In the data center network, traffic has a distinct heavy-tailed distribution characteristic, with the minority of throughput-sensitive elephant flows occupy most of the bandwidth and the majority of latency-sensitive mice flows require low latency. Therefore, it is very important to predict the network flow size and make a reasonable balanced scheduling. Currently, the traditional elephant flow detection schemes based on thresholds have poor accuracy and low granularity, while the intelligent detection schemes based on SDN has a certain flow scheduling response delay. For this reason, a two-stage online elephant flow prediction method for load balancing (OPLB) is proposed. Based on the programmable data plane, OPLB first pre-identifies the elephant flow by extracting the stateless features of the first packet of the network flow arriving at the switch. Secondly, the size of the elephant flow is predicted by extracting the features of the first${n}$packets of the flow. Finally, the detected elephant and mice flows are balanced to high throughput and low latency paths. Combined with the computing and storage capabilities of the programmable switch, the models and parameters in OPLB can be updated online by mapping the trained classification and prediction decision tree models to the matching-action pipelines of the programmable switch, thus achieving dynamic load balancing. We prototype OPLB in P4 software simulation environment and evaluate it with packet traces from the university data centers (UNI). The experiment shows that the accuracy of classification and prediction reached 89.3% when the proportion of elephant flow was 20%. At the same time, compared to the scheme that only uses single stage elephant flow prediction, OPLB reduces the amount of information collected by the switch by about 40% when the elephant flow proportion is 20%.

IN THE above article[1], in(5), the allocated bandwidth is corrected as

In order to achieve requirements such as fast search of flow entries and mask matching, OpenFlow hardware switches usually use TCAM to store flow entries. Limited by the capacity of TCAM, the current commercial OpenFlow switches can only support hundreds of thousands of flow entries, which makes SDN network using OpenFlow hardware switches vulnerable to the threat of flow table overflow attack. Among them, low-rate DoS (LDoS) attack against table overflow poses a serious threat to SDN networks due to its high attack efficiency and concealed flow, and it is also difficult to detect. In this regard, this paper analyzed two types of LDoS attack flow against table overflow and proposed an attack detection and defense mechanism named SAIA (Small-flow Analysis and Inport-flow Analysis) through the design of table overflow prediction and flow entries deletion strategy. Experiments conducted through the SDN network environment showed that SAIA can effectively detect and suppress LDoS attack flows in the flow table in large-scale network conditions and verified that the deployment of SAIA is lightweight. At the same time, SAIA implemented the flow entry deletion strategy based on LRU when the flow table overflows in a nonattack situation, which further enhances the stability of the network.

With the emerging paradigm of Virtual Network and Network Function Virtualization (NFV), the data center operator can flexibly manage the network to reduce Operating Expenditures (OPEX) and Capital Expenditures (CAPEX). Some new issues including the assignment of Virtual Network Functions (VNFs) and Service Function Chain (SFC) scheduling should be considered to apply in the communication. Mathematically, the SFC scheduling process can be formulated as a queuing system, and every server installed with VNF instances can be regarded as a service node, where batched requests are parallel submitted and processed one after another. In the management of SFC, the cloud service provider needs to resolve the problem of network congestion. Latency and throughput are two important but contradictory indexes in the network management, and both of them are deserved to be optimized during the scheduling of SFC requests. In this paper, different from most existing studies, we focus on the service rate control problem in the scheduling of SFC requests. Firstly, we formulate this problem as an integer programming problem. Through a cooperative game approach, a Nash bargaining based model is proposed to jointly optimize the latency and throughput, which is proven to provide fair performance guarantees by both theoretical analysis and simulation. To improve the scalability of the proposed algorithm, we also design a polynomial two-phase heuristic to perform Pareto optimization. Simulation evaluation shows that the proposed algorithm can implement balanced traffic scheduling and avoid excessive server latency caused by network congestion.