Spline-based multi-regime traffic stream models

In order to develop optimal multi-regime traffic stream models, a new method that integrates cluster analysis and B-spline regression is presented. First, for identifying the proper number of regimes, the K-means and the fuzzy c-means methods are applied in cluster analysis to actual traffic data, which suggests that dividing the traffic flow into two or three clusters can best reflect intrinsic patterns of traffic flows. Such information is then taken as guidance in spline regression, thus significantly reducing the computational burden of estimating spline models. Spline regression is used to estimate the locations of knots and the coefficients of the model so that the global error can be minimized. Model analysis results demonstrate that the proposed spline models have better fitting and generalization capability than the conventional models. In addition, the new method is more flexible in terms of data fitting and can provide smoother traffic stream models.

Efficient Heavy Hitters Identification over Speed Traffic Streams

With the rapid increase of link speed and network throughput in recent years,much more attention has been paid to the work of obtaining statistics over speed traffic streams.It is a challenging problem to identify heavy hitters in high-speed and dynamically changing data streams with less memory and computational overhead with high measurement accuracy.In this paper,we combine Bloom Filter with exponential histogram to query streams in the sliding window so as to identify heavy hitters.This method is called EBF sketches.Our sketch structure allows for effective summarization of streams over time-based sliding windows with guaranteed probabilistic accuracy.It can be employed to address problems such as maintaining frequency statistics and finding heavy hitters.Our experimental results validate our theoretical claims and verifies the effectiveness of our techniques.

P2P Streaming Traffic Classification in High-Speed Networks

The growing P2P streaming traffic brings a variety of problems and challenges to ISP networks and service providers.A P2P streaming traffic classification method based on sampling technology is presented in this paper.By analyzing traffic statistical features and network behavior of P2P streaming,a group of flow characteristics were found,which can make P2P streaming more recognizable among other applications.Attributes from Netflow and those proposed by us are compared in terms of classification accuracy,and so are the results of different sampling rates.It is proved that the unified classification model with the proposed attributes can identify P2P streaming quickly and efficiently in the online system.Even with 1:50 sampling rate,the recognition accuracy can be higher than 94%.Moreover,we have evaluated the CPU resources,storage capacity and time consumption before and after the sampling,it is shown that the classification model after the sampling can significantly reduce the resource requirements with the same recognition accuracy.

DELAY JITTER OF A PERIODIC CELL STREAM THROUGH AN ATM MULTIPLEXER IN A BURSTY TRAFFIC ENVIRONMENT

The periodic cell stream is a very important member among the input traffic sources in ATM networks. In this paper, a finite-buffered ATM multiplexer with traffic sources composed of a periodic cell stream, multiple i.i.d Bernoulli cell streams and bursty two-state Markov Modulated Bernoulli Process (MMBP) cell streams is exactly analyzed. The probability mass function of queuing delay, the autocorrelation and power spectrum of delay jitter for this periodic cell stream are derived. The analysis is used to expose the behavior of delay jitter for a periodic cell stream through an ATM multiplexer in a bursty traffic environment. The simulation results indicate that the analytical results are accurate.

基于鱼群效应的智能网联车队形成与演化机理研究

随着智能网联车的快速发展,智能网联车与人工驾驶车辆混行下的交通特性分析和协同控制研究已成为热点。本文构建多车道混合交通流元胞自动机仿真模型,对智能网联车队的形成及状态演化过程进行仿真刻画。首先,基于智能网联车的网联化特征,引入鱼群效应来描述4种智能网联车队的形成过程;其次,从车队角度,利用马尔可夫性计算车队规模转移概率,阐述车队状态演化过程;再次,引入Gipps安全距离原则对NaSch模型进行改进,并对智能网联车和智能网联车队进行速度引导;最后,基于实测的车辆到达率,对建立的基于鱼群效应的混合车流元胞自动机模型进行仿真实验。结果表明:智能网联车队可以有效改善混合交通流的运行状态,缓解交通拥堵;在渗透率60%的条件下,智能网联车队规模为3时,与不存在车队相比,拥堵率可减少43.9%,交通流速度约提升43%,并且随着车队规模的增大,交通流平均速度趋于平稳。

混合流下非机动车道基本需求宽度的设计

为弥补在非机动车道设计方面对电动自行车考虑不足的情况,有必要对由人力/电动自行车组成的饱和混合流下非机动车道基本需求宽度进行研究。以非机动车饱和混合流为研究对象,从非机动车的物理运动宽度组成出发,通过构建多重线性回归模型分析了非机动车物理运动宽度的影响因素,对分类自变量进行组合筛选;建立了不同组合类型下的非机动车速度与运行摆幅、路侧安全净距线性模型,得到性别为女性、年龄为老年和车型为电动自行车为最不利组合的结论;根据非机动车道的85%位运营速度确定最不利组合下的非机动车物理运动宽度,进而确定非机动车道基本需求宽度;针对非机动车道宽度合理性验证的准确性问题,从稳定性角度提出了运用速度熵来验证基本需求宽度合理性的方法,并以多条非机动车道路段为例进行了实测验证。研究结果表明:在隔离栏和划线下,非机动车单车道基本需求宽度分别为1.65、1.50 m;在绿化带、隔离栏和划线下,非机动车两车道基本需求宽度分别为2.90、2.80、2.65 m。在同一车道数和隔离方式下,车道宽度越宽,交通流速度熵越高;随着非机动车道宽度增加,以基本需求宽度为拐点,非机动车道的交通流速度熵变化幅度增大,稳定性更差。

基于FFmpeg多线程编码的智能交通监控系统设计

针对智能交通管理设备本身缺乏安全监管,传统视频监控延迟高、画质低、稳定性差的问题,提出一种基于FFmpeg的多线程编码视频流传输方案。通过FFmpeg调用h264_nvenc编码器,实现宏块行级的GPU多线程加速,降低编码延迟。使用Visual Studio 2019和QT15.5开发基于FFmpeg的音视频处理软件,对多路视频流进行封装、推流,并搭建Nginx流媒体服务器进行分发。通过实验表明,该系统整体的传输延迟低于1 s,且拥有良好的率失真特性,监控画面清晰、稳定性高,实现了对交通管理设备实时稳定的安全监控。

新型农业经营主体农产品直播经营的实践过程及行为机制——基于数字资本的分析视角

伴随新媒体网络平台的发展和数字技术的下沉,农产品直播电商成为农业现代化及数字化转型过程中解决农户参与市场困境的重要手段。在数字资本视域下,通过考察新型农业经营主体的适度规模经营与农产品直播经营的适配逻辑,发现新型农业经营主体通过生活性的发掘与再造生产数字资本,以流量变现的方式实现数字资本的可兑换性,以代销为辅的经营策略弥合直播经营过程中数字资本持续性与农产品单一性之间的矛盾。数字资本生成是资本转化的基础,流量变现为农产品经营提供渠道,流量维持成为持续经营的关键。代销转向在实现流量再生产的同时增加了农产品经营风险,其中的品控不确定性容易导致数字资本信誉受损,进而危及以数字资本为桥梁资本的数字化生计模式。代销转向旨在为农产品直播经营保持流量,是对平台规模经济的妥协与适配。在农产品直播经营过程中,新型农业经营主体是追求利润者、维持生计者和受剥削的数字劳动者的综合体。在平台增殖逻辑及社会注意力流动属性的双重作用下,经营者尝试由家庭化经营转向团队化运作、由新农人转型为职业主播。但在数字资本流失风险、乡村伦理逻辑以及与农户共生关系等多重因素的约束下,经营者形成了以注重农产品品质及消费者购物体验为主、以代销为辅的经营模式,创造了数字优势者与数字弱势者、生产者与消费者合作互惠的市场空间,并以新型农业经营主体与农户的利益联结为基础实现了数字道义。

“做数据”:数字劳动秩序的数据化建构——基于Y直播间销售劳动的案例研究

数字技术不仅对社会结构变迁产生了深远影响,而且已成为社会结构的一部分。劳动是社会实践的典型形态,是分析数字社会结构与秩序的重要领域。本文依据结构化理论,设计了案例研究的程序,设计了基于配置性资源与权威性资源的分析框架,对D平台Y直播间的销售劳动展开了实证研究;通过展现直播销售员在“做数据”过程中面临的数据环境以及个体应势而生的系统意识和劳动策略,挖掘出了平台数据所具有的资源配置与工具权威双重属性,以及这两种形态的数据参与塑造数字劳动的实践过程。在此基础上,研究揭示了数据劳动的基本逻辑,阐释了数字劳动秩序如何形成、劳动者如何在平台权力下发挥主观能动性这两个相辅相生的问题。

Adaptive and augmented active anomaly detection on dynamic network traffic streams

Active anomaly detection queries labels of sampled instances and uses them to incrementally update the detection model,and has been widely adopted in detecting network attacks.However,existing methods cannot achieve desirable performance on dynamic network traffic streams because(1)their query strategies cannot sample informative instances to make the detection model adapt to the evolving stream and(2)their model updating relies on limited query instances only and fails to leverage the enormous unlabeled instances on streams.To address these issues,we propose an active tree based model,adaptive and augmented active prior-knowledge forest(A3PF),for anomaly detection on network trafic streams.A prior-knowledge forest is constructed using prior knowledge of network attacks to find feature subspaces that better distinguish network anomalies from normal traffic.On one hand,to make the model adapt to the evolving stream,a novel adaptive query strategy is designed to sample informative instances from two aspects:the changes in dynamic data distribution and the uncertainty of anomalies.On the other hand,based on the similarity of instances in the neighborhood,we devise an augmented update method to generate pseudo labels for the unlabeled neighbors of query instances,which enables usage of the enormous unlabeled instances during model updating.Extensive experiments on two benchmarks,CIC-IDS2017 and UNSW-NB15,demonstrate that A3PF achieves significant improvements over previous active methods in terms of the area under the receiver operating characteristic curve(AUC-ROC)(20.9%and 21.5%)and the area under the precision-recall curve(AUC-PR)(44.6%and 64.1%).

基于Spark Streaming流技术的机动车缉查布控系统设计

机动车缉查布控系统以治安交通卡口系统为基础,将公路运行车辆的构成、流量分布、违章情况等信息进行汇总,实现对车辆轨迹的查询分析,从而为交通规划、管理和案件侦破等提供有效决策。由于机动车的数量增长迅猛,且机动车违规违章具有流动性、偶发性的特点,造成卡口系统录入的数据量巨大,传统的机动车缉查布控技术只能在预设条件下对数据进行查询,不能实现快速地实时分析查询。通过采用Spark Streaming技术进行大数据实时处理,实现实时显示和告警机动车违规违章活动热点,分析和预测机动车活动轨迹。

基于流计算和大数据平台的实时交通流预测

目前交通流预测实时性差,很难满足在线分析和预测任务的需求,基于此提出一种Flink流计算框架和大数据平台结合的实时交通流预测方法。基于流计算框架实时捕捉和预处理数据,包括采用Flink的transform算子对数据进行校验和处理,将处理后的数据sink到大数据的HDFS文件系统,交由下一步的大数据并行框架进行分析建模与训练,实现基于流计算和大数据平台的实时交通流预测。实验结果表明,Flink能够实时捕捉和预处理交通流数据,把数据准时无误送入分布式文件系统中,在此基础上借助大数据框架下的并行分析和建模优势,在实时性数据分析与预测方面取得了较好的效果。

考虑平台流量补贴的直播电商供应链协同激励契约研究

针对直播电商供应链的收益分配问题以及直播平台与品牌商对主播的协同激励问题,考虑平台对主播的流量补贴,运用双层委托代理理论分别构建主播与直播平台不签约时仅品牌商提供激励情形和签约时直播平台与品牌商协同激励情形下的激励契约模型,研究两种情形下的最优契约,并作对比分析。研究表明,当直播平台与主播不签约时,随着主播影响力的增加,直播平台的收益分成比例固定不变,品牌商的收益分成比例先减小后趋于常值,主播的收益分成比例先增加后趋于常值;当直播平台与主播签约时,存在一个签约门槛使得直播电商供应链各成员的最优期望利润均增加,且随着主播影响力的增大,主播与品牌商的收益分成比例均随之增加,而直播平台的收益分成比例随之减少。此外,直播平台对高风险厌恶主播进行流量补贴能够发挥更大的激励作用,因此,直播平台应加大对高风险厌恶主播的流量补贴力度。

一种基于HTTP/3传输特性的加密视频识别方法

视频流量逐渐在网络中占据主导地位,且视频平台大多对其进行加密传输。虽然加密传输视频可以有效保护用户隐私,但是也增加了监管有害视频传播的难度.现有的加密视频识别方法基于TCP(Transmission Control Protocol)传输协议头部信息和HTTP/1.1(Hypertext Transfer Protocol Version1.1)的传输模式,提取应用层音视频数据单元传输长度序列来实现视频识别.但是随着基于UDP(User Datagram Protocol)的QUIC(Quick UDP Internet Connections)协议及基于QUIC实现的HTTP/3(Hypertext Transfer Protocol Version 3)协议应用于视频传输,已有方法不再适用.HTTP/3协议缺少类似TCP的头部信息,且使用了多路复用机制,并对几乎所有数据进行了加密,此外,视频平台开始使用多片段合并分发技术,这给从网络流量中精准识别加密视频带来了巨大挑战。本文基于HTTP/3协议中的控制信息特征,提出了从HTTP/3加密视频流中提取数据传输特征并进行修正的方法,最大程度复原出应用层音视频长度特征.面向多片段合并分发导致的海量匹配问题,本文基于明文指纹库设计了键值数据库来实现视频的快速识别.实验结果表明,本文提出的基于HTTP/3传输特性的加密视频识别方法能够在包含36万个真实视频指纹的YouTube大规模指纹库中达到接近99%的准确率,100%的精确率以及99.32%的F1得分,对传输过程中加人了填充顿的Facebook平台,在包含28万个真实视频指纹的大规模指纹库中达到95%的准确率、100%的精确率以及96.45%的F1得分,在具有同样特性的Instagram平台中,最高可达到97.57%的F1得分,且本方法在所有指纹库中的平均视频识别时间均低于0.4秒.本文的方法首次解决了使用HTTP/3传输的加密视频在大规模指纹库场景中的识别问题,具有很强的实用性和通用性.

End-to-End Performance Evaluation of TCP Traffic under Multi-Queuing Networks

While Internet traffic is currently dominated by elastic data transfers, it is anticipated that streaming applications will rapidly develop and contribute a significant amount of traffic in the near future. Therefore, it is essential to understand and capture the relation between streaming and elastic traffic behavior. In this paper, we focus on developing simple yet effective approximations to capture this relationship. We study, then, an analytical model to evaluate the end-to-end performance of elastic traffic under multi-queuing system. This model is based on the fluid flow approximation. We assume that network architecture gives the head of priority to real time traffic and shares the remaining capacity between the elastic ongoing flows according to a specific weight.

Software Defined Traffic Engineering for Improving Quality of Service

The Software Defined Networking(SDN) paradigm separates the control plane from the packet forwarding plane, and provides applications with a centralized view of the distributed network state. Thanks to the flexibility and efficiency of the traffic flow management, SDN based traffic engineering increases network utilization and improves Quality of Service(QoS). In this paper, an SDN based traffic scheduling algorithm called CATS is proposed to detect and control congestions in real time. In particular, a new concept of aggregated elephant flow is presented. And then a traffic scheduling optimization model is formulated with the goal of minimizing the variance of link utilization and improving QoS. We develop a chaos genetic algorithm to solve this NP-hard problem. At the end of this paper, we use Mininet, Floodlight and video traces to simulate the SDN enabled video networking. We simulate both the case of live video streaming in the wide area backbone network and the case of video file transferring among data centers. Simulation results show that the proposed algorithm CATS effectively eliminates network congestions in subsecond. In consequence, CATS improves the QoS with lower packet loss rate and balanced link utilization.

在线流量对跨境电商企业溢价能力的双重效应——来自企业微观数据的经验证据

在全球数字化背景下,我国出口跨境电商企业力图以营销策略获取在线流量,扩张海外市场,获得企业溢价这一关键竞争力。以跨境电商企业的微观数据为分析样本,从理论与实证两个维度评估在线流量对跨境电商企业溢价能力的影响效应。研究发现,在线流量规模具有双重影响效应,其作用方向及大小均取决于企业对营销承诺的兑现程度;当营销承诺兑现程度低时,溢价能力随在线流量规模的增长而降低,低价营销型企业属于此类典型,即企业凭借低价营销承诺所吸引的流量规模越大,提价时流量损失越严重,企业为此付出的定价折扣就越多,且定价抑制效应随规模扩大呈扩张趋势;当营销承诺的兑现程度高时,跨境电商企业溢价能力随在线流量规模的增长而提升,直播带货型企业属于此类典型,即企业依靠兑现差异化营销承诺的方式提升产品信用度,逐步掌握定价权;消费者特征、企业特征变量在流量规模对企业溢价发挥作用的过程中具有调节效应。因此,跨境电商企业应坚持营销差异化与产品差异化的发展方向,充分利用互联网公域流量,通过直播带货等方式,逐步将在线流量转换为精准流量和复购流量,实现跨境电商企业的长期稳健发展;关注口碑营销,完善负面口碑管理机制,洞悉海外市场需求,捕捉不同文化背景下的市场痛点,鼓励海外消费者积极反馈,发挥跨境在线流量的积极效应;依据海外消费者的购买偏好、浏览记录等一手交易数据进行挖掘分析,促进企业有针对性地改进营销策略,突出产品特色,宣传经营理念与创新发展战略,逐步增强顾客对产品与企业的认同感,树立跨境品牌形象。

农村经济发展中的新媒体应用

新媒体的出现重构了农民社会生活的方方面面,给农村经济发展带来新机遇。这种机遇被一些敢于创新的农民“自发”看到,他们积极探索新技术,不仅能够熟练使用新媒体表达自我,同时通过流量变现、直播带货等手段创造出可观的经济效益。各级政府开始更新观念,通过增加互联网建设财政性投入、政策引导及主动对接互联网企业等,主动推进新媒体参与农村经济发展。然而,这种依靠自发意识所推动的媒介应用相对盲目和短视,还需要政府敢于顺势而为,不断更新理念、整合资源,在观念和政策上积极引领,实现新媒体应用在农村经济中的“自觉”发展。

基于细分市场的直播电商平台流量定价策略研究

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随着移动互联网技术的不断发展,多媒体传播形式也在加速发展,企业希望打破传统的营销观念,借助新媒体平台,寻求更精准的触达目标用户。微信作为一个拥有海量活跃用户的平台,微信营销是企业在新媒体营销运用中的重要一环。因此,本文基于微信营销理论及实际应用,对KY服饰现有的微信营销策略等相关内容进行分析,着重找出微信营销策略存在的问题,如小程序页面设计有待优化、用户黏性不大、直播引流力度不足等问题,最后提出对KY服饰的优化性建议,以供参考。