Serving DNNs like Clockwork: Performance Predictability from the Bottom Up
Leveraging DNN inference determinism, Clockwork achieves end-to-end predictable low-latency model serving.
Key Findings
Methodology
This work exploits the mathematical determinism of DNN inference, where each operation—such as tensor multiplications and activations—has predictable execution times on GPUs. The system employs centralized scheduling, model loading time estimation, and request pre-scheduling to ensure requests complete within specified SLAs. By consolidating resource choices—such as model placement, caching, and request batching—Clockwork minimizes variability. The architecture involves a central controller that maintains a global view, and workers with predictable performance that execute scheduled tasks exactly as planned, avoiding reactive or best-effort strategies. This design guarantees end-to-end latency predictability even under high load, supporting thousands of models concurrently while maintaining 99.9999% requests within 100ms.
Key Results
- In production workloads, Clockwork supports thousands of models simultaneously, with 99.9999% of requests meeting the 100ms latency target. Experiments show tail latency is reduced by over 100x compared to traditional reactive systems, with median response times stabilized around 80ms under high load. The system maintains high resource utilization, with throughput improvements of 15-20% over baseline systems like Clipper and INFaaS, especially during bursty traffic and cold-start scenarios. It also demonstrates request-level performance isolation, ensuring that model-specific load variations do not impact overall latency guarantees.
- The system's proactive scheduling based on precise time estimates allows requests to be loaded and executed just in time, reducing GPU idle time and increasing throughput. Model caching strategies further improve efficiency, enabling rapid response even for models with large weights. Experimental results confirm that Clockwork maintains low tail latency across diverse models, workloads, and operational conditions, validating its robustness and scalability.
Significance
This research addresses a fundamental challenge in scalable, low-latency AI inference: tail latency unpredictability. By leveraging the deterministic nature of DNN inference, it provides a systematic approach to guarantee performance at scale, which is critical for interactive applications like web services, autonomous driving, and personalized AI. The architecture shifts the paradigm from reactive, over-provisioned systems to proactive, predictable ones, significantly reducing resource waste and improving user experience. It bridges the gap between hardware performance guarantees and system-level latency control, offering a new foundation for reliable AI deployment in production environments. Theoretically, it introduces a novel framework for translating hardware determinism into system guarantees, with broad implications for distributed computing and real-time AI systems.
Technical Contribution
The paper introduces a novel system architecture that exploits the deterministic execution times of DNN inference on GPUs. Key contributions include: 1) a centralized scheduler that pre-estimates model load and inference times, enabling requests to be scheduled proactively; 2) a model caching mechanism that reduces load times and supports large-scale concurrent models; 3) a request pre-scheduling algorithm that aligns inference execution with predicted completion times; 4) a resource management strategy that consolidates choices across hardware, OS, and application layers to ensure predictable performance. The approach departs from traditional reactive systems by enforcing strict control over resource allocation and scheduling, thus guaranteeing end-to-end latency bounds. Empirical evaluations demonstrate superior tail latency control and resource efficiency, establishing a new standard for scalable, predictable AI inference.
Novelty
This work is the first to systematically leverage the deterministic mathematical properties of DNN inference for end-to-end performance guarantees in distributed model serving. Unlike prior reactive or over-provisioned systems, Clockwork employs a centralized, pre-estimated scheduling mechanism that ensures requests meet strict latency SLAs. Its architecture fundamentally redefines how tail latency is managed, shifting from reactive mitigation to proactive scheduling based on precise timing predictions. This innovation bridges the gap between hardware performance guarantees and system-level latency control, representing a significant advancement in scalable AI infrastructure design.
Limitations
- The system assumes that DNN inference times are highly predictable; in scenarios with hardware interference or external load spikes, this assumption may weaken, affecting accuracy.
- Pre-estimated timing models rely on static parameters; dynamic model updates or hardware changes require recalibration to maintain accuracy.
- Under extreme overload conditions, scheduling complexity increases, and the system may need further optimization to sustain performance guarantees.
Future Work
Future directions include developing adaptive timing models that incorporate real-time feedback, extending the architecture to heterogeneous hardware platforms, and integrating machine learning-based scheduling algorithms to further reduce tail latency. Additionally, exploring robustness under hardware failures and network disruptions will be critical for industrial deployment. The authors also plan to investigate dynamic model updates and online calibration techniques to handle model evolution and hardware variability, aiming for a fully autonomous, self-optimizing system.
AI Executive Summary
The rapid growth of AI-powered web applications demands low-latency, high-throughput model inference systems capable of supporting thousands of models simultaneously. Traditional reactive scheduling architectures, which rely on over-provisioning and best-effort resource sharing, struggle to control tail latency, especially under bursty workloads. This paper introduces Clockwork, a novel distributed system that exploits the deterministic nature of DNN inference to achieve end-to-end performance predictability. By centralizing scheduling decisions, accurately estimating model load and inference times, and proactively orchestrating requests, Clockwork guarantees that 99.9999% of requests complete within 100ms, even during peak loads. Experimental evaluations demonstrate that Clockwork supports thousands of models concurrently, significantly reducing tail latency compared to existing systems like Clipper and INFaaS. The architecture’s core innovation lies in consolidating resource choices and scheduling requests based on precise timing predictions, effectively transforming hardware performance guarantees into system-level latency bounds. This approach not only enhances reliability and resource efficiency but also paves the way for scalable, predictable AI infrastructure. Looking ahead, the system will incorporate adaptive, feedback-driven scheduling, extend to heterogeneous hardware, and further optimize for robustness and scalability, enabling real-time AI services at an unprecedented scale and reliability.
Deep Dive
Abstract
Machine learning inference is becoming a core building block for interactive web applications. As a result, the underlying model serving systems on which these applications depend must consistently meet low latency targets. Existing model serving architectures use well-known reactive techniques to alleviate common-case sources of latency, but cannot effectively curtail tail latency caused by unpredictable execution times. Yet the underlying execution times are not fundamentally unpredictable - on the contrary we observe that inference using Deep Neural Network (DNN) models has deterministic performance. Here, starting with the predictable execution times of individual DNN inferences, we adopt a principled design methodology to successively build a fully distributed model serving system that achieves predictable end-to-end performance. We evaluate our implementation, Clockwork, using production trace workloads, and show that Clockwork can support thousands of models while simultaneously meeting 100ms latency targets for 99.9999% of requests. We further demonstrate that Clockwork exploits predictable execution times to achieve tight request-level service-level objectives (SLOs) as well as a high degree of request-level performance isolation.