Top-k ranking of websites according to traffic volume is important for Internet Service Providers(ISPs) to understand network status and optimize network resources. However, the ranking result always has a big deviati...Top-k ranking of websites according to traffic volume is important for Internet Service Providers(ISPs) to understand network status and optimize network resources. However, the ranking result always has a big deviation with actual rank for the existence of unknown web traffic, which cannot be identified accurately under current techniques. In this paper, we introduce a novel method to approximate the actual rank. This method associates unknown web traffic with websites according to statistical probabilities. Then, we construct a probabilistic top-k query model to rank websites. We conduct several experiments by using real HTTP traffic traces collected from a commercial ISP covering an entire city in northern China. Experimental results show that the proposed techniques can reduce the deviation existing between the ground truth and the ranking results vastly. In addition, we find that the websites providing video service have higher ratio of unknown IP as well as higher ratio of unknown traffic than the websites providing text web page service. Specifically, we find that the top-3 video websites have more than 90% of unknown web traffic. All these findings are helpful for ISPs understanding network status and deploying Content Distributed Network(CDN).展开更多
In modern wireless communication network, the increased consumer demands for multi-type applications and high quality services have become a prominent trend, and put considerable pressure on the wireless network. In t...In modern wireless communication network, the increased consumer demands for multi-type applications and high quality services have become a prominent trend, and put considerable pressure on the wireless network. In that case, the Quality of Experience(Qo E) has received much attention and has become a key performance measurement for the application and service. In order to meet the users' expectations, the management of the resource is crucial in wireless network, especially the Qo E based resource allocation. One of the effective way for resource allocation management is accurate application identification. In this paper, we propose a novel deep learning based method for application identification. We first analyse the requirement of managing Qo E for wireless communication, and review the limitation of the traditional identification methods. After that, a deep learning based method is proposed for automatically extracting the features and identifying the type of application. The proposed method is evaluated by using the practical wireless traffic data, and the experiments verify the effectiveness of our method.展开更多
The network resource allocation in SDN for control applications is becoming a key problem in the near future because of the conflict between the need of the flow-level flexibility control and the limited capacity of f...The network resource allocation in SDN for control applications is becoming a key problem in the near future because of the conflict between the need of the flow-level flexibility control and the limited capacity of flow table.Based on the analysis of the difference of the definition of network resource between SDN and traditional IP network,the idea of the integrated allocation of link bandwidth and flow table for multiple control applications in SDN is proposed in this paper.Furthermore,a price-based joint allocation model of network resource in SDN is built by introducing the price for each of the resources,which can get the proportional fair allocation of link bandwidth and the minimum global delay at the same time.We have also designed a popular flow scheduling policy based on the proportional fair allocation of link bandwidth in order to achieve the minimum global delay.A flow scheduling module has been implemented and evaluated in Floodlight,named virtual forwarding space(VFS).VFS can not only implement the fair allocation of link bandwidth and minimum delay flow scheduling in data plane but also accelerate packet forwarding by looking up control rules in control plane.展开更多
基金supported by 111 Project of China under Grant No.B08004
文摘Top-k ranking of websites according to traffic volume is important for Internet Service Providers(ISPs) to understand network status and optimize network resources. However, the ranking result always has a big deviation with actual rank for the existence of unknown web traffic, which cannot be identified accurately under current techniques. In this paper, we introduce a novel method to approximate the actual rank. This method associates unknown web traffic with websites according to statistical probabilities. Then, we construct a probabilistic top-k query model to rank websites. We conduct several experiments by using real HTTP traffic traces collected from a commercial ISP covering an entire city in northern China. Experimental results show that the proposed techniques can reduce the deviation existing between the ground truth and the ranking results vastly. In addition, we find that the websites providing video service have higher ratio of unknown IP as well as higher ratio of unknown traffic than the websites providing text web page service. Specifically, we find that the top-3 video websites have more than 90% of unknown web traffic. All these findings are helpful for ISPs understanding network status and deploying Content Distributed Network(CDN).
基金supported by NSAF under Grant(No.U1530117)National Natural Science Foundation of China(No.61471022 and No.61201156)
文摘In modern wireless communication network, the increased consumer demands for multi-type applications and high quality services have become a prominent trend, and put considerable pressure on the wireless network. In that case, the Quality of Experience(Qo E) has received much attention and has become a key performance measurement for the application and service. In order to meet the users' expectations, the management of the resource is crucial in wireless network, especially the Qo E based resource allocation. One of the effective way for resource allocation management is accurate application identification. In this paper, we propose a novel deep learning based method for application identification. We first analyse the requirement of managing Qo E for wireless communication, and review the limitation of the traditional identification methods. After that, a deep learning based method is proposed for automatically extracting the features and identifying the type of application. The proposed method is evaluated by using the practical wireless traffic data, and the experiments verify the effectiveness of our method.
基金Supported by the National High-tech R&D Program("863" Program) of China (No.2013AA013505)the National Science Foundation of China(No.61472213)National Research Foundation of Korea(NRF 2014K1A1A2064649)
文摘The network resource allocation in SDN for control applications is becoming a key problem in the near future because of the conflict between the need of the flow-level flexibility control and the limited capacity of flow table.Based on the analysis of the difference of the definition of network resource between SDN and traditional IP network,the idea of the integrated allocation of link bandwidth and flow table for multiple control applications in SDN is proposed in this paper.Furthermore,a price-based joint allocation model of network resource in SDN is built by introducing the price for each of the resources,which can get the proportional fair allocation of link bandwidth and the minimum global delay at the same time.We have also designed a popular flow scheduling policy based on the proportional fair allocation of link bandwidth in order to achieve the minimum global delay.A flow scheduling module has been implemented and evaluated in Floodlight,named virtual forwarding space(VFS).VFS can not only implement the fair allocation of link bandwidth and minimum delay flow scheduling in data plane but also accelerate packet forwarding by looking up control rules in control plane.
