# Low-Level Augmented Bayesian Optimization for Finding the Best Cloud VM

arXiv: https://arxiv.org/abs/1712.10081 · HTML: https://mlsanity.com/paper/1712.10081/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Dec 2017.

## Built on by (most cited first, 9 of 9)

- [Predicting Software Performance with Divide-and-Learn](https://mlsanity.com/paper/2306.06651/) (2023)
- [Deep Configuration Performance Learning: A Systematic Survey and Taxonomy](https://mlsanity.com/paper/2403.03322/) (2024)
- [Bellamy: Reusing Performance Models for Distributed Dataflow Jobs Across Contexts](https://mlsanity.com/paper/2107.13921/) (2021)
- [Dividable Configuration Performance Learning](https://mlsanity.com/paper/2409.07629/) (2024)
- [Enel: Context-Aware Dynamic Scaling of Distributed Dataflow Jobs using Graph Propagation](https://mlsanity.com/paper/2108.12211/) (2021)
- [Search-based Methods for Multi-Cloud Configuration](https://mlsanity.com/paper/2204.09437/) (2022)
- [RIBBON: Cost-Effective and QoS-Aware Deep Learning Model Inference using a Diverse Pool of Cloud Computing Instances](https://mlsanity.com/paper/2207.11434/) (2022)
- [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)

With the advent of big data applications, which tends to have longer execution time, choosing the right cloud VM to run these applications has significant performance as well as economic implications. For example, in our large-scale empirical study of 107 different workloads on three popular big data systems, we found that a wrong choice can lead to a 20 times slowdown or an increase in cost by 10 times. Bayesian optimization is a technique for optimizing expensive (black-box) functions. Previous attempts have only used instance-level information (such as # of cores, memory size) which is not sufficient to represent the search space. In this work, we discover that this may lead to the fragility problem---either incurs high search cost or finds only the sub-optimal solution. The central insight of this paper is to use low-level performance information to augment the process of Bayesian Optimization. Our novel low-level augmented Bayesian Optimization is rarely worse than current practices and often performs much better (in 46 of 107 cases). Further, it significantly reduces the search cost in nearly half of our case studies. Based on this work, we conclude that it is often insufficient to use general-purpose off-the-shelf methods for configuring cloud instances without augmenting those methods with essential systems knowledge such as CPU utilization, working memory size and I/O wait time.