# Bellamy: Reusing Performance Models for Distributed Dataflow Jobs Across Contexts

arXiv: https://arxiv.org/abs/2107.13921 · HTML: https://mlsanity.com/paper/2107.13921/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 33 references that are in the map.
- First posted on arXiv 29 Jul 2021.

## Builds on (most cited first, 12 of 12)

- [Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification](https://mlsanity.com/paper/1502.01852/) (2015)
- [Optuna: A Next-generation Hyperparameter Optimization Framework](https://mlsanity.com/paper/1907.10902/) (2019)
- [Self-Normalizing Neural Networks](https://mlsanity.com/paper/1706.02515/) (2017)
- [Tune: A Research Platform for Distributed Model Selection and Training](https://mlsanity.com/paper/1807.05118/) (2018)
- [Low-Level Augmented Bayesian Optimization for Finding the Best Cloud VM](https://mlsanity.com/paper/1712.10081/) (2017)
- [Micky: A Cheaper Alternative for Selecting Cloud Instances](https://mlsanity.com/paper/1803.05587/) (2018)
- [Tuneful: An Online Significance-Aware Configuration Tuner for Big Data Analytics](https://mlsanity.com/paper/2001.08002/) (2020)
- [C3O: Collaborative Cluster Configuration Optimization for Distributed Data Processing in Public Clouds](https://mlsanity.com/paper/2107.13317/) (2021)
- [Scout: An Experienced Guide to Find the Best Cloud Configuration](https://mlsanity.com/paper/1803.01296/) (2018)
- [Parallelization in Scientific Workflow Management Systems](https://mlsanity.com/paper/1303.7195/) (2013)
- [Towards Collaborative Optimization of Cluster Configurations for Distributed Dataflow Jobs](https://mlsanity.com/paper/2011.07965/) (2020)
- [OptEx: A Deadline-Aware Cost Optimization Model for Spark](https://mlsanity.com/paper/1603.07936/) (2016)

## Built on by (most cited first, 4 of 4)

- [Enel: Context-Aware Dynamic Scaling of Distributed Dataflow Jobs using Graph Propagation](https://mlsanity.com/paper/2108.12211/) (2021)
- [On the Potential of Execution Traces for Batch Processing Workload Optimization in Public Clouds](https://mlsanity.com/paper/2111.08759/) (2021)
- [Perona: Robust Infrastructure Fingerprinting for Resource-Efficient Big Data Analytics](https://mlsanity.com/paper/2211.08227/) (2022)
- [Karasu: A Collaborative Approach to Efficient Cluster Configuration for Big Data Analytics](https://mlsanity.com/paper/2308.11792/) (2023)

## Abstract (arXiv)

Distributed dataflow systems enable the use of clusters for scalable data analytics. However, selecting appropriate cluster resources for a processing job is often not straightforward. Performance models trained on historical executions of a concrete job are helpful in such situations, yet they are usually bound to a specific job execution context (e.g. node type, software versions, job parameters) due to the few considered input parameters. Even in case of slight context changes, such supportive models need to be retrained and cannot benefit from historical execution data from related contexts. This paper presents Bellamy, a novel modeling approach that combines scale-outs, dataset sizes, and runtimes with additional descriptive properties of a dataflow job. It is thereby able to capture the context of a job execution. Moreover, Bellamy is realizing a two-step modeling approach. First, a general model is trained on all the available data for a specific scalable analytics algorithm, hereby incorporating data from different contexts. Subsequently, the general model is optimized for the specific situation at hand, based on the available data for the concrete context. We evaluate our approach on two publicly available datasets consisting of execution data from various dataflow jobs carried out in different environments, showing that Bellamy outperforms state-of-the-art methods.