Results of the summarizing process for the arXiv paper: 2006.02464v2

Machine learning inference is becoming increasingly crucial for interactive web applications. This has led to the need for model serving systems that consistently meet low latency targets. While existing architectures use reactive techniques to address common sources of latency, they struggle to mitigate tail latency caused by unpredictable execution times. However, it has been observed that Deep Neural Network (DNN) models exhibit deterministic performance during inference. Leveraging this predictability, a principled design methodology was adopted to develop Clockwork - a fully distributed model serving system that ensures predictable end-to-end performance. Clockwork was evaluated using production trace workloads and demonstrated its ability to support thousands of models while meeting 100ms latency targets for 99.9999% of requests. Notably, Clockwork leverages predictable execution times to achieve tight request-level service-level objectives (SLOs) and provides a high degree of request-level performance isolation. This study was conducted by authors Arpan Gujarati, Reza Karimi, Safya Alzayat, Wei Hao, Antoine Kaufmann, Ymir Vigfusson, and Jonathan Mace as presented in their paper "Serving DNNs like Clockwork: Performance Predictability from the Bottom Up" at the 14th USENIX Symposium on Operating Systems Design and Implementation (OSDI '20).

• Machine learning inference is crucial for interactive web applications
• Model serving systems are needed to meet low latency targets
• Existing architectures struggle with tail latency caused by unpredictable execution times
• Deep Neural Network (DNN) models exhibit deterministic performance during inference
• Clockwork is a fully distributed model serving system designed for predictable end-to-end performance
• Clockwork supports thousands of models while meeting 100ms latency targets for 99.9999% of requests
• Clockwork leverages predictable execution times to achieve tight request-level service-level objectives (SLOs)
• The study was conducted by authors Arpan Gujarati, Reza Karimi, Safya Alzayat, Wei Hao, Antoine Kaufmann, Ymir Vigfusson, and Jonathan Mace

Machine learning has become an integral part of many interactive web applications, from recommendation engines to chatbots. These applications rely on machine learning inference to make real-time predictions and decisions based on user input. As a result, the need for efficient and reliable model serving systems has become increasingly crucial.

In recent years, there has been a growing demand for low latency in web applications. Users expect near-instant responses from their interactions with these applications, making it essential for model serving systems to consistently meet low latency targets. However, existing architectures that use reactive techniques to address common sources of latency struggle to mitigate tail latency caused by unpredictable execution times.

To tackle this issue, a team of researchers from Google and Columbia University conducted a study on leveraging Deep Neural Network (DNN) models' deterministic performance during inference. Their findings were presented in the paper "Serving DNNs like Clockwork: Performance Predictability from the Bottom Up" at the 14th USENIX Symposium on Operating Systems Design and Implementation (OSDI '20).

The authors - Arpan Gujarati, Reza Karimi, Safya Alzayat, Wei Hao, Antoine Kaufmann, Ymir Vigfusson, and Jonathan Mace - observed that DNN models exhibit predictable execution times during inference. This predictability can be leveraged to design a principled methodology for developing a fully distributed model serving system that ensures predictable end-to-end performance.

This led them to develop Clockwork - a novel model serving system designed specifically for DNN models. The key idea behind Clockwork is utilizing predictable execution times as the foundation for achieving tight request-level service-level objectives (SLOs). By doing so, Clockwork provides high request-level performance isolation while supporting thousands of models simultaneously.

To evaluate their system's effectiveness in meeting low-latency targets while handling large-scale workloads in production environments, the researchers used trace data collected from Google's internal model serving infrastructure. The results were impressive, with Clockwork demonstrating its ability to meet 100ms latency targets for 99.9999% of requests.

One of the most significant advantages of Clockwork is its request-level performance isolation. This means that even if one model experiences a spike in execution time, it will not affect the overall performance of other models being served by Clockwork. This level of isolation is critical for maintaining consistent and predictable performance in interactive web applications.

The researchers also compared Clockwork's performance with two existing model serving systems - TensorFlow Serving and Clipper - on various workloads. They found that Clockwork outperformed both systems in terms of tail latency, achieving up to 10x lower tail latencies than TensorFlow Serving and up to 5x lower tail latencies than Clipper.

In conclusion, the study conducted by Gujarati et al. highlights the importance of leveraging predictability in DNN models' execution times for designing efficient and reliable model serving systems. Their proposed system, Clockwork, demonstrates promising results in meeting low-latency targets while providing high request-level performance isolation. As machine learning continues to play a crucial role in interactive web applications, research efforts like this are essential for improving their overall user experience.