# Dynamic Traffic Scheduling and Congestion Control across Data Centers Based on SDN
## 2 Related Work
### Data Center Based on SDN  
A higher-level of visibility and a fine-grained control of the entire network can be achieved in the SDN paradigm. The SDN controller is able to program infrastructural forwarding devices in the data plane to monitor and manage network packets passing through these devices in a fine-grained way. Therefore, we can use the SDN controller to implement the periodic collection of these statistics. Furthermore, we can also obtain a centralized view of the network status for the SDN applications via open APIs and notify the up-level applications of a change in a real-time network [15]. Data centers are typically composed of thousands of high-speed links such as the 10 G Ethernet. In conventional packet capture mechanisms like switch port analyzer and port mirroring, infeasibility is completely reflected from a cost and scale perspective because a stupendous number of physical ports might be used. Adopting the SDN-based approaches to data center networks has become the focus of current research [1]. [PERSON] et al. [16] first applied SDN to data center networks by using NOX (the first SDN controller) to efficiently actualize addressing and routing of the typical data center networks VL2 and PortLand. [PERSON] [17] proposed HyperFlow, a new distributed control plan for OpenFlow. Multiple controllers cooperate with each other to make up for the low scalability of a single controller while the advantage of centralized management is retained. The cooperation of distributed controllers can realize the expansion of the network and conveniently manage network devices [18; 19; 20]. [PERSON] et al. [21] presented Onix, on which the SDN control plane could be implemented as a distributed system. [PERSON] et al. [22] proposed MicroTE, a fine-grained routing approach for data centers where he found a lack of multi-path routing, plus an overall view of workloads and routing strategies in data center networks based on a traditional network. [PERSON] et al. [23] presented Mesos, a platform for fine-grained resource sharing in data centers. [PERSON] [24] proposed Mahout to handle elephant flows (i.e., large flows) while large flows in Socket buffers were detected[PERSON] Due to the burst of the traffic and the failure of the equipment, congestion and failure frequently occur. Large flows and small flows are always mingled in data center networks. [PERSON] et al. [25] proposed a flow-based edge-to-edge rerouting scheme[PERSON] When congestions appeared, it focused on rerouting the large flows to alternative links[PERSON] The reason for this mechanism is that shifting these short-lived small flows among links would additionally increase the overhead and latency. [PERSON] et al. [26] provided a layer between a software-defined networking controller and network devices called VeriFlow. It is a network debugging tool to find faulty rules issued by SDN applications and anomalous network behavior. [PERSON] [27] presented the Baatdaat flow scheduling algorithm using spare data center network capacity to mitigate the performance degradation of heavily utilized links[PERSON] This could ensure real-time dynamic scheduling, which could avoid congestion caused by instantaneous flows. [PERSON] et al. [28] proposed a traffic scheduling solution based on the fuzzy synthetic evaluation mechanism, and the path could be dynamically adjusted according to the overall view of the network. In summary, most of the current active approaches solving traffic engineering in data center networks based on SDN have two alternatives.