Toward designing cost-optimal policies to utilize IaaS clouds with online learning

Xiaohu Wu*, Patrick Loiseau, Esa Hyytia

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

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 instances 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 percent when spot and on-demand instances are considered and of up to 43.74 percent when self-owned instances are considered, compared to previously proposed or intuitive policies.

Original languageEnglish
Article number8821399
Pages (from-to)501-514
Number of pages14
JournalIEEE Transactions on Parallel and Distributed Systems
Volume31
Issue number3
DOIs
Publication statusPublished - 1 Mar 2020

Bibliographical note

Funding Information:
The work of Patrick Loiseau was supported by the French National Research Agency (ANR) through the Investissements davenir program (ANR-15-IDEX-02), and by the Alexander von Humboldt Foundation. Part of Xiaohu Wu’s work was done when he was with Eurecom, Sophia-Antipolis, France; in addition, his work was also supported by the European Union’s Horizon 2020 research and innovation programme in the ROMA project (grant no. 754514). The work of Esa Hyytia was supported by the Academy of Finland in the FQ4BD project (grant no. 296206).

Publisher Copyright:
© 1990-2012 IEEE.

Other keywords

  • cost efficiency
  • On-demand instances
  • online learning
  • spot instances

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