Abstract
In data center networks, resource allocation based on workload is an effective way to allocate the infrastructure resources to diverse cloud applications and satisfy the quality of service for the users, which refers to mapping a large number of workloads provided by cloud users/tenants to substrate network provided by cloud providers. Although the existing heuristic approaches are able to find a feasible solution, the quality of the solution is not guaranteed. Concerning this issue, based on the minimum mapping cost, this paper solves the resource allocation problem by modeling it as a distributed constraint optimization problem. Then an efficient approach is proposed to solve the resource allocation problem, aiming to find a feasible solution and ensuring the optimality of the solution. Finally, theoretical analysis and extensive experiments have demonstrated the effectiveness and efficiency of our proposed approach.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Shieh A, Kandula S, Greenberg A, et al. Sharing the data center network. In: Proceedings of the 8th USENIX Symposium on Networked Systems Design and Implementation, Boston, 2011. 22–30
Guo C, Lu G, Wang H, et al. Secondnet: a data center network virtualization architecture with bandwidth guarantees. In: Proceedings of the 6th International Conference on Emerging Networking Experiments and Technologies, Philadelphia, 2010. 7–14
Gar_nkel S. An evaluation of amazons grid computing services: EC2, S3, and SQS. Center for Citeseer, 2007
Wang G, Ng T. The impact of virtualization on network performance of amazon EC2 data center. In: Proceedings of IEEE INFOCOM, San Diego, 2010. 1–9
Bansal N, Lee K, Nagarajan V, et al. Minimum congestion mapping in a cloud. In: Proceedings of the 30th Annual ACM SIGACT-SIGOPS Symposium on Principles of Distributed Computing, San Jose, 2011. 267–276
Gupta A, Kleinberg J, Kumar A, et al. Provisioning a virtual private network: a network design problem for multicommodity flow. In: Proceedings of the 33rd Annual ACM symposium on Theory of Computing, Creta, 2001. 389–398
Yu M, Yi Y, Rexford J, et al. Rethinking virtual network embedding: substrate support for path splitting and migration. ACM SIGCOMM Comput Commun Rev, 2008, 38: 17–29
Zhu Y, Ammar M. Algorithms for assigning substrate network resources to virtual network components. In: Proceedings of IEEE International Conference on Computer Communications, Barcelona, 2006. 12–24
Lu J, Turner J. Efficient Mapping of Virtual Networks onto a Shared Substrate. Technical Report, Washington University in St. Louis, 2006
Fan J, Ammar M. Dynamic topology configuration in service overlay networks: a study of reconfiguration policies. In: Proceedings of IEEE International Conference on Computer Communications, Barcelona, 2006. 1–12
Cheng X, Su S, Zhang Z, et al. Virtual network embedding through topology-aware node ranking. ACM SIGCOMM Comput Commun Rev, 2011, 41: 38–47
Houidi I, Louati W, Zeghlache D. A distributed virtual network mapping algorithm. In: Proceedings of IEEE International Conference on Communications, Beijing, 2008. 5634–5640
Ricci R, Alfeld C, Lepreau J. A solver for the network testbed mapping problem. ACM SIGCOMM Comput Commun Rev, 2003, 33: 65–81
Lischka J, Karl H. A virtual network mapping algorithm based on subgraph isomorphism detection. In: Proceedings of the 1st ACM Workshop on Virtualized Infrastructure Systems and Architectures, Barcelona, 2009. 81–88
Chowdhury N, Rahman M, Boutaba R. Virtual network embedding with coordinated node and link mapping. In: Proceedings of IEEE International Conference on Computer Communications, Rio de Janeiro, 2009. 783–791
Modi P, Shen W, Tambe M, et al. Adopt: asynchronous distributed constraint optimization with quality guarantees. Artif Intell, 2005, 161: 149–180
Petcu A. A class of algorithms for distributed constraint optimization. Dissertation of Doctoral Degree. Ecole Polytechnique Federale De Lausanne, 2007
Hirayama K, Yokoo M. An approach to overconstrained distributed constraint satisfaction problems: distributed hierarchical constraint satisfaction. In: Proceedings of the 4th International Conference on Multiagent Systems, Boston, 2000. 135–142
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Li, X., Wang, H., Ding, B. et al. MABP: an optimal resource allocation approach in data center networks. Sci. China Inf. Sci. 57, 1–16 (2014). https://doi.org/10.1007/s11432-014-5164-y
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11432-014-5164-y