海量网络服务实时自动分类系统的研究与实现

随着信息技术的发展,互联网数据急剧增长,导致网络服务的种类多种多样。由于网络服务内容多种多样,文本长度长短不一,使得传统分类方法难以有效解决大规模网页分类问题。文章设计并实现了一个面向海量网络服务的实时自动分类系统(AWCS),包括自反馈的系统体系结构、多维度多层次的网络服务分类标准、主动与被动方式相结合的网络服务发现采集技术、自修正的网络服务自动分类技术。性能测试显示,该系统的分类准确率明显高于已有算法。AWCS系统对构建实时大规模网络数据分类系统具有重要的参考和借鉴价值。

MULTI-CLASS TRAFFIC QOS ROUTING FOR LEO SATELLITE NETWORKS

Due to the diversified demands of quality of service（QoS） in volume multimedia application, QoS routings for multiservice are becoming a research hotspot in low earth orbit（LEO） satellite networks. A novel QoS satellite routing algorithm for multi-class traffic is proposed. The goal of the routing algorithm is to provide the distinct QoS for different traffic classes and improve the utilization of network resources. Traffic is classified into three classes and allocated priorities based on their QoS requirements, respectively. A priority queuing mechanism guarantees the algorithm to work better for high-priority classes. In order to control the congestion, a blocking probability analysis model is built up based on the Markov process theory. Finally, according to the classification link-cost metrics, routings for different classes are calculated with the distinct QoS requirments and the status of network resource. Simulations verify the performance of the routing algorithm at different time and in different regions, and results demonstrate that the algorithm has great advantages in terms of the average delay and the blocking probability. Meanwhile, the robustness issue is also discussed.

PPP： Towards Parallel Protocol Parsing

Network traffic classification plays an important role and benefits many practical network issues,such as Next-Generation Firewalls(NGFW),Quality of Service(QoS),etc.To face the challenges brought by modern high speed networks,many inspiring solutions have been proposed to enhance traffic classification.However,taking many factual network conditions into consideration,e.g.,diversity of network environment,traffic classification methods based on Deep Inspection(DI) technique still occupy the top spot in actual usage.In this paper,we propose a novel classification system employing Deep Inspection technique,aiming to achieve Parallel Protocol Parsing(PPP).We start with an analytical study of the existing popular DI methods,namely,regular expression based methods and protocol parsing based methods.Motivated by their relative merits,we extend traditional protocol parsers to achieve parallel matching,which is the representative merit of regular expression.We build a prototype system,and evaluation results show that significant improvement has been made comparing to existing open-source solutions in terms of both memory usage and throughput.

Network-based naive Bayes model for social network

Naive Bayes(NB) is one of the most popular classification methods. It is particularly useful when the dimension of the predictor is high and data are generated independently. In the meanwhile, social network data are becoming increasingly accessible, due to the fast development of various social network services and websites. By contrast, data generated by a social network are most likely to be dependent. The dependency is mainly determined by their social network relationships. Then, how to extend the classical NB method to social network data becomes a problem of great interest. To this end, we propose here a network-based naive Bayes(NNB) method, which generalizes the classical NB model to social network data. The key advantage of the NNB method is that it takes the network relationships into consideration. The computational efficiency makes the NNB method even feasible in large scale social networks. The statistical properties of the NNB model are theoretically investigated. Simulation studies have been conducted to demonstrate its finite sample performance.A real data example is also analyzed for illustration purpose.