TY - GEN
T1 - Towards Designing Cost-Optimal Policies to Utilize IaaS Clouds with Online Learning
AU - Wu, Xiaohu
AU - Loiseau, Patrick
AU - Hyytiä, Esa
N1 - Funding Information: ACKNOWLEDGMENT Part of Xiaohu Wu’s work was done when he was with Eurecom, France and Aalto University, Finland. Esa Hyytia’s work and a part of Xiaohu’s work were supported by the Academy of Finland in the FQ4BD project (grant no. 296206). Patrick Loiseau acknowledges support from the Alexander von Humboldt Foundation. Publisher Copyright: © 2017 IEEE.
PY - 2017/10/9
Y1 - 2017/10/9
N2 - 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 62.85% when spot and on-demand instances are considered and of up to 44.00% when self-owned instances are considered, compared to previously proposed or intuitive policies.
AB - 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 62.85% when spot and on-demand instances are considered and of up to 44.00% when self-owned instances are considered, compared to previously proposed or intuitive policies.
UR - https://www.scopus.com/pages/publications/85035315691
U2 - 10.1109/ICCAC.2017.23
DO - 10.1109/ICCAC.2017.23
M3 - Conference contribution
T3 - Proceedings - 2017 IEEE International Conference on Cloud and Autonomic Computing, ICCAC 2017
SP - 160
EP - 171
BT - Proceedings - 2017 IEEE International Conference on Cloud and Autonomic Computing, ICCAC 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 4th IEEE International Conference on Cloud and Autonomic Computing, ICCAC 2017
Y2 - 18 September 2017 through 22 September 2017
ER -