Large Language Models as Realistic Microservice Trace Generators

ICLR 2025 Conference Submission12738 Authors

Keywords: synthetic data, synthetic trace, microservice, large language model, machine learning for systems

TL;DR: We train a language model to generate synthetic computer system traces, specifically microservice call graphs.

Abstract: Computer system workload traces, which record hardware or software events during application execution, are essential for understanding the behavior of complex systems and managing their processing and memory resources. However, obtaining real-world traces can be challenging due to the significant collection overheads in performance and privacy concerns that arise in proprietary systems. As a result, synthetic trace generation is considered a promising alternative to using traces collected in real-world production deployments. This paper proposes to train a large language model (LLM) to generate synthetic workload traces, specifically microservice call graphs. To capture complex and arbitrary hierarchical structures and implicit constraints in such traces, we fine-tune LLMs to generate each layer recursively, making call graph generation a sequence of easier steps. To further enforce learning constraints in traces and generate uncommon situations, we apply additional instruction tuning steps to align our model with the desired trace features. Our evaluation results show that our model can generate diverse realistic traces under various conditions and outperform existing methods in accuracy and validity. We show that our synthetically generated traces can effectively substitute real-world data in optimizing or tuning systems management tasks. We also show that our model can be adapted to perform key downstream trace-related tasks, specifically, predicting key trace features and infilling missing data given partial traces.

Primary Area: generative models

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Submission Number: 12738