Dynamic resource reservation via broker federation in cloud service: A fine-grained heuristic-based approach

In cloud computing, Infrastructure-as-a-Service (IaaS) cloud providers can offer two types of purchasing plans for cloud users, including on-demand plan and reservation plan. Generally reservation price is cheaper than on-demand price, while reservation plan may cause highly underutilized capacity problem. How to joint optimize the service cost and the resource utilization for clouds is a critical issue. To address this issue, a novel steady broker federation is developed to coordinate service demands in this paper. And the optimal reservation problem can be formulated as a nonlinear integer programming model. Then a fine-grained heuristic algorithm is proposed to reduce its computational complexity and obtain quasi-optimal solutions. Numerical simulations driven by large-scale Parallel Workloads Archive demonstrate that the proposed approach can save considerable costs for cloud users and improves the resource utilization for IaaS cloud providers.

[1]  Dusit Niyato,et al.  A Framework for Cooperative Resource Management in Mobile Cloud Computing , 2013, IEEE Journal on Selected Areas in Communications.

[2]  Rajkumar Buyya,et al.  Article in Press Future Generation Computer Systems ( ) – Future Generation Computer Systems Cloud Computing and Emerging It Platforms: Vision, Hype, and Reality for Delivering Computing as the 5th Utility , 2022 .

[3]  Rajkumar Buyya,et al.  InterCloud: Utility-Oriented Federation of Cloud Computing Environments for Scaling of Application Services , 2010, ICA3PP.

[4]  Yang Song,et al.  Optimal bidding in spot instance market , 2012, 2012 Proceedings IEEE INFOCOM.

[5]  Chris Rose,et al.  A Break in the Clouds: Towards a Cloud Definition , 2011 .

[6]  Bu-Sung Lee,et al.  Optimization of Resource Provisioning Cost in Cloud Computing , 2012, IEEE Transactions on Services Computing.

[7]  Ramin Yahyapour,et al.  Cloud computing networking: challenges and opportunities for innovations , 2013, IEEE Communications Magazine.

[8]  Xiaoling Sun,et al.  Nonlinear Integer Programming , 2006 .

[9]  Rubén S. Montero,et al.  Scheduling strategies for optimal service deployment across multiple clouds , 2013, Future Gener. Comput. Syst..

[10]  Zhen Xiao,et al.  Dynamic Resource Allocation Using Virtual Machines for Cloud Computing Environment , 2013, IEEE Transactions on Parallel and Distributed Systems.

[11]  Randy H. Katz,et al.  A view of cloud computing , 2010, CACM.

[12]  Martin Bichler,et al.  A Mathematical Programming Approach for Server Consolidation Problems in Virtualized Data Centers , 2010, IEEE Transactions on Services Computing.

[13]  Raouf Boutaba,et al.  Cloud computing: state-of-the-art and research challenges , 2010, Journal of Internet Services and Applications.

[14]  Baochun Li,et al.  Dynamic Cloud Resource Reservation via Cloud Brokerage , 2013, 2013 IEEE 33rd International Conference on Distributed Computing Systems.

[15]  Mladen A. Vouk,et al.  Cloud Computing – Issues, Research and Implementations , 2008, CIT 2008.