Towards Online Code Specialization of Systems

2025-01-20

AI Summary

Low-level systems (e.g., network stacks) resist efficient compile-time specialization for dynamic workloads and runtime environments due to high implementation complexity, difficulty in predicting optimal strategies, and continuously evolving conditions. This paper introduces Iridescent—the first online, automatic, measurement-driven JIT code specialization framework for low-level systems. Developers annotate only lightweight specialization points; the system autonomously explores and deploys optimal specialization strategies via JIT compilation and dynamic binary rewriting, guided by real-time performance feedback (e.g., latency, throughput), eliminating reliance on static compilation and manual modeling. Evaluated on real-world network stacks, Iridescent achieves significant performance improvements—up to 2.3× latency reduction and 1.8× throughput gain—while imposing minimal developer overhead. Crucially, it enables continuous optimization in response to dynamic workload shifts and heterogeneous hardware platforms, demonstrating robust adaptability across diverse deployment scenarios.

Technology Category

• Machine Learning: Hardware-aware ML
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

Specializing low-level systems to specifics of the workload they serve and platform they are running on often significantly improves performance. However, specializing systems is difficult because of three compounding challenges: i) specialization for optimal performance requires in-depth compile-time changes; ii) the right combination of specialization choices for optimal performance is hard to predict a priori; and iii) workloads and platform details often change online. In practice, benefits of specialization are thus not attainable for many low-level systems. To address this, we advocate for a radically different approach for performance-critical low-level systems: designing and implementing systems with and for runtime code specialization. We leverage just-in-time compilation to change systems code based on developer-specified specialization points as the system runs. The JIT runtime automatically tries out specialization choices and measures their impact on system performance, e.g. request latency or throughput, to guide the search. With Iridescent, our early prototype, we demonstrate that online specialization (i) is feasible even for low-level systems code, such as network stacks, (ii) improves system performance without the need for complex cost models, (iii) incurs low developer effort, especially compared to manual exploration. We conclude with future opportunities online system code specialization enables.

Problem

• Low-level system optimization
• Runtime adaptation
• Performance enhancement

Innovation

• Runtime Code Adjustment
• Just-In-Time Compilation
• Online Optimization