Dynamic Traffic Scheduling and Congestion Control across Data Centers Based on SDN

Software-defined Networking (SDN) and Data Center Network (DCN) are receiving considerable attention and eliciting widespread interest from both academia and industry. When the traditionally shortest path routing protocol among multiple data centers is used, congestion will frequently occur in the shortest path link, which may severely reduce the quality of network services due to long delay and low throughput. The flexibility and agility of SDN can effectively ameliorate the aforementioned problem. However, the utilization of link resources across data centers is still insufficient, and has not yet been well addressed. In this paper, we focused on this issue and proposed an intelligent approach of real-time processing and dynamic scheduling that could make full use of the network resources. The traffic among the data centers could be classified into different types, and different strategies were proposed for these types of real-time traffic. Considering the prolonged occupation of the bandwidth by malicious flows, we employed the multilevel feedback queue mechanism and proposed an effective congestion control algorithm. Simulation experiments showed that our scheme exhibited the favorable feasibility and demonstrated a better traffic scheduling effect and great improvement in bandwidth utilization across data centers.

[1]  Violet R. Syrotiuk,et al.  Load Balancing in a Campus Network Using Software Defined Networking , 2014, 2014 Third GENI Research and Educational Experiment Workshop.

[2]  Kuochen Wang,et al.  Dynamic load-balanced path optimization in SDN-based data center networks , 2016, 2016 10th International Symposium on Communication Systems, Networks and Digital Signal Processing (CSNDSP).

[3]  Dan Li,et al.  A survey of network update in SDN , 2017, Frontiers of Computer Science.

[4]  Fung Po Tso,et al.  Baatdaat: Measurement-based flow scheduling for cloud data centers , 2013, 2013 IEEE Symposium on Computers and Communications (ISCC).

[5]  Sanjay Jha,et al.  A Survey of Securing Networks Using Software Defined Networking , 2015, IEEE Transactions on Reliability.

[6]  Sakir Sezer,et al.  Queen ' s University Belfast-Research Portal Are We Ready for SDN ? Implementation Challenges for Software-Defined Networks , 2016 .

[7]  MengChu Zhou,et al.  Routing in Internet of Vehicles: A Review , 2015, IEEE Transactions on Intelligent Transportation Systems.

[8]  Rong Pan,et al.  Data center transport mechanisms: Congestion control theory and IEEE standardization , 2008, 2008 46th Annual Allerton Conference on Communication, Control, and Computing.

[9]  Min Zhu,et al.  B4: experience with a globally-deployed software defined wan , 2013, SIGCOMM.

[10]  Jun Bi,et al.  WEBridge: west-east bridge for distributed heterogeneous SDN NOSes peering , 2015, Secur. Commun. Networks.

[11]  Jianbin Qiu,et al.  T-S fuzzy-model-based piecewise H∞ output feedback controller design for networked nonlinear systems with medium access constraint , 2014, Fuzzy Sets Syst..

[12]  Martín Casado,et al.  Onix: A Distributed Control Platform for Large-scale Production Networks , 2010, OSDI.

[13]  Praveen Yalagandula,et al.  Mahout: Low-overhead datacenter traffic management using end-host-based elephant detection , 2011, 2011 Proceedings IEEE INFOCOM.

[14]  GhemawatSanjay,et al.  The Google file system , 2003 .

[15]  Yonggang Wen,et al.  “ A Survey of Software Defined Networking , 2020 .

[16]  Thierry Turletti,et al.  A Survey of Software-Defined Networking: Past, Present, and Future of Programmable Networks , 2014, IEEE Communications Surveys & Tutorials.

[17]  Yang Peng,et al.  Field demonstration of datacenter resource migration via multi-domain software defined transport networks with multi-controller collaboration , 2014, OFC 2014.

[18]  Jie Cui,et al.  Reprint of "LBBSRT: An efficient SDN load balancing scheme based on server response time" , 2018, Future Gener. Comput. Syst..

[19]  Albert G. Greenberg,et al.  The cost of a cloud: research problems in data center networks , 2008, CCRV.

[20]  Bu-Sung Lee,et al.  OpenFlow based control for re-routing with differentiated flows in Data Center Networks , 2012, 2012 18th IEEE International Conference on Networks (ICON).

[21]  Yashar Ganjali,et al.  HyperFlow: A Distributed Control Plane for OpenFlow , 2010, INM/WREN.

[22]  Jie Cui,et al.  LBBSRT: An efficient SDN load balancing scheme based on server response time , 2017, Future Gener. Comput. Syst..

[23]  Nick Feamster,et al.  Improving network management with software defined networking , 2013, IEEE Commun. Mag..

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

[25]  Jianbin Qiu,et al.  Reliable Output Feedback Control for T-S Fuzzy Systems With Decentralized Event Triggering Communication and Actuator Failures , 2017, IEEE Transactions on Cybernetics.

[26]  Randy H. Katz,et al.  Mesos: A Platform for Fine-Grained Resource Sharing in the Data Center , 2011, NSDI.

[27]  Martín Casado,et al.  Applying NOX to the Datacenter , 2009, HotNets.

[28]  Chuang Lin,et al.  Analysing and improving convergence of quantized congestion notification in Data Center Ethernet , 2018, Comput. Networks.

[29]  Hairong Kuang,et al.  The Hadoop Distributed File System , 2010, 2010 IEEE 26th Symposium on Mass Storage Systems and Technologies (MSST).

[30]  Ming Zhang,et al.  MicroTE: fine grained traffic engineering for data centers , 2011, CoNEXT '11.

[31]  Laizhong Cui,et al.  When big data meets software-defined networking: SDN for big data and big data for SDN , 2016, IEEE Network.

[32]  Jun Li,et al.  An Effective Path Load Balancing Mechanism Based on SDN , 2014, 2014 IEEE 13th International Conference on Trust, Security and Privacy in Computing and Communications.

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

[34]  Nirwan Ansari,et al.  Software-defined network virtualization: an architectural framework for integrating SDN and NFV for service provisioning in future networks , 2016, IEEE Network.

[35]  Brighten Godfrey,et al.  VeriFlow: verifying network-wide invariants in real time , 2012, HotSDN '12.