This paper proposes a scheme to reduce the number of flow entries permanently stored in an OpenFlow switch and the number of configuration messages from a controller in a software-defined network (SDN). In an SDN, a flow table in an OpenFlow switch is used to instruct packets. The flow table consists of flow entries decided by the controller. A flow request is sent from the OpenFlow switch to the controller if the incoming packet does not match any flow entry in the flow table. The controller’s central processing unit may be overloaded to handle user requests, since the user requests for different data types have been rapidly increasing. As a result, flow configuration in switches is delayed. Moreover, the control plane may be flooded by configuration messages of those requests. A scheme to permanently keep the flow entries in the switch can reduce the number of requests. However, a large number of permanent flow entries is required. Other switch features may be degraded, since there is not enough memory in the flow table to implement those features. In the proposed scheme, switches in the network are divided into multiple regions. In order to guide packets from sources to destinations, the flow table incorporating the concept of two multiprotocol label switching tags is re-designed. One tag directs a packet from a source switch to an edge switch in the destination region. The other tag directs the packet from that edge switch to another switch in the same region. A mathematical model for the proposed scheme is formulated as an integer linear programming to determine a set of switches in each region so that the total number of permanent flow entries in the network can be minimized. The performance of the proposed scheme is analyzed. Moreover, the proposed scheme is implemented and demonstrated via Japanese Science Information Network 5.

Flows Reduction Scheme Using Two MPLS Tags in Software-Defined Network

The biggest feature of the era of big data is that people can easily generate, access, and make use of massive data resources. As one of the most important and popular kind of big data, location big data and its application technology provide users with convenient services. However, improper collection, analysis and publishing of location big data also brings huge crisis of personal privacy disclosure. Spatial decomposition is one of the effective ways to achieve the statistics publication of location big data. In order to make full use of the redundant characteristics of location big data in spatial and temporal distribution, a hierarchical differential privacy hybrid decomposition algorithm is proposed in this paper. In the first layer of decomposition, an adaptive density grid structure is used to cluster the location big data, which not only reduces the uniform assumption errors but also avoids noise errors caused by large number of empty nodes. In order to guide the reasonable decomposition for skewed grids in the second layer, a heuristic quad-tree decomposition algorithm based on regional uniformity is designed, which solved the difficult problem for determining stop condition of the top-down decomposition of two-dimensional space. Comparative experiments show that the hierarchical differential privacy hybrid decomposition algorithm proposed in this paper has good effect in improving the accuracy of regional counting queries. The proposed algorithm has low computational complexity and obvious advantages in the publishing environment of big data.

Hierarchical differential privacy hybrid decomposition algorithm for location big data

A switch in a software-defined network (SDN) uses a flow table for packet forwarding. The flow table consists of flow entries decided by a controller. As user requests with a variety of data types have been dramatically increasing, a controller's CPU is overloaded to process those requests. The users may not obtain real-time data. Furthermore, the control plane might be overflowed by OpenFlow messages that correspond to the requests. The number of requests can be reduced by a scheme to keep permanent flow entries in the switch. A huge number of permanent flow entries to support the forwarding function degrade other functions. This paper proposes a scheme to reduce the number of permanent flow entries and the number of OpenFlow messages generated by the controller. The proposed scheme divides switches in the network into several regional groups. It re-designs a flow table that is incorporated with a concept of two multiprotocol label switching tags to guide packets from sources to destinations. One tag guides the packets from a source switch to an edge switch in the destination group. The other tag guides the packets from the edge switch to another switch in the local group. Performance evaluation shows 74% reductions of the number of permanent flow entries, compared to a conventional scheme that keeps permanent flow entries without inquiry to the controller. Moreover, 81% of the number of OpenFlow messages is reduced, compared to another scheme that inquires the controller every time once a request arrives at the switch.

Analysis of flows reduction scheme by adopting two MPLS tags in software-defined network

Ecosystem scientists will increasingly be called on to inform forecasts and define uncertainty about how changing planet conditions affect human well-being. We should be prepared to leverage the best tools available, including big data. Use of the term ‘big data’ implies an approach that includes capacity to aggregate, search, cross-reference, and mine large volumes of data to generate new understanding that can inform decision-making about emergent properties of complex systems. Although big-data approaches are not a panacea, there are large-scale environmental questions for which big data are well suited, even necessary. Ecosystems are complex biophysical systems that are not easily defined by any one data type, location, or time. Understanding complex ecosystem properties is data intensive along axes of volume (size of data), velocity (frequency of data), and variety (diversity of data types). Ecosystem scientists have employed impressive technology for generating high-frequency, large-volume data streams. Yet important challenges remain in both theoretical and infrastructural development to support visualization and analysis of large and diverse data. The way forward includes greater support for network science approaches, and for development of big-data infrastructure that includes capacity for visualization and analysis of integrated data products. Likewise, a new paradigm of cross-disciplinary training and professional evaluation is needed to increase the human capital to fully exploit big-data analytics in a way that is sustainable and adaptable to emerging disciplinary needs.

https://link.springer.com/content/pdf/10.1007%2Fs10021-016-0075-y.pdf

The Next Decade of Big Data in Ecosystem Science

Big data is more than a matter of size; it is an emerging paradigm of data of very large size (volume) and fast in/out (velocity), from various sources (variety), and of high value for knowledge extraction and decision making. Technological advances in data gathering have led to a rapid proliferation of big data in diverse areas such as remote sensing, medicine, the Internet, and social sectors. Such data brings opportunities and challenges to scientists and engineers. In order for us to make use of this massive amount of data, new data management and computational approaches are needed to permit scientists and engineers to analyze the data in (nearly) real time, often in a distributed or streaming manner. Various technologies are being discussed, and some have been realized, to support the handling of big data. In addition, big dataset cannot be stored in one location, and massively parallel processing databases and scalable storage systems are being designed to store the large datasets. What is more, big data generates an industry of supporting architectures; many cloud computing platforms and frameworks are developed to handle the big data operations, such as MapReduce. To deal with different properties of big data, different algorithms also need to be developed. Overall, big data is an opportunity to find insights in new and emerging types of data and content, to make models more agile, and to answer questions that were previously considered beyond our reach. The purpose of this special is to highlight some recent advancement to address such challenges in the big data era.

Special issue on big data networking-challenges and applications

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Special issue on big data networking-challenges and applications