Generating representative macrobenchmark microservice systems from distributed traces with Palette
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
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| _version_ | 1866915331866361856 |
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| author | Anand, Vaastav Stolet, Matheus Mace, Jonathan Kaufmann, Antoine |
| author_facet | Anand, Vaastav Stolet, Matheus Mace, Jonathan Kaufmann, Antoine |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_06448 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Generating representative macrobenchmark microservice systems from distributed traces with Palette Anand, Vaastav Stolet, Matheus Mace, Jonathan Kaufmann, Antoine Distributed, Parallel, and Cluster Computing 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. |
| title | Generating representative macrobenchmark microservice systems from distributed traces with Palette |
| topic | Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2506.06448 |