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Efficient Serverless Cold Start: Reducing Library Loading Overhead by Profile-guided Optimization

Syed Salauddin Mohammad Tariq, Ali Al Zein, Soumya Sripad Vaidya, Arati Khanolkar, Zheng Song, Probir Roy

TL;DR

SlimStart introduces a profile-guided optimization for serverless functions to curb cold-start latency by identifying and lazily loading workload-dependent libraries. It combines a lightweight runtime profiler, automated AST-based code transformations, and an adaptive mechanism that reacts to evolving workloads, integrating smoothly with CI/CD pipelines. Empirical evaluation across benchmarks and real applications shows substantial gains in initialization and end-to-end latency, as well as memory savings, outperforming static-analysis baselines. This runtime profiling approach enables more precise, workload-aware optimizations, offering practical benefits for cloud deployments with variable workloads.

Abstract

Serverless computing abstracts away server management, enabling automatic scaling, efficient resource utilization, and cost-effective pricing models. However, despite these advantages, it faces the significant challenge of cold-start latency, adversely impacting end-to-end performance. Our study shows that many serverless functions initialize libraries that are rarely or never used under typical workloads, thus introducing unnecessary overhead. Although existing static analysis techniques can identify unreachable libraries, they fail to address workload-dependent inefficiencies, resulting in limited performance improvements. To overcome these limitations, we present SLIMSTART, a profile-guided optimization tool designed to identify and mitigate inefficient library usage patterns in serverless applications. By leveraging statistical sampling and call-path profiling, SLIMSTART collects runtime library usage data, generates detailed optimization reports, and applies automated code transformations to reduce cold-start overhead. Furthermore, SLIMSTART integrates seamlessly into CI/CD pipelines, enabling adaptive monitoring and continuous optimizations tailored to evolving workloads. Through extensive evaluation across three benchmark suites and four real-world serverless applications, SLIMSTART achieves up to a 2.30X speedup in initialization latency, a 2.26X improvement in end-to-end latency, and a 1.51X reduction in memory usage, demonstrating its effectiveness in addressing cold-start inefficiencies and optimizing resource utilization.

Efficient Serverless Cold Start: Reducing Library Loading Overhead by Profile-guided Optimization

TL;DR

SlimStart introduces a profile-guided optimization for serverless functions to curb cold-start latency by identifying and lazily loading workload-dependent libraries. It combines a lightweight runtime profiler, automated AST-based code transformations, and an adaptive mechanism that reacts to evolving workloads, integrating smoothly with CI/CD pipelines. Empirical evaluation across benchmarks and real applications shows substantial gains in initialization and end-to-end latency, as well as memory savings, outperforming static-analysis baselines. This runtime profiling approach enables more precise, workload-aware optimizations, offering practical benefits for cloud deployments with variable workloads.

Abstract

Serverless computing abstracts away server management, enabling automatic scaling, efficient resource utilization, and cost-effective pricing models. However, despite these advantages, it faces the significant challenge of cold-start latency, adversely impacting end-to-end performance. Our study shows that many serverless functions initialize libraries that are rarely or never used under typical workloads, thus introducing unnecessary overhead. Although existing static analysis techniques can identify unreachable libraries, they fail to address workload-dependent inefficiencies, resulting in limited performance improvements. To overcome these limitations, we present SLIMSTART, a profile-guided optimization tool designed to identify and mitigate inefficient library usage patterns in serverless applications. By leveraging statistical sampling and call-path profiling, SLIMSTART collects runtime library usage data, generates detailed optimization reports, and applies automated code transformations to reduce cold-start overhead. Furthermore, SLIMSTART integrates seamlessly into CI/CD pipelines, enabling adaptive monitoring and continuous optimizations tailored to evolving workloads. Through extensive evaluation across three benchmark suites and four real-world serverless applications, SLIMSTART achieves up to a 2.30X speedup in initialization latency, a 2.26X improvement in end-to-end latency, and a 1.51X reduction in memory usage, demonstrating its effectiveness in addressing cold-start inefficiencies and optimizing resource utilization.
Paper Structure (25 sections, 7 equations, 10 figures, 5 tables)

This paper contains 25 sections, 7 equations, 10 figures, 5 tables.

Table of Contents

  1. Introduction
  2. Motivation
  3. Optimizing Library Loading: Why and How
  4. Deficiency of Static-Analysis based Detection
  5. Potential Impact of Addressing such Deficiency
  6. Design of SlimStart
  7. Methodology and Implementation
  8. Dynamic Monitoring of Library Initialization and Usage
  9. Hierarchical Breakdown of Initialization Overhead
  10. Sampling-Based Call-path Profiling
  11. Code Optimization
  12. Adaptive Mechanism for Evolving Workloads
  13. Implementation
  14. Evaluation
  15. Experimental setup
  16. ...and 10 more sections

Figures (10)

  • Figure 1: Ratio of library Initialization time to end-to-end time.
  • Figure 2: Comparison of library initialization overhead in serverless applications, grouped by library categorization from static reachability analysis (STAT) and dynamic profiling via statistical sampling (DYN). The dynamic profiling highlights finer-grained insights into library usage during execution, emphasizing the differences in overhead contributions.
  • Figure 3: PDF plot of serverless applications by the number of handler functions and CDF of entry point invocation frequencies from production trace data.
  • Figure 4: SlimStart workflow overview.
  • Figure 5: Dependency graph with an entry point for a serverless function. The entry point (App) delegates to the orchestrator (Lib-1), which coordinates tasks among libraries with cascading dependencies. Lib-4 (blue) represents a library with all samples originating during initialization, demonstrating the challenge of attributing initialization vs. runtime usage. Dashed edges represent indirect calls, such as between Lib-5 and Lib-6. Lib-6 (Brown) is accessed via multiple call paths, illustrating the complexity of analyzing usage in multi-path scenarios.
  • ...and 5 more figures