Wireless Internet Access Using IS-2000 Third Generation System: A Performance and Capacity Study

The Internet has been growing tremendously in the recent years and applications like web browsing are becoming increasingly popular. In a collective effort to provide seamless access to the Internet, wireless equipment manufacturers and service providers are developing 3G wireless systems that efficiently support current and future Internet applications. In this paper, we evaluate the performance and capacity of a 3G wireless data system based on IS-2000 standard. We consider web browsing as the common application for all users and evaluate the system performance for single and parallel web browsing sessions. We perform this study through a detailed simulation of web traffic model described by distributions of number of objects per page, object size, page request size and page reading time. The simulation includes HTTP and TCP/IP protocols, link level recovery, radio resource management, mobility, channel model and, delays in the Internet and the radio access network. We quantify important system attributes like average page download times and system throughput (Kb/s per carrier per sector). We also evaluate normalized object download time, normalized page download time, penalty in performance due to link errors, link layer buffer sizes needed, channel holding time, average power used and distribution of the power used in the system.     

面向Weblog的模糊协同聚类算法

面向Weblog的协同聚类算法具有同时发现用户聚类及与之对应的页面聚类的能力,已成为Weblog数据挖掘的重要研究内容。由于现有的面向Weblog的协同聚类算法大多采用硬划分方法将用户和页面分配到聚类,因此,无法很好地处理聚类边界的问题,即一个用户可能属于多个聚类,从而影响了聚类质量。该文给出了一种面向Weblog的模糊协同聚类FCOW(Fuzzy CO-clustering for Weblog)算法来解决协同聚类算法的边界问题,以提高聚类结果的质量。该算法首先利用矩阵Hadamard积运算发现Weblog中隐含的独立用户模式1={,,K}PA pa pa;其次,依据pa k所对应的页面子集将剩余用户分配到该独立模式中,从而产生协同聚类结果 {k,k}CS CP,k=1,,K;最后计算每个用户和页面与协同聚类之间的模糊隶属度,并以该隶属度作为个性化推荐的依据。实验结果表明,FCOW算法具有获得高质量聚类结果的能力。     

A Learning-Based Approach for Web Cache Management

Web caching has been widely used to alleviate Internet traffic congestion in World Wide Web (WWW) services. To reduce download throughput, an effective strategy on web cache management is needed to exploit web usage information in order to make a decision on evicting the document stored in case of cache saturation. This paper presents a so-called Learning Based Replacement algorithm (LBR), a hybrid approach towards an efficient replacement model for web caching by incorporating a machine learning technique (naive Bayes) into the LRU replacement method to improve prediction of possibility that an existing page will be revised by a succeeding request, from access history in a web log. The learned knowledge includes information on which URL objects in cache should be kept or evicted. The learning-based model is acquired to represent the hidden aspect of user request pattern for predicting the re-reference possibility. By a number of experiments, the LBR gains potential improvement of prediction on revisit probability, hit rate and byte hit rate overtraditional methods; LRU, LFU, and GDSF, respectively.     

高阶异构数据模糊联合聚类算法

为了更有效地分析聚簇重叠部分高阶异构数据的聚簇结果,提出了一种高阶异构数据模糊联合聚类（HFCC）算法,该算法最小化每个特征空间中对象与聚簇中心的加权距离。推导出对象隶属度和特征权重的迭代更新公式,设计出聚类过程的迭代算法,并且从理论上证明了该迭代算法的收敛性。另外,通过泛化XB指标,提出适用于评估高阶异构数据聚类质量的指标GXB,用于判断聚簇数目。实验表明,HFCC算法能够有效探测数据内部隐藏的重叠聚簇结构,并且HFCC算法聚类效果明显优于5种有代表性的硬划分算法,此外GXB指标能够有效判定高阶异构数据的聚簇数目。     

基于BP神经网络的Web页面分类算法

提出了一种基于BP神经网络的Web页面分类算法。在搜索引擎的结构中提取页面标题、内容标题和内容摘要来表示页面，采用向量空间模型计算分类组合与页面组合的相关性对页面进行矢量化，将训练后的BP神经网络用于对Web页面进行分类。实验结果表明，该分类算法有一定的实用价值。     

无线网络仿真中数据业务源模型研究

本文研究了业务源模型的发展，针对无线应用环境中数据业务提出了仿真中可以使用的业务源模型，由于WWW应用的广泛性，模型研究的主要对象是Web浏览产生的业务，对其他广泛使用的E-mail业务、FTP业务、游戏业务也给出了介绍及解决方案，最后提出了仿真中的注意事项。     

Outdoor scene image segmentation based on background recognition and perceptual organization

In this paper, we propose a novel outdoor scene image segmentation algorithm based on background recognition and perceptual organization. We recognize the background objects such as the sky, the ground, and vegetation based on the color and texture information. For the structurally challenging objects, which usually consist of multiple constituent parts, we developed a perceptual organization model that can capture the nonaccidental structural relationships among the constituent parts of the structured objects and, hence, group them together accordingly without depending on a priori knowledge of the specific objects. Our experimental results show that our proposed method outperformed two state-of-the-art image segmentation approaches on two challenging outdoor databases (Gould data set and Berkeley segmentation data set) and achieved accurate segmentation quality on various outdoor natural scene environments.     

Schemes for fault identification in communication networks

A single fault in a large communication network may result in a large number of fault indications (alarms) making the isolation of the primary source of failure a difficult task. The problem becomes worse in cases of multiple faults. In this paper we present an approach for modelling the problem of fault diagnosis. We propose a graph based network model that takes into account the dependencies among the different objects in the telecommunication environment and a novel approach to estimate the domain of an alarm. Based on that model, we design an algorithm for fault diagnosis and analyze its performance with respect to the accuracy of the fault hypotheses it provides. We also propose and analyze a fault diagnosis algorithm suitable for systems for which an independent failure assumption is valid. Finally, we examine the importance of the information of dependency between objects for the fault diagnosis process     

Unveiling the underlying relationships over a network for monitoring purposes

Nowadays traffic monitoring and analysis tools provide poor information about traffic volume without giving any clear view of what the hidden rules and relationships that govern these flows are. Since the majority of flows is generated by services (web browsing, email, p2p) and most of these applications are dependent on many network assets (servers and databases) we should discover the underlying relationships of every application. We present a technique that discovers the hidden relationships among components of a network that consist of parts of specific applications. From time information and flow attributes, such as IP addresses and service ports, our method using a novel hybrid genetic algorithm produces a small set of fuzzy rules that can reveal the underlying relationships over a network without any guidance. These dependencies build a service graph which can become a useful tool for fault localization, monitoring service performance, designing changes and anomaly detection. Copyright © 2009 John Wiley & Sons, Ltd.     

A compressive sensing‐based network tomography approach to estimating origin–destination flow traffic in large‐scale backbone networks

A traffic matrix can exhibit the volume of network traffic from origin nodes to destination nodes. It is a critical input parameter to network management and traffic engineering, and thus it is necessary to obtain accurate traffic matrix estimates. Network tomography method is widely used to reconstruct end‐to‐end network traffic from link loads and routing matrix in a large‐scale Internet protocol backbone networks. However, it is a significant challenge because solving network tomography model is an ill‐posed and under‐constrained inverse problem. Compressive sensing reconstruction algorithms have been well known as efficient and precise approaches to deal with the under‐constrained inference problem. Hence, in this paper, we propose a compressive sensing‐based network traffic reconstruction algorithm. Taking into account the constraints in compressive sensing theory, we propose an approach for constructing a novel network tomography model that obeys the constraints of compressive sensing. In the proposed network tomography model, a framework of measurement matrix according to routing matrix is proposed. To obtain optimal traffic matrix estimates, we propose an iteration algorithm to solve the proposed model. Numerical results demonstrate that our method is able to pursuit the trace of each origin–destination flow faithfully. Copyright © 2014 John Wiley & Sons, Ltd.     

Multimedia annotation via semi-supervised shared-subspace feature selection

With the rapid development of social network and computer technologies, we always confront with high-dimensional multimedia data. It is time-consuming and unrealistic to organize such a large amount of data. Most existing methods are not appropriate for large-scale data due to their dependence of Laplacian matrix on training data. Normally, a given multimedia sample is usually associated with multiple labels, which are inherently correlated to each other. Although traditional methods could solve this problem by translating it into several single-label problems, they ignore the correlation among different labels. In this paper, we propose a novel semi-supervised feature selection method and apply it to the multimedia annotation. Both labeled and unlabeled samples are sufficiently utilized without the need of graph construction, and the shared information between multiple labels is simultaneously uncovered. We apply the proposed algorithm to both web page and image annotation. Experimental results demonstrate the effectiveness of our method.     

Defending against distributed denial-of-service attacks with max-min fair server-centric router throttles

Our work targets a network architecture and accompanying algorithms for countering distributed denial-of-service (DDoS) attacks directed at an Internet server. The basic mechanism is for a server under stress to install a router throttle at selected upstream routers. The throttle can be the leaky-bucket rate at which a router can forward packets destined for the server. Hence, before aggressive packets can converge to overwhelm the server, participating routers proactively regulate the contributing packet rates to more moderate levels, thus forestalling an impending attack. In allocating the server capacity among the routers, we propose a notion of level-k max-min fairness. We first present a control-theoretic model to evaluate algorithm convergence under a variety of system parameters. In addition, we present packet network simulation results using a realistic global network topology, and various models of good user and attacker distributions and behavior. Using a generator model of web requests parameterized by empirical data, we also evaluate the impact of throttling in protecting user access to a web server. First, for aggressive attackers, the throttle mechanism is highly effective in preferentially dropping attacker traffic over good user traffic. In particular, level-k max-min fairness gives better good-user protection than recursive pushback of max-min fair rate limits proposed in the literature. Second, throttling can regulate the experienced server load to below its design limit - in the presence of user dynamics - so that the server can remain operational during a DDoS attack. Lastly, we present implementation results of our prototype on a Pentium III/866 MHz machine. The results show that router throttling has low deployment overhead in time and memory.     

The cache location problem

This paper studies the problem of where to place network caches. Emphasis is given to caches that are transparent to the clients since they are easier to manage and they require no cooperation from the clients. Our goal is to minimize the overall flow or the average delay by placing a given number of caches in the network. We formulate these location problems both for general caches and for transparent en-route caches (TERCs), and identify that, in general, they are intractable. We give optimal algorithms for line and ring networks, and present closed form formulae for some special cases. We also present a computationally efficient dynamic programming algorithm for the single server case. This last case is of particular practical interest. It models a network that wishes to minimize the average access delay for a single web server. We experimentally study the effects of our algorithm using real web server data. We observe that a small number of TERCs are sufficient to reduce the network traffic significantly. Furthermore, there is a surprising consistency over time in the relative amount of web traffic from the server along a path, lending a stability to our TERC location solution. Our techniques can be used by network providers to reduce traffic load in their network     

Recognizing and characterizing dynamics of cellular devices in cellular data network through massive data analysis

The user clients for accessing Internet are increasingly shifting from desktop computers to cellular devices. To be competitive in the rapidly changing market, operators, Internet service providers and application developers are required to have the capability of recognizing the models of cellular devices and understanding the traffic dynamics of cellular data network. In this paper, we propose a novel Jaccard measurement‐based method to recognize cellular device models from network traffic data. This method is implemented as a scalable paralleled MapReduce program and achieves a high accuracy, 91.5%, in the evaluation with 2.9 billion traffic records collected from the real network. Based on the recognition results, we conduct a comprehensive study of three characteristics of network traffic from device model perspective, the network access time, the traffic volume, and the diurnal patterns. The analysis results show that the distribution of network access time can be modeled by a two‐component Gaussian mixture model, and the distribution of traffic volumes is highly skewed and follows the power law. In addition, seven distinct diurnal patterns of cellular device usage are identified by applying unsupervised clustering algorithm on the collected massive traffic data. Copyright © 2014 John Wiley & Sons, Ltd.     

Network level perspective in web sessions troubleshooting

In the last years, the quantity of data and the number of applications carried over web traffic have been continuously increasing and nowadays web browsing accounts for most of the Internet traffic. In such a scenario, a poor browsing experience can result very annoying to the end user, and the effective identification of the root cause of such bad performance is of primary interest to both the users and the network operators. In this paper, we present a unified framework, based on a novel lightweight open‐source publicly available probe and on an original statistical diagnosis algorithm, to correctly and effectively point out the segment of a web connection (eg, local client, backbone network, and DNS server) responsible for a poor web browsing experience. The extensive experimental evaluation carried out in the paper demonstrates the effectiveness of the proposed approach to diagnose poor quality of experience at a large scale.     

A unifying co‐operative web caching architecture

Network caching of objects has become a standard way of reducing network traffic and latency in the web. However, web caches exhibit poor performance with a hit rate of about 30%. A solution to improve this hit rate is to have a group of proxies form co‐operation where objects can be cached for later retrieval. A co‐operative cache system includes protocols for hierarchical and transversal caching. The drawback of such a system lies in the resulting network load due to the number of messages that need to be exchanged to locate an object. This paper proposes a new co‐operative web caching architecture, which unifies previous methods of web caching. Performance results shows that the architecture achieve up to 70% co‐operative hit rate and accesses the cached object in at most two hops. Moreover, the architecture is scalable with low traffic and database overhead. Copyright © 2002 John Wiley & Sons, Ltd.     

Measuring web page complexity by analyzing TCP flows and HTTP headers

To understand website complexity deeply, a web page complexity measurement system is developed. The system measures the complexity of a web page at two levels: transport-level and content-level, using a packet trace-based approach rather than server or client logs. Packet traces surpass others in the amount of information contained. Quantitative analyses show that different categories of web pages have different complexity characteristics. Experimental results show that a news web page usually loads much more elements at more accessing levels from much more web servers within diverse administrative domains over much more concurrent transmission control protocol (TCP) flows. About more than half of education pages each only involve a few logical servers, where most of elements of a web page are fetched only from one or two logical servers. The number of content types for web game traffic after login is usually least. The system can help web page designers to design more efficient web pages, and help researchers or Internet users to know communication details.     

互联网流量识别中的基于标签传播的重叠社团发现算法

The increased capacity and availability of the Internet has led to a wide variety of applications. Internet traffic characterization and application i-dentification is important for network management. In this paper, based on detailed flow data collected from the public networks of Internet Service Pro-viders, we construct a flow graph to model the in-teractions among users. Considering traffic from different applications, we analyze the community structure of the flow graph in terms of community size, degree distribution of the community, commu-nity overlap, and overlap modularity. The near line-ar time community detection algorithm in complex networks, the Label Propagation Algorithm (LPA), is extended to the flow graph for application identi-fication. We propose a new initialization and label propagation and update scheme. Experimental re-sults show that the proposed algorithm has high ac-curacy and efficiency.     

Finding and Extracting Data Records from Web Pages

Many HTML pages are generated by software programs by querying some underlying databases and then filling in a template with the data. In these situations the metainformation about the data structure is lost, so automated software programs cannot process these data in such powerful manners as information from databases. We propose a set of novel techniques for detecting structured records in a web page and extracting the data values that constitute them. Our method needs only an input page. It starts by identifying the data region of interest in the page. Then it is partitioned into records by using a clustering method that groups similar subtrees in the DOM tree of the page. Finally, the attributes of the data records are extracted by using a method based on multiple string alignment. We have tested our techniques with a high number of real web sources, obtaining high precision and recall values.     

Throughput and Energy-Efficiency-Aware Protocol for Ultrawideband Communication in Wireless Sensor Networks: A Cross-Layer Approach

In this paper, we propose an efficient MAC protocol: the throughput maximized MAC protocol (TM-MAC), inspired by the availability that a number of ultrawideband (UWB) transmission parameters can be tuned to better match the requirements of data flow. In TM-MAC, we implement a concurrent multiuser access scheme instead of a mutual exclusion method such as TDMA and random access. For multiuser interference, we establish a model to adaptively adjust the data transmission rate to generate the expected signal to interference noise ratio (SINR) at the receiver side for reliable communications. We also analyze the relationship among the theoretical maximum channel capacity, achievable maximum channel capacity, and data transmission rate. According to network topology, TM-MAC redivides each piconet into several subsets in which communication pairs can make communication simultaneously and achieve the maximum throughput using the highest data rate. In subset formation, we propose a general analytical framework that captures the unique characteristics of shared wireless channel and throughput variance, as well as allows the modeling of a large class of systemwide throughput maximization via the specification of the per-link utilization function. For algorithm essential parameters design, we consider the influence of traffic type on the system performance. Heavy tailed distribution, compared to Poisson distribution for most existing work, is exploited to accurately model the real traffic to achieve the adaptation of our algorithm. Simulation results show that our algorithm can maximize throughput to achieve short latency.