The continuous expansion in scale of multi-field entries and the growing increase in bit-width bring heavy storage pressure in hardware on the Internet. In order to solve this problem, a compression method based on Bit Extraction of Independent rule Subsets (BEIS) was proposed. Firstly, some fields were merged based on the logical relationships among multiple match fields, thus reducing the number of match fields and the width of flow tables. Secondly, with the division of independent rule subsets for the merged rule set, some differentiate bits in the divided subsets were extracted to achieve the matching and searching function, further reducing the used Ternary Content Addressable Memory (TCAM) space. Finally, the lookup hardware architecture of this method was put forward. Simulation results show that, with certain time complexity, the storage space of the proposed method can be reduced by 20% compared with Field Trimmer (FT) in OpenFlow flow table; in addition, for common packet classification rule sets such as access control list and firewall in practical application, the compression ratio of 20%-40% can be achieved.

To solve the problem existing in traffic classification that Peer-to-Peer (P2P) traffic is much more than that of non-P2P, a new traffic classification method for imbalanced network data was presented. By introducing and improving Synthetic Minority Over-sampling Technique (SMOTE) algorithm, a Mean SMOTE (M-SMOTE) algorithm was proposed to realize the balance of traffic data. On the basis of this, three kinds of machine learning classifiers:Random Forest (RF), Support Vector Machine (SVM), Back Propagation Neural Network (BPNN) were used to identify the various types of traffic. The theoretical analysis and simulation results show that, compared with the imbalanced state, the SMOTE algorithm improves the recognition accuracy of non-P2P traffic by 16.5 percentage points and raises the overall recognition rate of network traffic by 9.5 percentage points. Compared with SMOTE algorithm, the M-SMOTE algorithm further improves the recognition rate of non-P2P traffic and the overall recognition rate of network traffic by 3.2 percentage points and 2.6 percentage points respectively. The experimental results show that the way of imbalanced data classification can effectively solve the problem of low P2P traffic recognition rate caused by excessive P2P traffic, and the M-SMOTE algorithm has higher recognition accuracy rate than SMOTE.

Aiming at the lack of semantic description for Service-Level Agreement (SLA) elements used in negotiation and the negotiation process in the SLA auto-negotiation, a negotiation mechanism based on the semantic Web technology was proposed, At first, a negotiation ontology named Osn was proposed, which was used for the description of SLA elements directly used in negotiation;the mapping function and the evaluation function of negotiation for these SLA elements were designed and described in this Osn, and the formal description of the main concepts and the relationship between these concepts was given based on description logic to provide a satisfiable semantic model for the Osn. Then a bargain model was put forward for SLA negotiation, and it was illustrated that a Pareto optimal offer could be generated by adopting this model through the proof of the related proposition and theorem;the service ontology was designed for SLA negotiation based on the mapping between OWL-S and Unified Modeling Language (UML) using this bargain model. The result of case study shows that the knowledge can form the sequence of offers which satisfied the need to maximize the interest of negotiation participants. It is illustrated that Osn can provide the service ontology with the parameter type support for the negotiation of an arbitrary SLA;the SLA negotiation oriented bargain model can generate the SLA accepted by both negotiation participants.

Minimum Steiner tree problem is a NP complete problem, and widely used in communication network point to multi-point routing. In order to realize more link sharing, reduce the cost of the desired Steiner tree, an algorithm named NWMPH (Node Weight based Minimum cost Path Heuristic) was proposed to solve the Steiner tree based on weighted node. The algorithm constructed a weighted formula of nonregular points, for each nonregular point weighting value. According to the weights of modifying the link cost. By modifying the cost shortest path in order to connect all regular points, get the minimum tree containing all regular points. For part of the data to calculate STEINLIB standard data set, the results show that: NWMPH algorithm and MPH algorithm used basically the same time. The cost of NWMPH algorithm to get Steiner tree is less than that of MPH algorithm. NWMPH algorithm uses less time and costs less to get Steiner tree than KBMPH algorithm.