# Micky: A Cheaper Alternative for Selecting Cloud Instances

arXiv: https://arxiv.org/abs/1803.05587 · HTML: https://mlsanity.com/paper/1803.05587/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Mar 2018.

## Built on by (most cited first, 6 of 6)

- [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)
- [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)
- [Karasu: A Collaborative Approach to Efficient Cluster Configuration for Big Data Analytics](https://mlsanity.com/paper/2308.11792/) (2023)

## Abstract (arXiv)

Most cloud computing optimizers explore and improve one workload at a time. When optimizing many workloads, the single-optimizer approach can be prohibitively expensive. Accordingly, we examine "collective optimizer" that concurrently explore and improve a set of workloads significantly reducing the measurement costs. Our large-scale empirical study shows that there is often a single cloud configuration which is surprisingly near-optimal for most workloads. Consequently, we create a collective-optimizer, MICKY, that reformulates the task of finding the near-optimal cloud configuration as a multi-armed bandit problem. MICKY efficiently balances exploration (of new cloud configurations) and exploitation (of known good cloud configuration). Our experiments show that MICKY can achieve on average 8.6 times reduction in measurement cost as compared to the state-of-the-art method while finding near-optimal solutions. Hence we propose MICKY as the basis of a practical collective optimization method for finding good cloud configurations (based on various constraints such as budget and tolerance to near-optimal configurations).