A framework for allocating server time to spot and on-demand services in cloud computing
File(s)
Accepted version
Author(s)
Wu, Xiaohu
De Pellegrini, Francesco
Gao, Guanyu
Casale, G
Type
Journal Article
Abstract
Cloud computing delivers value to users by facilitating their access to computing capacity in periods when their need arises. An approach is to provide both on-demand and spot services on shared servers. The former allows users to access servers on demand at a fixed price and users occupy different periods of servers. The latter allows users to bid for the remaining unoccupied periods via dynamic pricing; however, without appropriate design, such periods may be arbitrarily small since on-demand users arrive randomly. This is also the current service model adopted by Amazon Elastic Cloud Compute. In this paper, we provide the first integral framework for sharing the time of servers between on-demand and spot services while optimally pricing spot instances. It guarantees that on-demand users can get served quickly while spot users can stably utilize servers for a properly long period once accepted, which is a key feature to make both on-demand and spot services accessible. Simulation results show that, by complementing the on-demand market with a spot market, a cloud provider can improve revenue by up to 464.7%. The framework is designed under assumptions which are met in real environments. It is a new tool that cloud operators can use to quantify the advantage of a hybrid spot and on-demand service, eventually making the case for operating such service model in their own infrastructures.
Date Issued
2019-12-06
Date Acceptance
2019-10-01
Citation
ACM Transactions on Modeling and Performance Evaluation of Computing Systems, 2019, 4 (4)
ISSN
2376-3639
Publisher
Association for Computing Machinery
Journal / Book Title
ACM Transactions on Modeling and Performance Evaluation of Computing Systems
Volume
4
Issue
4
Copyright Statement
© 2019 ACM. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in ACM Transactions on Modeling and Performance Evaluation of Computing Systems (TOMPECS), Volume 4, Issue 4, December 2019, https://doi.org/10.1145/3366682
Sponsor
Commission of the European Communities
Grant Number
825040
Publication Status
Published
Article Number
20