# OptEx: A Deadline-Aware Cost Optimization Model for Spark

arXiv: https://arxiv.org/abs/1603.07936 · HTML: https://mlsanity.com/paper/1603.07936/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Mar 2016.

## Built on by (most cited first, 1 of 1)

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

## Abstract (arXiv)

We present OptEx, a closed-form model of job execution on Apache Spark, a popular parallel processing engine. To the best of our knowledge, OptEx is the first work that analytically models job completion time on Spark. The model can be used to estimate the completion time of a given Spark job on a cloud, with respect to the size of the input dataset, the number of iterations, the number of nodes comprising the underlying cluster. Experimental results demonstrate that OptEx yields a mean relative error of 6% in estimating the job completion time. Furthermore, the model can be applied for estimating the cost optimal cluster composition for running a given Spark job on a cloud under a completion deadline specified in the SLO (i.e., Service Level Objective). We show experimentally that OptEx is able to correctly estimate the cost optimal cluster composition for running a given Spark job under an SLO deadline with an accuracy of 98%.