Generating representative macrobenchmark microservice systems from distributed traces with Palette

2025-06-06

AI Summary

Microservice evaluation has long suffered from the lack of representative benchmarks that simultaneously capture real-world scale, topology, and execution patterns. To address this, we propose Palette, a novel framework that introduces Graph Causal Models (GCMs) to microservice topology abstraction for the first time. Palette integrates call-branching probabilities, dependency ordering, and latency distributions to enable end-to-end generation of configurable macroscopic benchmarks directly from industrial-scale distributed trace data. Unlike conventional synthetic or simulation-based approaches, Palette-generated benchmarks—evaluated across multiple large-scale trace datasets—exhibit significantly higher fidelity, scalability, and reproducibility. This advancement substantially enhances the realism and practical utility of microservice system evaluation, enabling more rigorous and production-relevant benchmarking.

Technology Category

• Data Mining & Knowledge Management: Scalability, Parallel & Distributed Systems
• Machine Learning: Scalability of ML Systems
• Planning, Routing, and Scheduling: Planning with Language Models

Application Category

• Security and Privacy: Large-scale security measurements
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• User Modeling, Personalization and Recommendation: Practical large-scale studies of user experience

Abstract

Microservices are the dominant design for developing cloud systems today. Advancements for microservice need to be evaluated in representative systems, e.g. with matching scale, topology, and execution patterns. Unfortunately in practice, researchers and practitioners alike often do not have access to representative systems. Thus they have to resort to sub-optimal non-representative alternatives, e.g. small and oversimplified synthetic benchmark systems or simulated system models instead. To solve this issue, we propose the use of distributed trace datasets, available from large internet companies, to generate representative microservice systems. To do so, we introduce a novel abstraction of a system topology which uses Graphical Causal Models (GCMs) to model the underlying system by incorporating the branching probabilities, execution order of outgoing calls to every dependency, and execution times. We then incorporate this topology in Palette, a system that generates representative flexible macrobenchmarks microservice systems from distributed traces.

Problem

• Generating representative microservice systems from distributed traces
• Addressing lack of access to realistic microservice benchmarks
• Modeling system topology using Graphical Causal Models (GCMs)

Innovation

• Generates microservice systems from distributed traces
• Uses Graphical Causal Models for topology abstraction
• Incorporates branching probabilities and execution times