Towards Designing Cost-Optimal Policies to Utilize IaaS Clouds under Online Learning

TL;DR

This paper develops an online learning-based framework for cost-effective cloud resource allocation, optimizing the use of self-owned, on-demand, and spot instances to reduce expenses while meeting response-time targets.

Contribution

It introduces a near-optimal policy for self-owned instances and an optimal policy for on-demand and spot instances, using online learning to adapt parameters in real-time.

Findings

Achieves up to 64.51% cost reduction with spot and on-demand instances.

Achieves up to 43.74% cost reduction with self-owned instances.

Policies outperform previous or intuitive approaches.

Abstract

Many businesses possess a small infrastructure that they can use for their computing tasks, but also often buy extra computing resources from clouds. Cloud vendors such as Amazon EC2 offer two types of purchase options: on-demand and spot instances. As tenants have limited budgets to satisfy their computing needs, it is crucial for them to determine how to purchase different options and utilize them (in addition to possible self-owned instances) in a cost-effective manner while respecting their response-time targets. In this paper, we propose a framework to design policies to allocate self-owned, on-demand and spot instances to arriving jobs. In particular, we propose a near-optimal policy to determine the number of self-owned instance and an optimal policy to determine the number of on-demand instances to buy and the number of spot instances to bid for at each time unit. Our policies rely on a small number of parameters and we use an online learning technique to infer their optimal values. Through numerical simulations, we show the effectiveness of our proposed policies, in particular that they achieve a cost reduction of up to 64.51% when spot and on-demand instances are considered and of up to 43.74% when self-owned instances are considered, compared to previously proposed or intuitive policies.