Generating representative macrobenchmark microservice systems from distributed traces with Palette

TL;DR

This paper introduces Palette, a system that uses distributed trace data and Graphical Causal Models to generate realistic macrobenchmark microservice systems that mirror real-world cloud architectures.

Contribution

It presents a novel approach combining GCMs with distributed traces to create representative microservice benchmarks, addressing the lack of realistic testing environments.

Findings

Palette can generate systems with realistic topology and execution patterns.

The generated benchmarks closely match real-world microservice systems.

This approach improves evaluation accuracy for microservice-based cloud systems.

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.