Distributed Resource Allocation for Data Center Networks: A Hierarchical Game Approach

被引:24
作者
Zhang, Huaqing [1 ]
Xiao, Yong [2 ]
Bu, Shengrong [3 ]
Yu, Richard [4 ]
Niyato, Dusit [5 ]
Han, Zhu [1 ]
机构
[1] Univ Houston, Dept Elect & Comp Engn, Houston, TX 77004 USA
[2] Univ Arizona, Dept Elect & Comp Engn, Tucson, AZ 85721 USA
[3] Univ Glasgow, Sch Engn, Glasgow G12 8QQ, Lanark, Scotland
[4] Carleton Univ, Dept Syst & Comp Engn, Ottawa, ON K1S 5B6, Canada
[5] Nanyang Technol Univ NTU, Sch Comp Engn, Singapore 639798, Singapore
基金
美国国家科学基金会;
关键词
Data center; hierarchical game; game theory; resource management;
D O I
10.1109/TCC.2018.2829744
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The increasing demand of data computing and storage for cloud-based services motivates the development and deployment of large-scale data centers. This paper studies the resource allocation problem for the data center networking system when multiple data center operators (DCOs) simultaneously serve multiple service subscribers (SSs). We formulate a hierarchical game to analyze this system where the DCOs and the SSs are regarded as the leaders and followers, respectively. In the proposed game, each SS selects its serving DCO with preferred price and purchases the optimal amount of resources for the SS's computing requirements. Based on the responses of the SSs' and the other DCOs', the DCOs decide their resource prices so as to receive the highest profit. When the coordination among DCOs is weak, we consider all DCOs are noncooperative with each other, and propose a sub-gradient algorithm for the DCOs to approach a sub-optimal solution of the game. When all DCOs are sufficiently coordinated, we formulate a coalition game among all DCOs and apply Kalai-Smorodinsky bargaining as a resource division approach to achieve high utilities. Both solutions constitute the Stackelberg Equilibrium. The simulation results verify the performance improvement provided by our proposed approaches.
引用
收藏
页码:778 / 789
页数:12
相关论文
共 46 条
  • [31] Liu L., Ren S., Han Z., Scalable workload management for water efficiency in data centers, Proc Ieee Global Commun. Conf., pp. 2504-2509, (2014)
  • [32] Curtis A.R., Carpenter T., Elsheikh M., Ortiz A.L., Keshav S., REWIRE: An optimization-based framework for unstructured data center network design, Proc Ieee Infocom, pp. 1116-1124, (2012)
  • [33] Chen C.-J., Liu Y.-S., Chang R.-G., DCSim: Design analysis on virtualization data center, Proc. 9th Int. Conf. Ubiquitous Intell. Comput. Autonomic Trusted Comput., pp. 900-905, (2012)
  • [34] Caron E., Desprez F., Loureiro D., Muresan A., Cloud computing resource management through a grid middleware: A case study with DIET and Eucalyptus, Proc Ieee Int. Conf. Cloud Comput., pp. 151-154, (2009)
  • [35] Gandhi A., Harchol-Balter M., Das R., Lefurgy C., Optimal power allocation in server farms, Acm Sigmetrics Perform. Eval. Rev, 37, 1, pp. 157-168, (2009)
  • [36] Liu Z., Lin M., Wierman A., Low S.H., Andrew L.L., Geographical load balancing with renewables, Acm Sigmetrics Perform. Eval. Rev, 39, 3, pp. 62-66, (2011)
  • [37] Ren S., He Y., COCA: Online distributed resource management for cost minimization and carbon neutrality in data centers, Proc. Acm Int. Conf. High Perform. Comput. Netw. Storage Anal., pp. 1-2, (2013)
  • [38] Zhang H., Bennis M., DaSilva L.A., Han Z., Multi-leader multifollower stackelberg game among Wi-Fi, small cell and macrocell networks, Proc Ieee Global Commun. Conf., pp. 4520-4524, (2014)
  • [39] Chen Y., Zhang J., Zhang Q., Utility-Aware refunding framework for hybrid access femtocell network, Ieee Trans. Wireless Comm, 11, 5, pp. 1688-1697, (2012)
  • [40] Zhang H., Bennis M., DaSilva L.A., Han Z., Multi-leader multi-follower stackelberg game among Wi-Fi, small cell and macrocell networks, Proc Ieee Global Commun. Conf., pp. 4520-4524, (2014)