Achieving Application-Level Utility Max-Min Fairness of Bandwidth Allocation in Datacenter Networks

Providing fair bandwidth allocation for applications is becoming increasingly compelling in cloud datacenters as different applications compete for shared datacenter network resources. Existing solutions mainly provide bandwidth guarantees for virtual machines (VMs) and achieve the fairness of VM bandwidth allocation. However, scant attention has been paid to application bandwidth guarantees for the fairness of application performance. In this paper, we introduce a rigorous definition of application-level utility max-min fairness, which guides us to develop a non-linear model to investigate the relationship between the fairness of application performance (utility) and the application bandwidth allocation. Based on Newton’s method, we further design a simple yet effective algorithm to solve this problem, and evaluate its effectiveness with extensive experiments using OpenFlow in Mininet virtual network environment. Evaluation results show that our algorithm can achieve utility max-min fair share of bandwidth allocation for applications in datacenter networks, yet with an acceptable computational overhead.

[1]  Albert G. Greenberg,et al.  Sharing the Data Center Network , 2011, NSDI.

[2]  Nick McKeown,et al.  OpenFlow: enabling innovation in campus networks , 2008, CCRV.

[3]  Hitesh Ballani,et al.  Towards predictable datacenter networks , 2011, SIGCOMM 2011.

[4]  Sujata Banerjee,et al.  Application-driven bandwidth guarantees in datacenters , 2015, SIGCOMM.

[5]  Frank Kelly,et al.  Rate control for communication networks: shadow prices, proportional fairness and stability , 1998, J. Oper. Res. Soc..

[6]  Alex C. Snoeren,et al.  Inside the Social Network's (Datacenter) Network , 2015, Comput. Commun. Rev..

[7]  Marimuthu Palaniswami,et al.  Application-Oriented Flow Control: Fundamentals, Algorithms and Fairness , 2006, IEEE/ACM Transactions on Networking.

[8]  Hai Jin,et al.  Building a network highway for big data: architecture and challenges , 2014, IEEE Network.

[9]  Dimitri P. Bertsekas,et al.  Data networks (2nd ed.) , 1992 .

[10]  Hai Jin,et al.  On efficient bandwidth allocation for traffic variability in datacenters , 2014, IEEE INFOCOM 2014 - IEEE Conference on Computer Communications.

[11]  Gautam Kumar,et al.  A Case for Performance-Centric Network Allocation , 2012, HotCloud.

[12]  Amin Vahdat,et al.  A scalable, commodity data center network architecture , 2008, SIGCOMM '08.

[13]  Ion Stoica,et al.  FairCloud: sharing the network in cloud computing , 2011, SIGCOMM '12.

[14]  S. Shenker Fundamental Design Issues for the Future Internet , 1995 .

[15]  Ellen W. Zegura,et al.  Utility max-min: an application-oriented bandwidth allocation scheme , 1999, IEEE INFOCOM '99. Conference on Computer Communications. Proceedings. Eighteenth Annual Joint Conference of the IEEE Computer and Communications Societies. The Future is Now (Cat. No.99CH36320).

[16]  Hai Jin,et al.  Falloc: Fair network bandwidth allocation in IaaS datacenters via a bargaining game approach , 2013, 2013 21st IEEE International Conference on Network Protocols (ICNP).

[17]  Bo Li,et al.  Towards performance-centric fairness in datacenter networks , 2014, IEEE INFOCOM 2014 - IEEE Conference on Computer Communications.

[18]  Sanjay Ghemawat,et al.  MapReduce: Simplified Data Processing on Large Clusters , 2004, OSDI.

[19]  Athanasios V. Vasilakos,et al.  Managing Performance Overhead of Virtual Machines in Cloud Computing: A Survey, State of the Art, and Future Directions , 2014, Proceedings of the IEEE.