# Towards Collaborative Optimization of Cluster Configurations for Distributed Dataflow Jobs

arXiv: https://arxiv.org/abs/2011.07965 · HTML: https://mlsanity.com/paper/2011.07965/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Nov 2020.

## Built on by (most cited first, 3 of 3)

- [Bellamy: Reusing Performance Models for Distributed Dataflow Jobs Across Contexts](https://mlsanity.com/paper/2107.13921/) (2021)
- [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)

## Abstract (arXiv)

Analyzing large datasets with distributed dataflow systems requires the use of clusters. Public cloud providers offer a large variety and quantity of resources that can be used for such clusters. However, picking the appropriate resources in both type and number can often be challenging, as the selected configuration needs to match a distributed dataflow job's resource demands and access patterns. A good cluster configuration avoids hardware bottlenecks and maximizes resource utilization, avoiding costly overprovisioning. We propose a collaborative approach for finding optimal cluster configurations based on sharing and learning from historical runtime data of distributed dataflow jobs. Collaboratively shared data can be utilized to predict runtimes of future job executions through the use of specialized regression models. However, training prediction models on historical runtime data that were produced by different users and in diverse contexts requires the models to take these contexts into account.