Micky: A Cheaper Alternative for Selecting Cloud Instances

TL;DR

MICKY is a collective optimizer that efficiently finds near-optimal cloud configurations for multiple workloads simultaneously, significantly reducing measurement costs by framing the problem as a multi-armed bandit.

Contribution

We introduce MICKY, a novel collective optimization approach that models cloud configuration selection as a multi-armed bandit problem, enabling cost-effective optimization for multiple workloads.

Findings

MICKY reduces measurement costs by 8.6 times on average.

A single cloud configuration is often near-optimal for most workloads.

MICKY effectively balances exploration and exploitation in cloud optimization.

Abstract

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).