when the preferred backend is unavailable to ensure service availability.

Ren-Queue adopts cloud-native and microservice design: Task Queue Layer: Implemented based on Redis to ensure reliable storage and ordered processing of tasks. Scheduling Engine: Uses multi-queue priority scheduling + work-stealing mechanism to dynamically adjust task allocation. Backend Adaptation Layer: Abstracts a unified interface to support access to multiple backends. Monitoring and Observability: Built-in metric collection, supporting Prometheus integration.

Ren-Queue demonstrates value in multiple scenarios: Cost-sensitive Enterprises: By prioritizing the use of local models and free quotas, one case saved over 60% of costs. High-availability Services: Rely on cascading degradation to avoid single points of failure. Hybrid Cloud Architecture: Provides a unified abstraction layer to simplify development and operation. A/B Testing: Facilitates traffic routing and rollback.

Possible future development directions of Ren-Queue: Adaptive routing optimization based on reinforcement learning; support for streaming inference and incremental output to reduce first-token latency; integration with model fine-tuning processes to achieve end-to-end optimization.