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Yao G.,Tsinghua University | Bi J.,Tsinghua University | Guo L.,BUPT
Proceedings - International Conference on Network Protocols, ICNP | Year: 2013

In this paper, a potential threat to reliability of Software Defined Networking (SDN) is disclosed: the cascading failures of controllers. Current SDN designs have widely utilized multiple controllers and the load of a failed controller can be redistributed to the other controllers. However, simply utilizing multiple controllers cannot protect SDN networks from a single point of failure: the load of the controllers which carry the load of the failed controller can exceed the capacity of them, and then cascading failures of controllers will happen. In this article, at first we propose a model for such failures and present simulation results based on the model. Strategies for initial load balance and load redistribution after failure are designed to prevent such failures. The simulation result shows the strategies can significantly increase the resistance of SDN networks to cascading failures. © 2013 IEEE. Source

Lee Y.-C.,Hanyang University | Hong J.,Hanyang University | Kim S.-W.,Hanyang University | Gao S.,BUPT | Hwang J.-Y.,AskStory
HT 2015 - Proceedings of the 26th ACM Conference on Hypertext and Social Media | Year: 2015

AskStory is a company providing an e-recruitment service where job seekers find a variety of job openings. This paper discusses an approach to recommending job openings attractive to job seekers. Source

A significant portion of the Chinese characters is phonogram, whose phonetic part can be used for overall sound inference. Phonetic degree is an inherent problem in the inference because low phonetic degree implies little phonetic dependence between the phonogram and its phonetic components. Solving the phonetic degree problem requires association each phonogram with the acoustic features. This paper introduces acoustic feature-based clustering, a classifying model that divides the common phonogram by defining new similarity of the sounds. This allows phonetic degree to be evaluated more reasonable. We demonstrate the clustering outperformed the traditional empirical estimation by having more accurate and real expressiveness. Acoustic feature-based clustering output 48.6% as phonetic degree, less than the empirical claim which is around 75%. As a clustering classifier, our model is competitive with a much clearer boundary on the phonogram dataset. © (2012) Trans Tech Publications, Switzerland. Source

Wang D.,BUPT | Wang X.,BUPT | Gu B.,Waseda University
IEICE Transactions on Communications | Year: 2014

In this paper, a multicast concept for Device-to-Device (D2D) communication underlaying a cellular infrastructure is investigated. To increase the overall capacity and improve resource utilization, a novel interference coordination scheme is proposed. The proposed scheme includes three steps. First, in order to mitigate the interference from D2D multicast transmission to cellular networks (CNs), a dynamic power control scheme is proposed that can determine the upper bound of D2D transmitter power based on the location of Base Station (BS) and areas of adjacent cells from the coverage area of D2D multicast group. Next, an interference limited area control scheme that reduces the interference from CNs to each D2D multicast receiver is proposed. The proposed scheme does not allow cellular equipment (CUE) located in the interference limited area to reuse the same resources as the D2D multicast group. Then two resource block(RB) allocation rules are proposed to select the appropriate RBs from a candidate RB set for D2D multicast group. From the simulation results, it is confirmed that the proposed schemes improve the performance of the hybrid system compared to the conventional ways. © 2014 The Institute of Electronics, Information and Communication Engineers. Source

Zhang Y.J.,BUPT | Yang L.J.,BUPT
Applied Mechanics and Materials | Year: 2014

In traditional Bayesian classification data mining methods, there may be defects such as predictions unreliable because the selected predictors are little or not related with the target factor. this paper analyzes the correlation between predictors and the target factor using correlation coefficient based on Bayesian classification model and combines with Hadoop distributed file system and parallel programming models to explore an improved algorithm. The experiments show that this method not only makes the prediction more reliable but also saves resources and improves the efficiency of the algorithm greatly. In addition, it is suitable for massive data processing. © 2014 Trans Tech Publications, Switzerland. Source

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