随机丢弃机制下分组丢失重传的缓存性能分析

基于网络随机丢弃(RD)分组机制的缓存管理策略,建立了M/M/1/K排队模型和分组丢弃重传的M1 M2/M/1/K排队模型,以解决被丢弃的分组不重传和实现以一定概率重传的问题.利用生灭过程的极限定理和矩阵几何解方法给出了相应排队系统诸如缓存队列中平均分组数、平均重传分组数、系统利用率、分组丢失率和单位时间内平均进入缓存分组数等稳态性能指标.两类系统性能指标的数值比较结果表明,具有RD机制的缓存管理策略必须充分考虑分组重传对系统性能指标的影响.     

Analysis on D2D Heterogeneous Networks with State-Dependent PriorityTraffic

In this work, we consider the performance analysis of state dependent priority traffic and scheduling in device to device (D2D) heterogeneous networks. There are two priority transmission types of data in wireless communication, such as video or telephone, which always meet the requirements of high priority (HP) data transmission first. If there is a large amount of low priority (LP) data, there will be a large amount of LP data that cannot be sent. This situation will cause excessive delay of LP data and packet dropping probability. In order to solve this problem, the data transmission process of high priority queue and low priority queue is studied. Considering the priority jump strategy to the priority queuing model, the queuing process with two priority data is modeled as a two-dimensional Markov chain. A state dependent priority jump queuing strategy is proposed, which can improve the discarding performance of low priority data. The quasi birth and death process method (QBD) and fixed point iteration method are used to solve the causality, and the steady-state probability distribution is further obtained.Then, performance parameters such as average queue length, average throughput, average delay and packet dropping probability for both high and low priority data can be expressed. The simulation results verify the correctness of the theoretical derivation. Meanwhile, the proposed priority jump queuing strategy can significantly improve the drop performance of low-priority data.     

Precedence-enabled per-hop behaviour: impact on TCP and UDP flows

The author proposes and analyses an approach to support both precedence and preemption (P&P) and QoS handling in common packet transport infrastructures. Precedence has to do with the relative importance of the information content, whereas preemption has to do with mechanisms to deny lower precedence traffic access to network resources in favour of higher precedence traffic, when necessary. The author's approach to this duality is to enhance active queue management (AQM) techniques to provide P&P capabilities and rely on standard, well-studied QoS schedulers, for example, Weighted Round Robin, Class-Based Weighted Fair Queuing, etc., for handling QoS requirements. The present scheme allows low-order queues (within the context of QoS handling) to plead up to the next higher-order queue for help in alleviating queue congestion under periods of communication link overload. The scheme is referred as the cross-queue-AQM (CQ-ACM) scheme and can be extended to higher numbers of queues and any type of scheduler. Through extensive simulation studies, the performance of CQ-AQM scheme under heavy traffic limits is investigated, where preemption is required. The performance metrics of interest to this analysis are packet delay, packet loss and throughput as a function of the packet QoS class and P-L. Further, two new metrics of merit, Gain and System Efficiency, are defined, which are specific to P&P considerations. The studies concentrated on both flow-controlled and non-flow-controlled traffic, as well as mixed traffic conditions. Simulation results show that the algorithms perform extremely well for all cases studied.     

OBS组装算法与业务流特性关系研究

D V WILSON 对以太网研究表明,网络业务流呈现自相似性[1],自相似对网络的服务质量有很大影响。光突发包的组装是光突发交换的关键技术,它极大地影响光突发交换的网络性能,不同的组装算法对自相似的影响不尽相同。仿真结果表明,突发包的组装算法在一定程度上不仅能减少自相似性,而且能降低分组阻塞率。     

A pinwheel packet scheduling scheme for broadband wireless networks

Abstract

A frame‐based packet scheduling scheme, the pinwheel scheduling (PWS) scheme, is proposed and investigated at the medium access control (MAC) layer in broadband wireless networks. The objective of the proposed scheduling scheme is to provide low delay and low jitter for real‐time traffic. We have demonstrated in this paper that the proposed PWS scheme not only satisfies the packet‐level QoS requirements but also has low implementation cost. The PWS scheme gives the highest priority to CBR connections in a service cycle to minimize their delay and jitter. For ABR traffic, a number of slots are allocated to fit their MCR. The remaining time slots are then allocated to VBR traffic according to their PCR. Thus, the VBR traffic may suffer large delay and jitter when the number of CBR connections increases. The PWS with modular strategy (PWS/MS) is proposed to improve the performance of the VBR traffic. We also introduce three different slot‐reuse strategies named real‐time traffic first (RTF), non‐real‐time traffic first (NRTF) and longest queue first (LQF), respectively, to improve resource utilization. In order to demonstrate the performance of the PWS and PWS/MS schemes, comparisons are made with existing schemes such as the round‐robin (RR) and weighted‐round‐robin (WRR) by using OPNET software. Simulation results show that the proposed schemes are capable of maintaining the lowest delay and jitter for VBR and CBR traffic while not sacrificing the available bit rate (ABR) traffic.     

An Efficient Internet Traffic Classification System Using Deep Learning for IoT

Internet of Things (IoT) defines a network of devices connected to the internet and sharing a massive amount of data between each other and a central location. These IoT devices are connected to a network therefore prone to attacks. Various management tasks and network operations such as security, intrusion detection, Quality-of-Service provisioning, performance monitoring, resource provisioning, and traffic engineering require traffic classification. Due to the ineffectiveness of traditional classification schemes, such as port-based and payload-based methods, researchers proposed machine learning-based traffic classification systems based on shallow neural networks. Furthermore, machine learning-based models incline to misclassify internet traffic due to improper feature selection. In this research, an efficient multilayer deep learning based classification system is presented to overcome these challenges that can classify internet traffic. To examine the performance of the proposed technique, Moore-dataset is used for training the classifier. The proposed scheme takes the pre-processed data and extracts the flow features using a deep neural network (DNN). In particular, the maximum entropy classifier is used to classify the internet traffic. The experimental results show that the proposed hybrid deep learning algorithm is effective and achieved high accuracy for internet traffic classification, i.e., 99.23%. Furthermore, the proposed algorithm achieved the highest accuracy compared to the support vector machine (SVM) based classification technique and k-nearest neighbours (KNNs) based classification technique.     

Performance behaviour of IEEE 802.11 distributed coordination function

The authors present an extensive investigation of the performance of the IEEE 802.11 medium access control (MAC) protocol, with respect to throughput and delay. For the protocol analysis, a new model, which describes the protocol's behaviour to a great extent by incorporating and extending the existing models, is proposed. The authors also present a detailed analysis of the end-to-end delay through the study of the MAC delay and the queueing delay. The authors use the Z-transform of backoff duration to obtain the mean value, the variance and the probability distribution of the MAC delay. For the queueing analysis, first the authors consider an M/G/l queue in order to provide a first look at the queueing delay. Second, the authors modify the input process of the queue so that the packet arrival process is described by an ON- OFF model, which expresses the bursty nature of traffic. In the investigations, data rates of 1, 5.5 and 11 Mbps are assumed to highlight the effect of the bit rate on network performance for both Basic and request-to-send/ clear-to-send access mechanisms. The throughput and delay analyses are validated by simulating the distributed coordination function, whereas the models are compared with the existing models based on their results. The accuracy of the analyses was found to be quite satisfactory.     

Malicious Traffic Detection in IoT and Local Networks Using Stacked Ensemble Classifier

Malicious traffic detection over the internet is one of the challenging areas for researchers to protect network infrastructures from any malicious activity. Several shortcomings of a network system can be leveraged by an attacker to get unauthorized access through malicious traffic. Safeguard from such attacks requires an efficient automatic system that can detect malicious traffic timely and avoid system damage. Currently, many automated systems can detect malicious activity, however, the efficacy and accuracy need further improvement to detect malicious traffic from multi-domain systems. The present study focuses on the detection of malicious traffic with high accuracy using machine learning techniques. The proposed approach used two datasets UNSW-NB15 and IoTID20 which contain the data for IoT-based traffic and local network traffic, respectively. Both datasets were combined to increase the capability of the proposed approach in detecting malicious traffic from local and IoT networks, with high accuracy. Horizontally merging both datasets requires an equal number of features which was achieved by reducing feature count to 30 for each dataset by leveraging principal component analysis (PCA). The proposed model incorporates stacked ensemble model extra boosting forest (EBF) which is a combination of tree-based models such as extra tree classifier, gradient boosting classifier, and random forest using a stacked ensemble approach. Empirical results show that EBF performed significantly better and achieved the highest accuracy score of 0.985 and 0.984 on the multi-domain dataset for two and four classes, respectively.     

QoS Model of a Router with Feedback Control

The Internet has evolved into a shared, integrated platform of a broad range of applications with different Quality‐of‐Service (QoS) requirements. Routers are an important part of the Internet and play a critical role in assuring QoS. A router is usually placed between two networks to receive data packets from one network and then transmit those data packets to another network if necessary. Data packets are the actual units of data traveling on computer networks. A data packet has two parts: header and data. The data carries messages, such as e‐mail text, from computer applications. The header carries information that is required to control and manage the transmission of the data packet on computer networks. Existing approaches for providing QoS involve prediction or estimation for traffic characterization to determine parameters required of static traffic admission control. However, prediction or estimation inaccuracy in traffic characterization can result in inappropriate parameter settings for static admission control and, in turn, compromise QoS or resource utilization. This study presents a QoS model of a router with feedback control that monitors the state of resource usage and adaptively adjusts parameters of traffic admission control to overcome prediction or estimation inaccuracy and achieve a balance between QoS and resource utilization. The QoS model of a router with feedback control is simulated to test its performance on QoS and resource utilization in both heavy and light traffic conditions. The performance of the QoS model of a router with feedback control is also compared with that of two basic QoS models of a router with static admission control using admission control parameters resulting from over‐ and under‐characterization of traffic, respectively. The simulation results show that the QoS model of a router with feedback control achieves a better balance between QoS and resource utilization than the basic QoS models with over‐ and under‐characterizations of traffic in the heavy traffic condition. This study also shows that the three models of routers demonstrate similar QoS performances and resource utilization in the light traffic condition. Copyright © 2005 John Wiley & Sons, Ltd.     

Priority-based CDMA system with variable code reservation periods for multiclass traffic

A priority-based CDMA system with variable code reservation periods for supporting multiple traffic types is presented. For the purpose of evaluation we build mathematical models for analysing two subsystems with different types of multimedia traffic. Two performance measures, the average packet dropping probability and the average packet transmission delay, are derived from the models based on equilibrium-point analysis. The effects of the two performance measures on system performance are discussed by varying the code reservation periods to adapt to multiple-class traffic with various priorities.     

Network Traffic Obfuscation System for IIoT-Cloud Control Systems

One of the latest technologies enabling remote control, operational efficiency upgrades, and real-time big-data monitoring in an industrial control system (ICS) is the IIoT-Cloud ICS, which integrates the Industrial Internet of Things (IIoT) and the cloud into the ICS. Although an ICS benefits from the application of IIoT and the cloud in terms of cost reduction, efficiency improvement, and real-time monitoring, the application of this technology to an ICS poses an unprecedented security risk by exposing its terminal devices to the outside world. An adversary can collect information regarding senders, recipients, and prime-time slots through traffic analysis and use it as a linchpin for the next attack, posing a potential threat to the ICS. To address this problem, we designed a network traffic obfuscation system (NTOS) for the IIoT-Cloud ICS, based on the requirements derived from the ICS characteristics and limitations of existing NTOS models. As a strategy to solve this problem wherein a decrease in the traffic volume facilitates traffic analysis or reduces the packet transmission speed, we proposed an NTOS based on packet scrambling, wherein a packet is split into multiple pieces before transmission, thus obfuscating network analysis. To minimize the ICS modification and downtime, the proposed NTOS was designed using an agent-based model. In addition, for the ICS network traffic analyzer to operate normally in an environment wherein the NTOS is applied, a rule-based NTOS was adopted such that the actual traffic flow is known only to the device that is aware of the rule and is blocked for attackers. The experimental results verified that the same time requested for response and level of difficulty of analysis were maintained by the application of an NTOS based on packet scrambling, even when the number of requests received by the server per second was reduced. The network traffic analyzer of the ICS can capture the packet flow by using the pre-communicated NTOS rule. In addition, by designing an NTOS using an agent-based model, the impact on the ICS was minimized such that the system could be applied with short downtime.     

Improving performance in the OpMiGua hybrid network employing the network layer packet redundancy scheme

The optical migration-capable network with service guarantees hybrid network is a time-divided packet/circuit hybrid network offering absolute performance guarantees and high resource utilisation. Guaranteed service traffic (GST) follows lightpaths receiving absolute priority over statistically multiplexed (SM) traffic. Transmission resources are fully shared by interleaving SM and GST packets, but packet-switched SM packets experience contention, resulting in a packet loss that should be minimised. This article explores a novel bufferless network approach where the performance of the SM class is satisfied by applying packet-level forward error correction (FEC). Two implementation strategies are proposed: RedSM transmitting redundancy packets as SM traffic and RedGST transmitting redundancy packets as low-priority GST traffic. The performance of the schemes is explored analytically and by simulation. For uniform traffic loads, the RedGST scheme shows the best performance of the two, but even at 60% load the RedSM scheme shows packet loss rates (PLRs)close to 1025. Simulations with non-uniform traffic loads illustrate the importance of selecting the optimal redundancy ratio; the failure to do so results in up to two orders of magnitude increase in PLR for the RedGST scheme. A much higher tolerance for traffic variations is the main benefit for the RedSM scheme. In view of the results, both schemes are attractive alternatives to buffered contention resolution techniques.     

一种融合MAC层拥塞通告的混合网络TCP协议

在研究无线网络媒体接入控制(MAC)层拥塞测度的基础上,提出了一种跨层的显式拥塞通告(ECN)机制,即:当数据包中记录的请求发送(RTS)次数超过给定阈值时,通过ECN向传输控制协议(TCP)源端发送拥塞通告,从而启动TCP拥塞控制.这种跨层设计是对有线网络中基于主动队列管理(AQM)的拥塞控制的有效补充,由此可以得到一种与已有的协议无缝连接的混合网络TCP模型.通过在网络模拟器NS2中构造多流无线局域网和多跳无线/有线混合网络,对所提出的方法进行了仿真,实验结果说明该方法能够提高混合网络的性能,并且具备良好的扩展性.     

面向宽带流媒体的接入汇聚路由器中缓冲区容量的选取研究

根据宽带流媒体的突发性和自相似特点及接入汇聚路由器（ACR）对预留宽带的严格控制,建立了用分形布朗运动（FBM）来表示输入流量模型的自相似模型,用其定量分析了ACR中缓冲区容量与分组丢失率的关系。分析结果表明,为支持其有良好服务质量的宽带流媒体业务,必须确保每路视频流的丢失率小于一个定值,在此条件下,所需缓冲压的容量与媒流体的自相似系数及链路平均利用率成正比,在实际工程实践中可以根据系统丢失率的要求计算需要预留的缓冲区容量,以此来保证视频传输的服务质量。     

Dynamic resource allocation for supporting real-time multimedia applications in IEEE 802.15.3 WPANs

The IEEE 802.15.3 medium access control (MAC) protocol is an emerging standard for high-rate wireless personal area networks (WPANs), especially for supporting high-quality real-time multimedia applications. Despite defining quality of service (QoS) signalling mechanisms for interoperability between devices, IEEE 802.15.3 does not specify resource allocation algorithms that are left to manufacturers. To guarantee the QoS of real-time variable bit rate (VBR) videos and utilise the radio resource efficiently, the authors propose a dynamic resource allocation algorithm. The proposed bandwidth allocation algorithm is based on a novel traffic predictor. Recently, the variable step-size normalised least mean square (VSSNLMS) algorithm was employed for on-line traffic prediction of VBR videos. However, the performance of the VSSNLMS algorithm significantly degrades due to the abrupt traffic variation occurring at the scene boundary. To tackle this problem, the authors design a novel traffic predictor based on a simple scene detection algorithm and the VSSNLMS algorithm. Analyses using real-life MPEG video traces indicate that the proposed traffic predictor significantly outperforms the VSSNLMS algorithm with respect to the prediction error. The performance of the proposed bandwidth allocation algorithm is also investigated by comparing several existing algorithms. Simulation results demonstrate that the proposed bandwidth allocation algorithm surpasses other mechanisms in terms of channel utilisation, buffer usage and packet loss rate.     

Performance evaluation of a multi-product system under CONWIP control

Analytical models of multi-product manufacturing systems operating under CONWIP control are composed of closed queuing networks with synchronization stations. Under general assumptions, these queuing networks are hard to analyze exactly and therefore approximation methods must be used for performance evaluation. This research proposes a new approach based on parametric decomposition. Two-moment approximations are used to estimate the performance measures at individual stations. Subsequently, the traffic process parameters at the different stations are linked using stochastic transformation equations. The resulting set of non-linear equations is solved using an iterative algorithm to obtain estimates of key performance measures such as throughput, and mean queue lengths. Numerical studies indicate that the proposed method is computationally efficient and yields fairly accurate results when compared to simulation.     

Effective transmission opportunity allocation scheme for real-time variable bit rate traffic flows with different delay bounds

The medium access control of IEEE 802.11e defines a novel coordination function, namely, hybrid coordination function (HCF), which allocates transmission opportunity (TXOP) to stations taking their quality of service (QoS) requirements into account. However, the reference TXOP allocation scheme of HCF controlled channel access, a contention-free channel access function of HCF, is only suitable for constant bit rate traffic. For variable bit rate traffic, packet loss may occur seriously. The authors propose a TXOP allocation scheme to efficiently allocate bandwidth and meet the QoS requirements in terms of both delay bound and packet loss probability. To achieve high bandwidth efficiency, the authors take advantage of not only intra-flow multiplexing gain of traffic flows with large delay bounds, but also inter-flow multiplexing gain of multiple traffic flows with different delay bounds. According to numerical results obtained by computer simulations, the proposed TXOP allocation scheme results in much higher bandwidth efficiency than previous algorithms under the same constraints of delay bounds and packet loss probability.     

Robust end-to-end loss differentiation scheme for transport control protocol over wired/wireless networks

The authors propose a robust end-to-end loss differentiation scheme to identify the packet losses because of congestion for transport control protocol (TCP) connections over wired/wireless networks. The authors use the measured round trip time (RTT) values to determine whether the cause of packet loss is because of the congestion over wired path or regular bit errors over wireless paths. The classification should be as accurate as possible to achieve high throughput and maximum fairness for the TCP connections sharing the wired/wireless paths. The accuracies of previous schemes in the literature depends on varying network parameters such as RTT, buffer size, amount of cross traffic, wireless loss rate and congestion loss rate. The proposed scheme is robust in that the accuracy remains rather stable under varying network parameters. The basic idea behind the scheme is to set the threshold for the classification to be a function of the minimum RTT and the current sample RTT, so that it may automatically adapt itself to the current congestion level. When the congestion level of the path is estimated to be low, the threshold for a packet loss to be classified as a congestion loss is increased. This avoids unnecessary halving of the congestion window on packet loss because of the regular bit errors over the wireless path and hence improves the TCP throughput. When the congestion level of the path is estimated to be high, the threshold for a packet loss to be classified as the congestion loss not to miss any congestion loss is decreased and hence improves the TCP fairness. In ns 2 simulations, the proposed scheme correctly classifies the congestion losses under varying network parameters whereas the previous schemes show some dependency on subsets of parameters.     

Bio-inspired Hybrid Feature Selection Model for Intrusion Detection

Intrusion detection is a serious and complex problem. Undoubtedly due to a large number of attacks around the world, the concept of intrusion detection has become very important. This research proposes a multilayer bio-inspired feature selection model for intrusion detection using an optimized genetic algorithm. Furthermore, the proposed multilayer model consists of two layers (layers 1 and 2). At layer 1, three algorithms are used for the feature selection. The algorithms used are Particle Swarm Optimization (PSO), Grey Wolf Optimization (GWO), and Firefly Optimization Algorithm (FFA). At the end of layer 1, a priority value will be assigned for each feature set. At layer 2 of the proposed model, the Optimized Genetic Algorithm (GA) is used to select one feature set based on the priority value. Modifications are done on standard GA to perform optimization and to fit the proposed model. The Optimized GA is used in the training phase to assign a priority value for each feature set. Also, the priority values are categorized into three categories: high, medium, and low. Besides, the Optimized GA is used in the testing phase to select a feature set based on its priority. The feature set with a high priority will be given a high priority to be selected. At the end of phase 2, an update for feature set priority may occur based on the selected features priority and the calculated F-Measures. The proposed model can learn and modify feature sets priority, which will be reflected in selecting features. For evaluation purposes, two well-known datasets are used in these experiments. The first dataset is UNSW-NB15, the other dataset is the NSL-KDD. Several evaluation criteria are used, such as precision, recall, and F-Measure. The experiments in this research suggest that the proposed model has a powerful and promising mechanism for the intrusion detection system.     

以太无源光网络单级多功能ONU内调度算法

针对以太无源光网络(EPON),为各类业务提供服务质量支持,特别是保障时延敏感型业务的服务质量,提出一种调度算法,应用到EPON的光网络单元(ONU)中.我们设法让这个单级调度算法具备多级调度功能.通过理论分析和仿真测试,证明本算法具有灵活的优先级机制和公平调度功能.