The combination of Network Function Virtualization (NFV) and Software Defined Networking (SDN) possesses a great potential in accommodating dynamic network control via cloning/migration of virtualized NFs and steering of traffic flows. A great challenge is the lack of the proprietary internal NF state information to the control system (including SDN controller and NFV orchestrator), which may lead to incorrect packet/flow processing at the newly created NF instances. In this work, we design a light-weight approach which can function either independently or as a plug-in to the network control system to reveal the internal NF states. Unlike the previous work, we propose to learn the internal NF states through normal network functions instead of designing extra APIs for certain NFs. Moreover, we propose a feasible way to detect state violations and even correct them automatically. Our approach is tested by experiments, and the results confirm its efficiency and practicability.

Cooperative communication (CC) is shown to be a promising technology to improve the spatial diversity without additional equipments and antennas. The choice of relay nodes can significantly affect the performance of wireless cooperative networks, e.g. energy- efficiency. Most of existing works focused on dedicating one relay node to a transmission pair in the network. However, the energy-efficiency can be improved by sharing each relay node among more than one transmission pair. In this paper, we first introduce the feasibility of shared relay assignment to improve the energy-efficiency while achieving the required bandwidth for each transmission pair. Then, the Minimum Energy Consumption (MEC) problem of CC with shared relay is defined, and we formulate it as a mixed integer optimization program and prove it is NP-hard. A Simulated Annealing (SA) algorithm is designed to solve this problem. We conduct abundant simulation experiments to evaluate the performance of our proposed algorithm. The simulation results show that the SA algorithm can save about 15 energy consumption on average than the previous work in wireless networks.

It is a common view in Software Defined Network (SDN) that the flow table plays the most significant role in SDN architecture, but suffers from the limited TCAM chips. The shortage of flow table storage strongly impacts the quality of service (QoS) provided by SDN, but requires rational solutions. In this paper, we present a practical on-line approach based on the decision tree structure to solve this problem. Our performance is evaluated by the comparison with other existing technologies.

In this work, we focus on designing high-throughput anycast mechanisms in a software Defined Network (SDN), as anycast routing serves as a fundamental building block for many services. Specifically, we investigate two main challenges for efficient implementation of a SDN-based anycast system: high throughput anycast routing (HTAR) and congestion-free anycast reconfiguration (CFAR). For the first challenge, we design an anycast routing (ARFU) algorithm, based on the fully polynomial time approximation scheme and the single-source unsplittable flow routing method. We prove that ARFU produces a route that can achieve a throughput at least 1/2(1+ξ) of the optimum, where ξ > 0 is an arbitrarily small constant. We then propose an anycast reconfiguration algorithm with transitive configurations, called ARTC, for congestion-free. This algorithm tries to find a routing reconfiguration procedure consisting of at most T ≥ 1 transitive configurations while meeting all the flow demands. When traffic demands can not be satisfied, ARTC maximizes the minimum throughput using one transitive configuration. Our simulations demonstrate high-efficiency of our algorithms.

Surface transportation is of a great importance in urban life. Fueled by the promise of Smart City, it is becoming more and more common to provide WiFi services in urban Public Transportation System (PTS). In this paper, we present a practical method to make use of the WiFi service provided on buses to estimate real-time urban traffic condition. We validate the proposed method in a city district and show that it is an excellent alternative or supplement of the existing traffic condition estimation services.

Software Defined Network (SDN) is facilitating rapid innovation of network by providing a programmable network infrastructure. However, managing SDN flow rules, especially among multiple modules and administrators, has become complex and error-prone. Different controller modules with diverse objectives may be installed on the SDN controller, which can lead to anomalies among policies and rules. In this paper, we propose ADRS(Anomaly Detecting and Resolving for SDN) to solve this problem. Firstly, we analyse the rule-level anomalies that may occur in SDN based on OpenFlow protocol. Then we present an interval tree model for rapid rule scanning and a share model for network privilege allocating. By applying these models, we provide an automatic algorithm to detect and resolve the anomalies among SDN modules. Moreover, a rule-recovery mechanism is presented to avoid modification faults. We also implement and evaluate our system in the OpenDayLight controller.

The software defined network (SDN) has been developing tremendously in recent years. Numerous studies are proposed on the performance of SDN in academia. The idea of programmable network which is the foundation of SDN ensures dynamic network management by separating the control plane away from the network switches. The focus of this paper is the flow table's size in OpenFlow switches which is a significant bottleneck in SDN's practical application. We propose a mechanism named “Flow Table Reduction Scheme”(FTRS) for reducing the flow table size and maintaining the omnipotent controller's functions at the same time. We test the performance of FTRS both in simulation and experiment and the results show that FTRS is able to reduce 98% at most of the size of flow table with no impact on network's normal functions.

As more and more applications migrate into clouds, the placement of virtual machines for these applications has much impact on the performance of cloud systems. A number of virtual machine (VM) placement techniques have been proposed over recent years. However, most of the existing works on VM placement ignore the response latency of the requests from tenants. In this paper, we investigate the techniques of VM placement with stochastic requests from the tenants to minimize the total (average) response latency. We first model the requests for each application from the corresponding tenant as independent Poisson stream. Moreover, the VMs are modeled as simple M/M/1 queueing systems. Then, we define the problem of VM placement for minimizing the total response delay (VMMD) and show it is NP-hard. We propose three heuristic algorithms, namely, Greedy, Local Adjustment (LA) and Simulated Annealing (SA). We conduct abundant simulation experiments to evaluate the performance of our proposed algorithms. The simulation results show that the proposed algorithms are efficient in decreasing the total response latency of the requests from tenants. Especially, the SA heuristic, which decreases the total response latency about 68% at most, shows the best performance on minimizing the total response latency in cloud systems.

Wireless mesh networks (WMNs) are being used and deployed widely all around the world for various reasons such as public safety, environmental monitoring and city-wide wireless Internet services [4], [12]. Although faced with some difficulties, wireless mesh network is thought to be a preferred municipal Internet service provider [5] with huge potential due to its automatic connection, ease of installation, dynamic route discovery, flexibility and other advantages. Since plenty of cities such as Singapore and the city of Cambridge have put providing ubiquitous Internet access on the agenda [3], the research on providing QoS-guaranteed municipal WMNs is in urgent need.

Unlike traditional auction schemes that do not take security into account, a good secure auction scheme first has to satisfy the following two properties: 1) correctness: the auction result should be the same as the result using traditional correct schemes; 2) security: during the auction process, the final result is the only information revealed to the participants. Furthermore, the secure auction scheme should be efficient enough so that the final result can be determined within a reasonable time. Existing works mainly deal with single-sided spectrum auction, and are not totally secure though claimed to be secure. For example, in [2], the auctioneer can easily know the sums of bids for all the possible allocations, and in [3], the auctioneer can obtain the bids of all buyer groups and their ranking order in the auction. This kind of information is sensitive and should be kept secret. In a very timely recent study [4], the authors propose PS-TRUST, a solution for secure double spectrum auction based on TRUST [5]. To our best knowledge, this is the first work that achieves both correctness and security for secure spectrum auction. Unfortunately, the efficiency of this work is not satisfactory.

This paper focuses on recognizing person relations indicated by predicates from large scale of free texts. In order to determine whether a sentence contains a potential relation between persons, we cast this problem to a classification task. Dynamic Convolution Neural Network (DCNN) is improved for this task. It uses frame convolution for making uses of more features efficiently. Experimental results on Chinese person relation recognition show that the proposed model is superior when compared to the original DCNN and several strong baseline models. We also explore employing large scale unlabeled data to achieve further improvements.

Content Delivery Network (CDN) is proposed for replicating data objects at multiple locations in the network and encounters vast potential for future development, as a result of which, a number of replica placement techniques have been proposed over the last decade. However, most of the existing works on replica placement (RP) ignore the statistical property of the demands and the restricted service rate of the servers. In this paper, we investigate the techniques of replica placement in CDNs with stochastic demands and M/M/1 servers to optimize the overall performance in the network. We first model the demands and the servers as independent Poisson streams and simple M/M/1 queueing systems, respectively. Then, a formal definition and formalization of RP problem will be given. We show that RP problem is NP-complete and propose two heuristic algorithms: Greedy Dropping (GD) and Tabu Search (TS). We conduct abundant simulation experiments to evaluate the performance of our proposed algorithms. According to our simulation results, both of the two algorithms are efficient in finding a feasible solution with high probability. Especially, the TS decreases the average delay of the demands about 50% on average.

A metapattern (also known as a metaquery) is a new approach for integrated data mining systems. As opposed to a typical "toolbox"-like integration, where components must be picked and chosen by users without much help, metapatterns provide a common representation for inter-component communication as well as a human interface for hypothesis development and search control. One weakness of this approach, however, is that the task of generating fruitful metapatterns is still a heavy burden for human users. In this paper, we describe a metapattern generator and an integrated discovery loop that can automatically generate metapatterns. Experiments in both artificial and real-world databases have shown that this new system goes beyond the existing machine learning technologies, and can discover relational patterns without requiring humans to pre-label the data as positive or negative examples for some given target concepts. With this technology, future data mining systems could discover high-quality, human-comprehensible knowledge in a much more efficient and focused manner, and data mining could be managed easily by both expert and less-expert users.

Induction programs make several assumptions that limit their practical utility. Research to overcome the limitation of working within a fixed vocabulary is reported. A recently reported phenomenon in machine learning is that there is a tradeoff between the simplicity of concept descriptions and coverage of training instances, and a learning system cannot have both. It is argued that if a learning system can generate new terms, it can achieve both simplicity and coverage. A method for generating one new kind of terms, comparative terms, is given. The experimental results on a mushroom classification task show that a single comparative term can achieve 90% predictive accuracy.>