To address the low matching rate of flow table, an intelligent update method for flow table in Software Defined Network (SDN) switch was proposed. First, the impact of timeout value on the packet matching was described, besides, the shortcomings of First In First Out (FIFO), Least Recently Used (LRU) and other common methods were analyzed and compared. Secondly, based on the reality of survival time of the flow entry related closely to the characteristics of data flow, the Hidden Markov Model (HMM)-based Deep Flow Inspection (DFI) technology was used to classify the data flow. Finally, according to the condition of the flow table resources and controller's computing resources, the intelligent update of the flow entry of different type of data flow was realized. The simulation experiments conducted on data center behavior data of real campus indicate that the proposed method can improve more than 5% of the matching rate compared with the common methods, and it has a practical significance to the management of the SDN switch.

In wide-area Software Defined Networking (SDN) deployments, logically centralized controllers are usually physically placed in a distributed manner. To address the unbalanced controller load problem in controller placement, a load-aware optimal placement for heterogeneous controllers based on unbalanced graph partitioning was proposed. First, the controller placement problem and related metrics, controller load balance and latency, were analyzed. Secondly, controller load balance and latency caused by controller placement were quantified and computed using graph theory and cosine similarity. Moreover, based on graph partitioning theory, the load-aware controller placement problem was transformed into a specified graph partitioning problem. Finally, a load-aware optimal controller placement scheme was presented based on multi-level graph partitioning. Simulation experiments conducted on real network topologies indicate that the proposed scheme can effectively achieve nearly optimal controller load balance.

It is more and more difficult to find the valuable required scientific papers accurately and efficiently on Internet, thus a new thesis evaluation method was proposed based on consistency of the title and text to deal with this problem. First of all, the title and text were modeled by eigenvectors respectively. After that, the technique of words similarity was used to calculate the matching-degree of each feature word in title and text vector. The feature word pair was successfully matched if their matching degree was greater than a certain threshold. Then all such matching pairs and their word weights were counted up to calculate the credibility of the title. Based on the hierarchical tree structure of the thesis title, the similarity matching degree of all headings and their corresponding text were calculated by Depth First Traversal (DFT) algorithm, and then the credibility of the paper was evaluated. A case study results prove that the proposed method can realize the scientific papers' credible quality assessment, which makes it be more efficient for readers in paper reading.

In practical engineering applications, using traditional CPU serial computation for combustion numerical simulation can hardly meet the requirements on simulation speed. This paper took the advantage of GPU which has more computing capability than CPU, by discretizing the combustion physical equations on staggered grid, solving the discrete equations with preconditioned bi-conjugate gradient stabilized (PBiCGSTAB) method, exploring the parallel algorithm of GPU-oriented matrix vector multiplication and the parallel algorithm of inverse matrix vector multiplication. Hence a feasible method for numerical calculation of laminar diffusion combustion on GPU was offered. The experimental results indicate that the parallel program on GPU achieved more than 10 times speedup relative to serial program on CPU. Since the calculated results are in line with the actual situation, the method is feasible and efficient.