Subscription or Pay-as-You-Go: Optimally Purchasing IaaS Instances in Public Clouds
Document Type
Conference Proceeding
Publication Date
9-5-2018
Abstract
In public clouds such as Amazon EC2, there are two main pricing models in purchasing Infrastructure-as-a-Service (IaaS) instances: the pay-as-you-go model and the subscription model. For these two options, users can dynamically combine them to provide services for demands to save their instance acquisition costs. Making optimal decisions toward the purchase of IaaS instances generally requires prior knowledge of future demands; however, it is difficult for users to predict all future workloads accurately. To deal with this problem, online reservation algorithms have been proposed to guide users in reserving instances. However, existing online algorithms do not conform to the pricing rules currently used in public cloud platforms. Therefore, we put forward a new online reserving algorithm for instance in accordance with the pricing policies used in most public IaaS offerings. Specifically, in this study, we use Amazon EC2 as an example to illustrate our algorithm. Through theoretical analysis, we prove that the cost of the proposed algorithm A-β in this paper is not greater than 2-1/β times of the optimal offline algorithm, where β>1 is a critical point in the online reservation algorithm proposed in this paper. Via extensive experimental simulations using both synthetic and actual workload datasets, we demonstrated that the online algorithm A-β is much more cost effective for cloud users than always paying-as-you-go in public IaaS markets.
Publication Source (Journal or Book title)
Proceedings - 2018 IEEE International Conference on Web Services, ICWS 2018 - Part of the 2018 IEEE World Congress on Services
First Page
219
Last Page
226
Recommended Citation
Yang, S., Pan, L., Wang, Q., Liu, S., & Zhang, S. (2018). Subscription or Pay-as-You-Go: Optimally Purchasing IaaS Instances in Public Clouds. Proceedings - 2018 IEEE International Conference on Web Services, ICWS 2018 - Part of the 2018 IEEE World Congress on Services, 219-226. https://doi.org/10.1109/ICWS.2018.00035
