Enterprise AI Analysis
QOSERVE: Breaking the Silos of LLM Inference Serving
The widespread adoption of Large Language Models (LLMs) has enabled diverse applications with very different latency requirements. Existing LLM serving frameworks rely on siloed infrastructure with coarse-grained workload segregation – interactive and batch – leading to inefficient resource utilization and limited support for fine-grained Quality-of-Service (QoS) differentiation. We present QOSERVE, a novel QoS-driven inference serving system that enables efficient co-scheduling of diverse workloads on shared infrastructure. QOSERVE introduces fine-grained QoS classification allowing applications to specify precise latency requirements, and dynamically adapts scheduling decisions based on real-time system state. Leveraging the predictable execution characteristics of LLM inference, QOSERVE implements dynamic chunking to improve overall throughput while maintaining strict QoS guarantees. Additionally, QOSERVE introduces hybrid prioritization to balance fairness and efficiency, and employs selective request relegation for graceful service degradation during overloads. Our evaluation demonstrates that QOSERVE increases serving capacity by 23% compared to current siloed deployments, while maintaining QoS guarantees on an A100 cluster, and improves per-replica goodput by up to 2.4x compared to Sarathi on a shared cluster. Notably, under extreme load, our system reduces SLO violations by an order of magnitude compared to current strategies.
Authors: Kanishk Goel, Jayashree Mohan, Nipun Kwatra, Ravi Shreyas Anupindi, Ramachandran Ramjee
Executive Summary: Optimizing LLM Inference for Enterprise
QOSERVE introduces a novel QoS-driven LLM inference serving system designed to break the limitations of siloed infrastructure and enhance resource utilization for diverse enterprise applications.
Key Innovations:
- Fine-grained QoS classification for precise latency requirements.
- Dynamic chunking to improve throughput while maintaining QoS.
- Hybrid prioritization for balanced fairness and efficiency.
- Selective request relegation for graceful degradation under overload.
Evaluations show QOSERVE increases serving capacity by 23% compared to siloed deployments, improves per-replica goodput by up to 2.4x, and reduces SLO violations by an order of magnitude under extreme load.
Enterprises leveraging LLMs should consider QOSERVE's architecture for improved cost-efficiency, responsiveness, and robust performance under varying loads.
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Increased Serving Capacity
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Improved Per-Replica Goodput
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Reduced SLO Violations
Deep Analysis & Enterprise Applications
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QoS-Aware Adaptive Scheduling
Hybrid Prioritization & Eager Relegation
Improved Serving Capacity & Goodput
LLM Inference Process Flow
Comparison with Traditional Scheduling
Dynamic Scheduling with QoS
QOSERVE optimizes LLM serving by dynamically adapting scheduling decisions based on QoS requirements and system state. Unlike traditional fixed-chunk systems, it leverages workload characteristics to maximize throughput without violating latency SLAs.
Benefits:
- ✓ Increased throughput due to dynamic chunking.
- ✓ Reduced latency violations for critical requests.
- ✓ Efficient resource utilization across diverse workloads.
Intelligent Overload Management
QOSERVE combines hybrid prioritization (EDF/SRPF) with eager relegation to maintain service quality under varying loads. This prevents cascading failures and ensures critical services remain responsive.
Benefits:
- ✓ Graceful service degradation during overloads.
- ✓ Prioritization of high-importance requests.
- ✓ Fairness across diverse request types and lengths.
23%
Higher Serving Capacity
QOSERVE demonstrates significant performance improvements over state-of-the-art siloed deployments. It achieves up to 23% higher serving capacity and 2.4x improved per-replica goodput. Under extreme loads, SLO violations are reduced by an order of magnitude. These gains are attributed to QOSERVE's ability to co-schedule diverse workloads on shared infrastructure, dynamically adjust chunk sizes, and intelligently manage priority and relegation, leading to substantial cost savings and enhanced user experience.
Enterprise Process Flow
Request Arrives
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Prefill Queue
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Hybrid Prioritization
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Prefill Selector
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Violation Checker
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Chunk Size Estimator
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Mixed Batch Construction
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GPU Execution
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Decode Queue
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Output Token Generated
LLM inference involves two distinct computational phases: prefill and decode. The prefill phase processes the entire input prompt simultaneously, which is computationally intensive. The subsequent decode phase generates output tokens auto-regressively, with each token's generation depending on previously generated tokens. QOSERVE leverages the predictable characteristics of these phases and employs chunked prefills for efficient batching and scheduling.
| Feature | Traditional Siloed Systems | QOSERVE |
|---|---|---|
| Workload Segregation | Coarse-grained (Interactive/Batch), separate infrastructure | Fine-grained QoS classes, co-scheduled on shared infrastructure |
| Chunking Strategy | Fixed chunk sizes, less efficient for diverse loads | Dynamic chunking based on real-time system state and QoS targets |
| Overload Management | Rate limiting, indiscriminate delays, unfair short job prioritization | Hybrid prioritization (EDF/SRPF), eager relegation for graceful degradation |
| Resource Utilization | Inefficient due to workload fluctuations and siloed deployments | Maximized through co-scheduling and dynamic resource allocation |
| SLO Compliance | High violation rates under varied loads, especially for long jobs | Maintains QoS guarantees, significantly reduces SLO violations under extreme loads |
Traditional LLM serving frameworks often rely on coarse-grained workload segregation (interactive vs. batch) and independent serving. Scheduling policies like FCFS, SJF, SRPF, and EDF struggle under varying loads, leading to inefficiencies or unfairness. QOSERVE's hybrid approach, which interpolates between EDF and SRPF, provides superior performance by minimizing SLO violations across both low and high loads and ensures graceful degradation.
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Implementation Roadmap
A phased approach to integrating QOSERVE into your enterprise LLM serving infrastructure.
Phase 1: Initial Assessment & Data Collection
Review existing LLM workloads, identify key latency requirements (TTFT, TBT, TTLT), and collect performance profiles for different models and hardware configurations.
Phase 2: QoS Definition & Integration
Define fine-grained QoS classes and integrate them into the LLM serving framework. Implement mechanisms to specify and enforce precise latency requirements for diverse applications.
Phase 3: Dynamic Chunking & Scheduling Engine Development
Develop and integrate the dynamic chunking module and the hybrid prioritization scheduler. This involves training the lightweight random forest model for latency prediction and implementing the EDF/SRPF interpolation logic.
Phase 4: Eager Relegation & Overload Management
Implement the eager relegation policy, including mechanisms for application-provided hints for request importance. Test and refine graceful service degradation under various overload scenarios.
Phase 5: Performance Evaluation & Optimization
Conduct comprehensive evaluations across different workloads, models, and hardware. Benchmark QOSERVE against baselines for serving capacity, goodput, latency, and SLO violations. Optimize parameters for optimal real-world performance.
Phase 6: Deployment & Monitoring
Deploy QOSERVE in a production environment. Establish continuous monitoring for QoS compliance, resource utilization, and system stability. Iterate on improvements based on live traffic data.
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