Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
9-2020
Abstract
This paper proposes and analyzes a new virtual machine (VM) placement technique called Group Instance to deal with co-location attacks in public Infrastructure-as-a-Service (IaaS) clouds. Specifically, Group Instance organizes cloud users into groups with pre-determined sizes set by the cloud provider. Our empirical results obtained via experiments with real-world data sets containing million of VM requests have demonstrated the effectiveness of the new technique. In particular, the advantages of Group Instance are three-fold: 1) it is simple and highly configurable to suit the financial and security needs of cloud providers, 2) it produces better or at least similar performance compared to more complicated, state-of-the-art algorithms in terms of resource utilization and co-location security, and 3) it does not require any modifications to the underlying infrastructures of existing public cloud services.
Keywords
cloud security, co-location attacks, virtual machine placement
Discipline
Software Engineering
Research Areas
Software and Cyber-Physical Systems
Publication
2020 29th International Conference on Enabling Technologies: Infrastructure for Collaborative Enterprises (WETICE): Bayonne, France, September 10-13: Proceedings
First Page
64
Last Page
69
ISBN
9781728169750
Identifier
10.1109/WETICE49692.2020.00021
Publisher
IEEE Computer Society
City or Country
Los Alamitos, CA
Embargo Period
5-17-2021
Citation
LONG, Vu Duc and TA, Nguyen Binh Duong.
Group Instance: Flexible co-location resistant virtual machine placement in IaaS clouds. (2020). 2020 29th International Conference on Enabling Technologies: Infrastructure for Collaborative Enterprises (WETICE): Bayonne, France, September 10-13: Proceedings. 64-69.
Available at: https://ink.library.smu.edu.sg/sis_research/5945
Copyright Owner and License
Publisher
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1109/WETICE49692.2020.00021