Browse > Article
http://dx.doi.org/10.3745/KTCCS.2014.3.7.219

Cost Efficient Virtual Machine Brokering in Cloud Computing  

Kang, Dong-Ki (한국과학기술원 전기및전자공학과)
Kim, Seong-Hwan (한국과학기술원 전기및전자공학과)
Youn, Chan-Hyun (한국과학기술원 전기및전자공학과)
Publication Information
KIPS Transactions on Computer and Communication Systems / v.3, no.7, 2014 , pp. 219-230 More about this Journal
Abstract
In the cloud computing environment, cloud service users purchase and use the virtualized resources from cloud resource providers on a pay as you go manner. Typically, there are two billing plans for computing resource allocation adopted by large cloud resource providers such as Amazon, Gogrid, and Microsoft, on-demand and reserved plans. Reserved Virtual Machine(VM) instance is provided to users based on the lengthy allocation with the cheaper price than the one of on-demand VM instance which is based on shortly allocation. With the proper mixture allocation of reserved and on-demand VM corresponding to users' requests, cloud service providers are able to reduce the resource allocation cost. To do this, prior researches about VM allocation scheme have been focused on the optimization approach with the users' request prediction techniques. However, it is difficult to predict the expected demands exactly because there are various cloud service users and the their request patterns are heavily fluctuated in reality. Moreover, the previous optimization processing techniques might require unacceptable huge time so it is hard to apply them to the current cloud computing system. In this paper, we propose the cloud brokering system with the adaptive VM allocation schemes called A3R(Adaptive 3 Resource allocation schemes) that do not need any optimization processes and kinds of prediction techniques. By using A3R, the VM instances are allocated to users in response to their service demands adaptively. We demonstrate that our proposed schemes are able to reduce the resource use cost significantly while maintaining the acceptable Quality of Service(QoS) of cloud service users through the evaluation results.
Keywords
Cloud Broker; Virtual Machine Allocation; Adaptive Resource Management; Resource Reservation;
Citations & Related Records
연도 인용수 순위
  • Reference
1 S. Chaisiri, B. Lee, and D. Niyato, "Optimal Virtual Machine Placement across Multiple Cloud Providers," Proc. IEEE Asia-Pacific Services Computing Conf. (APSCC), 2009.
2 S. Chaisiri, B. -S. Lee, and D. Niyato, "Optimization of Resource Provisioning Cost in Cloud Computing," IEEE Trans. Services Computing, Vol.5, No.2, pp.164-177, Apr., 2012.   DOI
3 Amazon EC2 (2013), http://aws.amazon.com/ec2/
4 GoGrid (2013), http://www.gogrid.com/
5 C. Mark, D. Niyato, and T. Chen-Khong, "Evolutionary Optimal Virtual Machine Placement and Demand Forecaster for Cloud Computing," Proc. IEEE Int'l Conf on Advanced Information Networking and Apps. (ICAINA), 2011.
6 S. Son, and K. Sim, "A Price- and-Time-Slot-Negotiation Mechanism for Cloud Service Reservations," IEEE Trans. Systems, Man, and Cybernetics-Part B: Cybernetics, Vol.42, No.3, pp.713-728, June, 2012.   DOI
7 R. Buyya, C.S. Yeo, and S. Venugopal, "Market-oriented cloud computing: Vision, hype, and reality for delivering IT services as computing utilites," Proc. IEEE Int'l Conf on High Performance Computing and Communications. (HPCC), 2008.
8 J. Simarro, R. Vozmediano, R. Montero, and I. Llorente, "Dynamic Placement of Virtual Machines for Cost Optimization in Multi-CLoud Environments," Proc. IEEE Int'l Conf on High Performance Computing and Simulation. (HPCS), 2011.
9 R. Jeyarani, N. Nagaveni, and R. Vasanth Ram, "Design and implementation of adaptive power-aware virtual machine provisioner(APA-VMP) using swarm intelligence," Elsvier, Future Generation Computer Systems, Vol.28, Issue.5, pp.811-821, May, 2012.   DOI
10 OpenStack Foundation, http://www.openstack.org/, 2013.
11 D. Kang, S. Kim, Y. Ren, B. Kim, W. Kim, Y. Kim, C. Youn, and C. Jeong, "Enhancing a Strategy of Virtualized Resource Assignment in Adaptive Resource Cloud Framework," Proc. ACM Int'l Conf on Ubiquitous Information Management and Communication. (ICUIMC), 2013.
12 H. Zhang, G. Jiang, K. Yoshihira, H. Chen, and A. Szxena, "Intelligent Workload Factoring for a Hybrid Cloud Computing Model," Proc, Congress on Services-I, 2009.
13 S. Chaisiri, B. Lee, and D. Niyato, "Optimal Virtual Machine Placement across Multiple Cloud Providers," Proc, IEEE Int'l Conf on Asia-Pacific Services Computing Conference (APSCC), 2009.
14 Q. Zhang, Q. Zhu, and R. Boutaba, "Dynamic Resource Allocation for Spot Markets in Cloud Compputing Environments," Proc, IEEE Int'l Conf on Utility and Cloud Computing, 2011.