Fine-Grained, Adaptive Resource Sharing for Real Pay-Per-Use Pricing in Clouds
- Authors
- Lee, Young Choon; Kim, Youngjin; Han, Hyuck; Kang, Sooyong
- Issue Date
- Sep-2015
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Keywords
- Cloud computing; containerization; fine-grained resource sharing; pay-per-use pricing
- Citation
- Proceedings - 2015 International Conference on Cloud and Autonomic Computing, ICCAC 2015, pp.236 - 243
- Indexed
- SCOPUS
- Journal Title
- Proceedings - 2015 International Conference on Cloud and Autonomic Computing, ICCAC 2015
- Start Page
- 236
- End Page
- 243
- URI
- https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/156415
- DOI
- 10.1109/ICCAC.2015.36
- ISSN
- 0000-0000
- Abstract
- Cloud computing is characterized by its essentially pay-per-use pricing with elasticity. Typically, the granularity of usage for such pricing is at virtual machine (VM) level in IaaS clouds, e.g., a multiple of machine hours. The elasticity and cost effectiveness in these clouds are primarily achieved through the exploitation of resource virtualization and sharing. However, a majority of applications running on VMs in clouds struggle to fully utilize resources allocated to them. Since co-location granularity is strictly restricted to VM level and resources allocated to VMs are space-shared, the unused resources are apt to be wasted while users are still charged for such wastage. In this paper, we address the problem of fine-grained and adaptive resource sharing for real pay-per-use pricing. To this end, we devise a resource management mechanism as a cost efficiency solution for both users and providers of clouds. The mechanism consists of a container-based resource allocator and a real-usage based pricing scheme. We demonstrate the efficacy of this mechanism via experiments, in Amazon EC2, using two typical workloads in clouds, web services and database services, and a compute-intensive high energy physics application. Our results show that the mechanism can achieve near-optimal cost efficiency.
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