Migration to Software-Defined Networks:the Customers' View

Software Defined Networking(SDN) provides a flexible and convenient way to support fine-grained traffic-engineering(TE). Besides, SDN also provides better Quality of Experience(QoE) for customers. However, the policy of the evolution from legacy networks to the SDNs overemphasizes the controllability of the network or TE while ignoring the customers' benefit. Standing in the customers' position, we propose an optimization scheme, named as Optimal Migration Schedule based on Customers' Benefit(OMSB), to produce an optimized migration schedule and maximize the benefit of customers. Not only the quality and quantity of paths availed by migration, but also the number of flows from the customers that can use these multi-paths are taken into consideration for the scheduling. We compare the OMSB with other six migration schemes in terms of the benefit of customers. Our results suggest that the sequence of the migration plays a vital role for customers, especially in the early stages of the network migration to the SDN.

Application of Big Data Technology in Evaluation of Operating Status of High-pressure Hydrogenation Heat Exchanger

The high-pressure hydrogenation heat exchanger is an important equipment of the refinery, but it is exposed to the problem of leakage caused by ammonium salt corrosion. Therefore, it is very important to evaluate the operating status of the hydrogenation heat exchanger. To improve the method for evaluating the operating status of hydrogenation heat exchangers by using the traditional method, this paper proposes a new method for evaluating the operation of hydrogenation heat exchangers based on big data. To address the noisy data common in the industry, this paper proposes an automated noisy interval detection algorithm. To deal with the problem that the sensor parameters have voluminous and unrelated dimensions, this paper proposes a key parameter detection algorithm based on the Pearson correlation coefficient. Finally, this paper presents a system-based health scoring algorithm based on PCA(Principal Component Analysis) to assist site operators in assessing the health of hydrogenation heat exchangers. The evaluation of the operating status of the hydrorefining heat exchange device based on big data technology will help the operators to more accurately grasp the status of the industrial system and have positive guiding significance for the early warning of the failure.

Development of a Non-Pollution Orange Fruit Expert System Software Based on ASP.NET

Based on ASP.NET,a orange fruit tree fertilizer expert system software was developed.The system could simulate and decide an annual fertilization plan for young and mature trees in terms of geographical position and climate.This paper introduced the design conditions,framework,production,and deployment of the system.It exhibited characters of orange specialty and was a typical online agriculture expert system.The use of the system for orange fruit management could decrease production cost,guarantee orange quality and improve economical benefit at the same time.Farmer using the system saved N input by 41-238 g/plant,P2O5 input 3-24 g/plant,and K2O input 1-36 g/plant,and got higher yield by 6-17 kg/plant.

Finding Main Causes of Elevator Accidents via Multi-Dimensional Association Rule in Edge Computing Environment

In order to discover the main causes of elevator group accidents in edge computing environment, a multi-dimensional data model of elevator accident data is established by using data cube technology, proposing and implementing a method by combining classical Apriori algorithm with the model, digging out frequent items of elevator accident data to explore the main reasons for the occurrence of elevator accidents. In addition, a collaborative edge model of elevator accidents is set to achieve data sharing, making it possible to check the detail of each cause to confirm the causes of elevator accidents. Lastly the association rules are applied to find the law of elevator Accidents.

Term-Based Pooling in Convolutional Neural Networks for Text Classification

To achieve good results in convolutional neural networks(CNN) for text classification task, term-based pooling operation in CNNs is proposed. Firstly, the convolution results of several convolution kernels are combined by this method, and then the results after combination are made pooling operation, three sorts of CNN models(we named TBCNN, MCT-CNN and MMCT-CNN respectively) are constructed and then corresponding algorithmic thought are detailed on this basis. Secondly, relevant experiments and analyses are respectively designed to show the effects of three key parameters(convolution kernel, combination kernel number and word embedding) on three kinds of CNN models and to further demonstrate the effect of the models proposed. The experimental results show that compared with the traditional method of text classification in CNNs, term-based pooling method is addressed that not only the availability of the way is proved, but also the performance shows good superiority.

DDoS Defense Algorithm Based on Multi-Segment Timeout Technology

Through the analysis to the DDoS（distributed denial of service） attack, it will conclude that at different time segments, the arrive rate of normal SYN （Synchronization） package are similar, while the abnormal packages are different with the normal ones. Toward this situation a DDoS defense algorithm based on multi-segment timeout technology is presented, more than one timeout segment are set to control the net flow. Experiment results show that in the case of little flow, multi-segment timeout has the ability dynamic defense, so the system performance is improved and the system has high response rate.

Trustworthiness Technologies of DDSS

The most significant strategic development in information technology over the past years has been ＂trusted computing＂ and trusted computers have been produced. In this paper trusted mechanisms adopted by PC is imported into distributed system, such as chain of trust, trusted root and so on. Based on distributed database server system （DDSS）, a novel model of trusted distributed database server system （TDDSS） is presented ultimately. In TDDSS role-based access control, two-level of logs and other technologies are adopted to ensure the trustworthiness of the system.

The Reliability Design and Availability Analysis of a Distributed Storage System

Current distributed parallel file systems and database systems can not satisfy the demands of data-intensive applications, such as storage capacity, access performance, reliability, scalability, and so on. Cluster-based storage sys tems have some shortcomings, too. To solve this kind of problems, a novel PC storage cluster solution is proposed, a distributed storage system based on 3-tiered agent architecture is designed, the system reliability model based on the masterslave backup mode is built, and the system availability is analyzed with the Markov model. According to the system availability formula and the values of the system parameters, the novel system can provide higher reliability and availability to satisfy users＇ requirements,

Evaluation Model of System Survivability

This paper puts forward a survivability evaluation model, SQEM （Survivability Quantitative Evaluation Model）, based on lucubrating the main method existed. Then it defines the measurement factors and parses the survivability mathematically, introduces state change probability and the idea of setting the weights of survivability factors dynamically into the evaluating process of SQEM, which improved the accuracy of evaluation. An example is presented to illustrate the way SQEM works, which demonstrated the validity and feasibility of the method.

Design and Implementation of Two-Level Metadata Server in Small-Scale Cluster File System

The reliability and high performance of metadata service is crucial to the store architecture. A novel design of a two-level metadata server file system （TTMFS） is presented, which behaves high reliability and performance. The merits both centralized management and distributed management are considered simultaneously in our design. In this file system, the advanced-metadata server is responsible for manage directory metadata and the whole namespace. The double-metadata server is responsible for maintaining file metadata. And this paper uses the Markov return model to analyze the reliability of the two-level metadata server. The experiment data indicates that the design can provide high throughput.

一种面向物联网的无线传感器网络优化部署策略(英文)

This paper proposes an open hierarchical network architecture for the Internet of Things (IoT), which can provide a unified network topology by using heterogeneous Wireless Sensor Networks (WSNs). With this proposed architecture, our research focuses on the optimal deployment strategy of the nodes on the convergence level. We aim at the maximization of the sub-network's lifetime while minimizing the deployment cost. Meanwhile, a novel metric named as the Ratio of Lifetime to Cost (RLC) is proposed to estimate the efficiency of convergence nodes deployment. Simulation results indicate that the proposed deployment algorithm can achieve the optimal number of convergence nodes. The proposed deployment strategy is able to achieve a balanced tradeoff between the network lifetime and the deployment cost.

An Algorithm Based on Markov Chain to Improve Edge Cache Hit Ratio for Blockchain-Enabled IoT

Reasonable allocation of storage and computing resources is the basis of building big data system.With the development of IoT(Internet of Things),more data will be brought.A three-layer architecture includes smart devices layer,edge cloud layer and blockchain-based distributed cloud layer.Blockchain is used in IoT for building a distributed decentralize P2P architecture to deal with the secure issue while edge computing deals with increasing volume of data.Edge caching is one of the important application scenarios.In order to allocate edge cache resources reasonably,to improve the quality of service and to reduce the waste of bandwidth resources,this paper proposes a content selection algorithm of edge cache nodes.The algorithm adopts markov chain model,improves the utilization of cache space and reduces the content transmission delay.The hierarchical caching strategy is adopted and the secondary cache stores slides of contents to expand the coverage of cached content and to reduce user waiting time.Regional node cooperation is adopted to expand the cache space and to support the regional preference of cache content.Compared with the classical substitution algorithm,simulation results show that the algorithm in this paper has higher cache hit ratio and higher space utilization.

Power savings in software defined data center networks via modified hybrid genetic algorithm

In modern data centers, power consumed by network is an observable portion of the total energy budget and thus improving the energy efficiency of data center networks （DCNs） truly matters. One effective way for this energy efficiency is to make the size of DCNs elastic along with traffic demands by flow consolidation and bandwidth scheduling, i.e., turning off unnecessary network components to reduce the power consumption. Meanwhile, having the instinct support for data center management, software defined networking （SDN） provides a paradigm to elastically control the resources of DCNs. To achieve such power savings, most of the prior efforts just adopt simple greedy heuristic to reduce computational complexity. However, due to the inherent problem of greedy algorithm, a good-enough optimization cannot be always guaranteed. To address this problem, a modified hybrid genetic algorithm （MHGA） is employed to improve the solution＇s accuracy, and the fine-grained routing function of SDN is fully leveraged. The simulation results show that more efficient power management can be achieved than the previous studies, by increasing about 5% of network energy savings.

Fog Computing Architecture-Based Data Acquisition for WSN Applications

Efficient and effective data acquisition is of theoretical and practical importance in WSN applications because data measured and collected by WSN is often unreliable, such as those often accompanied by noise and error, missing values or inconsistent data. Motivated by fog computing, which focuses on how to effectively offload computation-intensive tasks from resource-constrained devices, this paper proposes a simple but yet effective data acquisition approach with the ability of filtering abnormal data and meeting the real-time requirement. Our method uses a cooperation mechanism by leveraging on both an architectural and algorithmic approach. Firstly, the sensor node with the limited computing resource only accomplishes detecting and marking the suspicious data using a light weight algorithm. Secondly, the cluster head evaluates suspicious data by referring to the data from the other sensor nodes in the same cluster and discard the abnormal data directly. Thirdly, the sink node fills up the discarded data with an approximate value using nearest neighbor data supplement method. Through the architecture, each node only consumes a few computational resources and distributes the heavily computing load to several nodes. Simulation results show that our data acquisition method is effective considering the real-time outlier filtering and the computing overhead.

多方数据共享环境下匿名化数据的安全性量化方法(英文)

This paper aims to find a practical way of quantitatively representing the privacy of network data. A method of quantifying the privacy of network data anonymization based on similarity distance and entropy in the scenario involving multiparty network data sharing with Trusted Third Party (TTP) is proposed. Simulations are then conducted using network data from different sources, and show that the measurement indicators defined in this paper can adequately quantify the privacy of the network. In particular, it can indicate the effect of the auxiliary information of the adversary on privacy.

Efficient Security Multimedia System on Embedded Platform

Security video communication is a challenging task,especially for wireless video applications.An efficient security multimedia system on embedded platform is designed.By analyzing the hardware architecture and resource,the efficient DSP-based H.264/AVC coding is studied by efficient video coding techniques and system optimizing implementation.To protect the confidentiality and integrity of media information,a novel security mechanism is presented,which includes user identify authentication and a perceptual video encryption algorithm based on exploiting the special feature of entropy coding in H.264.Experimental results show that the proposed hardware framework has high performance and achieves a better balance between security and efficiency.The proposed security mechanism can achieve high security and low complexity cost,and has a little effect on the compression ratio and transmission bandwidth.What’s more,encoding and encryption at the same time,the performance of data process can meet real-time application.

基于主成分分析法提取网络流量特征（英文）

The Deep Packet Inspection(DPI)method is a popular method that can accurately identify the flow data and its corresponding application.Currently,the DPI method is widely used in common network management systems.However,the major limitation of DPI systems is that their signature library is mainly extracted manually,which makes it hard to efficiently obtain the signature of new applications.Hence,in this paper,we propose an automatic signature extraction mechanism using Principal Component Analysis(PCA)technology,which is able to extract the signature automatically.In the proposed method,the signatures are expressed in the form of serial consistent sequences constructed by principal components instead of normally separated substrings in the original data extracted from the traditional methods.Extensive experiments based on numerous sets of data have been carried out to evaluate the performance of the proposed scheme,and the results prove that the newly proposed method can achieve good performance in terms of accuracy and efficiency.

Flow Label-Based TPv6 Packet Classification Algorithm with Dimension Reduction Capability

Traditional packet classification for IPv4 involves examining standard 5-tuple of a packet header,source address,destination address,source port,destination port and protocol.With introduction of IPv6 flow label field which entails labeling the packets belonging to the same flow,packet classification can be resolved based on 3 dimensions:flow label,source address and destination address.In this paper,we propose a novel approach for the 3-tuple packet classification based on flow label.Besides,by introducing a conversion engine to covert the source-destination pairs to the compound address prefixes,we put forward an algorithm called Reducing Dimension(RD) with dimension reduction capability,which combines heuristic tree search with usage of buckets.And we also provide an improved version of RD,called Improved RD(IRD),which uses two mechanisms:path compression and priority tag,to optimize the performance.To evaluate our algorithm,extensive experiments have been conducted using a number of synthetically generated databases.For the memory consumption,the two proposed new algorithms only consumes around 3% of the existing algorithms when the number of filters increases to 10 k.And for the average search time,the search time of the two proposed algorithms is more than four times faster than others when the number of filters is 10 k.The results show that the proposed algorithm works well and outperforms many typical existing algorithms with the dimension reduction capability.

A New Honeynet Model

Based on citing Realm, a new Honeynet Model-BRHNS （Based Realm Honeynet） is presented. BRHNS makes use of cooperation between Realms, the efficiency of Honeynet is improved, in intrusion behavior analysis module, unknown attack data are classified by unsupervised clustering algorithm, accordingly, prepared for extracting intrusion rules and adding the new rules to IDS rule-lib, consequently, the detection efficiency of IDS is improved and the workload of Honeynet is effectively reduced. Had performed experiments through cross-validate, we found it was effective to classify the attack data by unsupervised clustering algorithm.

Load-Balance Policy in Two Level-Cluster File System

In this paper, we explored a load-balancing algorithm in a cluster file system contains two levels of metadata-server, primary-level server quickly distributestasks to second-level servers depending on the closest load-balancing information. At the same time, we explored a method which accurately reflect I/O traffic and storage of storage-node： computing the heat-value of file, according to which we realized a more logical storage allocation. According to the experiment result, we conclude that this new algorithm shortens the executing time of tasks and improves the system performance compared with other load algorithm.