Search

For supporting the upcoming applications, wireless mobile users require high bandwidth. To serve these requirements, a wireless network that has an optical backbone with a colossal bandwidth capacity is required. The radio-over-fiber (RoF) network is a promising solution that can provide high bandwidth. To accomplish this goal, we propose a converged medium access control (MAC) protocol for the RoF network, which uses a single-gate or multi-gate polling algorithm for time and wavelength allocation to the users. We propose an analytical delay model for single and multiple wavelength architectures, considering the user identification and data transfer delay for single-gate and multi-gate polling algorithms. We verify it with the results obtained from simulations. Finally, we present the mean delay and packet loss rate of the proposed algorithm by varying the user load and traffic burstiness and prove that the proposed algorithm gives better delay performance and a lower packet loss than the state-of-the-art algorithms.

The impact of the traffic characteristics of secondary users (SUs) on the performance of cognitive radio networks (CRNs) should be understood for designing operation rules. This paper focuses on multi-type burst services and network congestion problem in CRNs and evaluates the QoS of SUs based on multiple cross-layer considerations. A two-state Markov-modulate Bernoulli process (MMBP-2) is adopted to model packet flows of SUs with different burst degrees in CRNs. We propose a two-dimensional discrete queuing model to consider spectrum access, burstiness of traffic, network congestion, channel environment, user activity and finite buffer. We construct an iterative algorithm to compute the steady-state distribution of the proposed queuing model and determine the performance metrics like the throughput, the average delay, the average queue length and the total packet loss probability. Numerical analysis evaluates the QoS of SUs under different burst environments.

In data center networks, many network-intensive applications leverage large fan-in and many-to-one communication to achieve high performance. However, the special traffic patterns, such as micro-burst and high concurrency, easily cause TCP Incast problem and seriously degrade the application performance. To address the TCP Incast problem, we first reveal theoretically and empirically that alleviating packet burstiness is much more effective in reducing the Incast probability than controlling the congestion window. Inspired by the findings and insights from our experimental observations, we further propose a general supporting scheme Adaptive Pacing (AP), which dynamically adjusts burstiness according to the flow concurrency without any change on switch. Additionally, a sender-based approach is proposed to estimate the flow concurrency. Another feature of AP is its broad applicability. We integrate AP transparently into different TCP protocols (i.e., DCTCP, L2DCT and D2TCP). Through a series of large-scale NS2 simulations and testbed experiments, we show that AP significantly reduces the Incast probability across different TCP protocols and the network goodput can be increased consistently by on average $7\times $ under severe congestion.

Power-efficient Data Center Networks (DCNs) have been proposed to save power of DCNs using OpenFlow. In these DCNs, the OpenFlow controller adaptively turns on/off links and OpenFlow switches to form a minimum-power subnet that satisfies the traffic demand. As the subnet changes, flows are dynamically routed and rerouted to the routes composed of active switches and links. However, existing flow scheduling schemes could cause undesired results: (1) power inefficiency: due to unbalanced traffic allocation on active routes, extra switches and links may be activated to cater to bursty traffic surges on congested routes, and (2) Quality of Service (QoS) fluctuation: because of the limited flow entry processing ability, switches may not be able to timely install/delete/update flow entries to properly route/reroute flows. In this paper, we propose AggreFlow, a dynamic flow scheduling scheme that achieves power efficiency and QoS improvement using three techniques: Flow-set Routing , Lazy Rerouting , and Adaptive Rerouting . Flow-set Routing achieves load balancing with a small number of flow entry operations by routing flows in a coarse-grained flow-set fashion. Lazy Rerouting spreads rerouting operations over a relatively long period of time, reducing the burstiness of entry operation on switches. Adaptive Rerouting selectively reroutes flow-sets to maintain load balancing. We built an NS3 based fat-tree network simulation platform to evaluate AggreFlow’s performance. The simulation results show that AggreFlow reduces power consumption by about 18%, yet achieving load balancing and improved QoS (low packet loss rate and reducing the number of processing entries for flow scheduling by 98%), compared with baseline schemes.

High-resolution studies of data center traffic at the network core uncover short-term bursty traffic patterns, periods of high buffer utilization that last for tens of microseconds and lead to packet loss and longer flow completion time tails. While recent attention has been directed towards studying the bursty traffic at the network core, less heed has been given to the origins of bursty traffic, e.g., host machines. In this study, we try to perform high-resolution traffic measurements in the host networking stack to quantify the impact of system software components on traffic burstiness. We enforce per-packet timestamping on the datapath using various techniques like NIC timestamping, eBPF, and direct kernel source modification and measure the gaps between egress packets under various configurations. We provide preliminary findings on how process scheduling can affect traffic burstiness.

Variable Bitrate (VBR) video encoding can provide much high quality-to-bits ratio compared to the widely adopted Constant Bitrate (CBR) encoding, and thus receives significant attentions by content providers in recent years. However, it is challenging to design efficient adaptive bitrate algorithms for VBR-encoded videos due to the sharply fluctuating chunk size and the resulting bitrate burstiness. In this paper, we propose a neural adaptive streaming framework called Vibra for VBR-encoded videos, which can well accommodate the high fluctuation of video chunk sizes and improve the quality-of-experience (QoE) of end users significantly. Our framework takes the characteristics of VBR-encoded videos into account, and adopts the technique of deep reinforcement learning to train a model for bitrate adaptation. We also conduct extensive trace-driven experiments, and the results show that Vibra outperforms the state-of-the-art ABR algorithms with an improvement of 8.17% -- 29.21% in terms of the average QoE.

In this paper, we consider a coordinated beamforming design for a multiple-input single-output bursty interference channel. In practical scenarios, the distributed medium access control mechanisms or the decentralized networking protocols across different users contribute to the bursty nature of network traffic. The potential gains provided by such burstiness can be exploited by feeding back the interference state information to the transmitters. Errors may occur during such feedback, thus causing uncertainties in the user traffic and resulting in complicated beamforming design problems. In this paper, we model such traffic uncertainties by using a hidden Markov model (HMM). Assuming that perfect channel state information (CSI) is available at the transmitters, our goal is to maximize the average system utility subjected to average power constraints under traffic uncertainties. We also extend the optimization problem to the imperfect CSI case. However, the resulting problem is highly nonconvex. Hence, we apply a series of convex approximation techniques. We further improve the approximation accuracy by using our proposed successive convex approximation (SCA) algorithms (HMM-SCA). In our simulation results, we observe that our proposed HMM-SCA provides a $46.3\%$ improvement in weighted sum rate over the SCA algorithm, in which the interference is assumed to be always present (NB-SCA). For the average weighted geometric mean rate, we observe that our proposed HMM-SCA also provides a $41.3\%$ improvement over the NB-SCA when user fairness is considered. Therefore, it is crucial to exploit bursty traffic for a practical wireless communication system when channel estimation errors are considered.

Firewalls are located at the front line of the network against outside threats. Performance modeling and analysis of network firewalls help to better understand their behavior and characteristics. Moreover, having an analytical model in hand helps firewall designers avoid developing multiple design alternatives and thus considerably reduce the design costs. Moreover, the network administrators can proactively identify the performance bottlenecks of the network and fix them before any malicious attack which targets the network or the firewall itself. In this paper, we propose a novel analytical approach for performance modeling and analysis of network firewalls based on a discrete-time queuing system in which the bursty nature of the incoming traffic is taken into account, where traditional queuing models such as $M/M/1$ model fails to capture peculiar characteristics of the Internet traffic. Throughput, packet loss, delay, and firewalls CPU utilization are employed as performance evaluation indicators in our proposed model. In addition, we introduce a potential DoS attack with a very low rate which can be launched against firewalls with different burstiness factors.

Although mature industrial wireless sensor network applications increasingly require low-power operations, deterministic communications, and end-to-end reliability, it is very difficult to achieve these goals because of link burstiness and interference. In this paper, we propose a novel link quality estimation mechanism named the burstiness distribution metric, which uses the distribution of burstiness in the links to deal with variations in wireless link quality. First, we estimated the quality of the link at the receiver node by counting the number of consecutive packets lost in each link. Based on that, we created a burstiness distribution list and estimated the number of transmissions. Our simulation in the Cooja simulator from Contiki-NG showed that our proposal can be used in scheduling as an input metric to calculate the number of transmissions in order to achieve a reliability target in industrial wireless sensor networks.

Restricting data plane processing to packet headers precludes analysis of payloads to improve routing and security decisions. DeepMatch delivers line-rate regular expression matching on payloads using Network Processors (NPs). It further supports packet reordering to match patterns in flows that cross packet boundaries. Our evaluation shows that an implementation of DeepMatch, on a 40 Gbps Netronome NFP-6000 SmartNIC, achieves up to line rate for streams of unrelated packets and up to 20 Gbps when searches span multiple packets within a flow. In contrast with prior work, this throughput is data-independent and adds no burstiness. DeepMatch opens new opportunities for programmable data planes.

LoRaWAN is one of the most promising standards for IoT applications. Slotted ALOHA has been recently proposed as LoRaWAnMac protocol, in an effort to achieve a performance improvement. However, the effect of traffic burstiness in a LoRaWAnenvironment has not been studied. Critical performance metrics, such as packet delay and network throughput, under bursty traffic remain to be evaluated. This paper explores the effect of bursty traffic in a Slotted-ALOHA based LoRaWAnnetwork. The network performance is studied via extensive simulation results, under traffic of different grades of burstiness (from low to high mean burst length) by using throughput and mean packet delay, as performance metrics and major conclusions are extracted.

With the rapid development of wireless network, the amount of heterogeneous services increases, which have significant differences in QoS requirements. However, the traditional service management methods realized by artificial distinction are difficult to satisfy the QoS requirements of services with high bandwidth and burstiness. Therefore, it has great significance to better use the limited network resources and design suitable QoS guarantee mechanisms for wireless network. In this paper, a reinforcement learning based QoS guarantee traffic scheduling algorithm RQTS is proposed to optimize the utilization rate of wireless network. First, the traffic scheduling optimization model is constructed based on Lyapunov theory, which sets the optimal system utilization as the objective and all the restrictions are transformed into queue stability problems. Then, the QoS routing method RQTS is adopted to solve the optimization problem through traffic access control and transmission path control, the effects of different transmission channels, transmission rates and transmission path delays of the system are examined. Simulation results show that, the proposed mechanism can satisfy the QoS requirements and optimize the weighted system utilization.

DDoS attacks plague the availability of online services today, yet like many cybersecurity problems are evolving and non-stationary. Normal and attack patterns shift as new protocols and applications are introduced, further compounded by burstiness and seasonal variation. Accordingly, it is difficult to apply machine learning-based techniques and defences in practice. Reinforcement learning (RL) may overcome this detection problem for DDoS attacks by managing and monitoring consequences ; an agent’s role is to learn to optimise performance criteria (which are always available) in an online manner. We advance the state-of-the-art in RL-based DDoS mitigation by introducing two agent classes designed to act on a per-flow basis, in a protocol-agnostic manner for any network topology. This is supported by an in-depth investigation of feature suitability and empirical evaluation. Our results show the existence of flow features with high predictive power for different traffic classes, when used as a basis for feedback-loop-like control. We show that the new RL agent models can offer a significant increase in goodput of legitimate TCP traffic for many choices of host density.

This paper proposes a novel haptic packet rate control scheme for networked teleoperation systems which improves the state-of-the-art perceptual deadband-based haptic data reduction approach. The proposed method uses a peak-suppressing adaptive deadband to mitigate the burstiness of the haptic traffic and to control the packet rate. Experimental results show that our method is able to reduce the overall network load by reducing the burstiness of the haptic data transmission. It can also stabilize haptic packet rate beneath a certain transmission limit while preserving a high quality of remote interaction.

The information that short-lived TCP flows provide on bandwidth estimation may benefit adaptive video streaming applications or may contribute towards the success of new TCP versions. To estimate the available bandwidth active probing techniques may be used, but they cause an intrusive effect on the network affecting TCP performance. Therefore, passive measurements are favored, though capturing traffic traces comes with its own challenges. In this paper, we use the feedback provided by TCP acknowledgments and perform the estimation from sender-side measurements only. However, difficulties arise when in the presence of discrete random cross traffic, multiple tight links, packet losses, and inaccurate timestamping in general, the acknowledgment packet gaps get distorted. To deal with noise-afflicted packet gaps, we consider a machine-learning approach, specifically the neural network, for estimating the available bandwidth. We also apply the neural network under a variety of notoriously difficult conditions that have not been included in the training, such as multiple tight links, heavy cross traffic burstiness and packet losses. We compare the performance of our proposed method with a state-of-the-art model-based technique, where our neural network approach shows improved performance.

This paper presents an intelligent collision avoidance mechanism within a carrier sense multiple access framework to instill efficiency and agility in random access performance in capillary machine-to-machine communication networks. Under a hierarchical infrastructure-based setting, a resourceful gateway/coordinator node serves as the conduit between the resource-limited end-nodes and the application/service level network nodes. The coordinator periodically executes the dynamic algorithm, which determines the appropriate backoff window size for the present estimated load, and the end-nodes are opportunistically updated with this window size. Based on key analytic properties derived from a stochastic model of the associated protocol, the intelligent collision avoidance mechanism is shown to be efficient in aggregate throughput, equitable among the nodes regarding packet service times and energy efficiency, and agile in responding to intermittent node activity and traffic elasticity/burstiness. Numerical results show that the intelligent collision avoidance mechanism significantly outperforms the usually deployed binary exponential backoff in most of the settings, while matching a normative protocol fair closely.

Modern industrial and data center networks adopt multipath TCP (MPTCP) to achieve low communication latency. To make full use of multiple paths, MPTCP divides one TCP connection into multiple subflows. Due to the connection-level ACK blocking, however, MPTCP suffers from the traffic burstiness, which leads to large queueing delay, frequent packet drops, and even TCP timeout. To mitigate the traffic burstiness caused by ACK blocking, we propose MPTCP-RB (Multipath TCP with Reduced Burstiness), which interleaves consecutive packets across multiple subflows, instead of assigning consecutive packets to individual subflows. MPTCP-RB is implemented as a loadable Linux kernel module, working on any traditional MPTCP-based schemes without any other modifications on existing network stacks. The testbed and NS2 experiments show that, MPTCP-RB reduces the average flow completion time by up to 91% compared with the state-of-the-art MPTCP-based schemes.

Making idle servers sleep is considered to be a key approach to reducing energy consumption of various information and communication systems. Optimal sleeping policies for a single server have been derived only for non-bursty traffic in prior work. In this paper, for the first time, we study the optimal sleeping operation for a single server with bursty traffic to answer the question of whether server sleeping can bring extra benefit with bursty traffic or not. Key factors including switchover energy consumption as well as delay performance are considered. We formulate the problem as a partially observable Markov decision process, and show that it can be solved by observing time elapsed since the last arrival. The optimal sleeping policy is shown to be a two-threshold policy with a wait-and-see feature, i.e., the server would wait a period of time to see if there are any extra arrivals before switching modes. Simulation results show that with the optimal sleeping mechanism, traffic burstiness can enhance system performance on energy cost and delay penalty.

The Third Generation Partnership Project (3GPP) Release 16 defines the sensing-based semi-persistent scheduling (SPS) as the resource allocation scheme for Sidelink Mode 2 in New Radio (NR)-based vehicle-to-everything (V2X) communication. A well-known issue in Mode 2 is the persistent packet collision that results from two or more vehicles repeatedly using the same resource for transmission. It may create serious safety problems when the vehicles are in a situation where only the broadcast safety beacons can assist in driving. To resolve this issue, a solution that relies on the feedback from neighboring vehicles is proposed, through which the vehicles suffering from persistent packet collisions can quickly part and select other resources. Extensive simulations show that the proposed broadcast feedback scheme reduces persistent packet collisions by an order of magnitude compared to SPS, and it is achieved without sacrificing the average packet reception ratio (PRR). Namely, it is the quality aspect (i.e., burstiness) of the packet collisions that the proposed scheme addresses rather than the quantity (i.e., total number of collision losses). By preventing extended packet loss events, the proposed scheme is expected to serve NR V2X better, which requires stringent QoS in terms of the information update delay thereby helping to reduce the chances of vehicle crashes.

The ZigBee communication can be easily and severely interfered by Wi-Fi traffic. Error recovery, as an important means for ZigBee to survive Wi-Fi interference, has been extensively studied in recent years. The existing works add upfront redundancy to in-packet blocks for recovering a certain number of random corruptions. Therefore, the bursty nature of ZigBee in-packet corruptions under Wi-Fi interference is often considered harmful, since some blocks are full of errors which cannot be recovered and some blocks have no errors but are still requiring redundancy. As a result, they often use interleaving to reshape the bursty errors, before applying complex FEC codes to recover the re-shaped random distributed errors. In this paper, we take a different view that burstiness may be helpful. With burstiness, the in-packet corruptions are often consecutive and the requirement for error recovery is reduced as “recovering any k consecutive errors” instead of “recovering any random k errors”. This lowered requirement allows us to design far more efficient code than the existing FEC codes. Motivated by this implication, we exploit the corruption burstiness to design a simple yet effective error recovery code using XOR operations (called ZiXOR). ZiXOR uses XOR code and the delay is significantly reduced. More, ZiXOR uses RSSI-hinted approach to detect in packet corruptions without CRC, incurring almost no extra transmission overhead. The testbed evaluation results show that ZiXOR outperforms the state-of-the-art works in terms of the throughput (by 47 percent) and latency (by 22 percent).

In this paper, throughput and energy efficiency of secure wireless transmission of delay sensitive data generated by random sources is studied. A fading broadcast model in which the transmitter sends confidential and common messages to two receivers is considered. It is assumed that the common and confidential data, generated from Markovian sources, are stored in buffers prior to transmission, and the transmitter operates under constraints on buffer/delay violation probability. Under such statistical quality-of-service constraints, effective capacity of time-varying wireless transmissions and effective bandwidth of Markovian sources are employed to determine the throughput. In particular, secrecy capacity is used to describe the service rate of buffers containing confidential messages. Moreover, energy per bit is used as the energy efficiency metric and energy efficiency is studied in the low signal-to-noise regime. Specifically, minimum energy per bit required for the reliable communication of common and confidential messages is determined and wideband slope expressions are identified. The impact of buffer/delay constraints, correlation between channels, source characteristics/burstiness, channel knowledge at the transmitter, power allocation, and secrecy requirements on the throughput and energy efficiency of common and confidential message transmissions is identified.

In this paper, we have worked on the bursty synthetic traffic for Gaussian and Non-Gaussian traffic traces on the NoC architecture. This is the first study on the performance of Gaussian and Non-Gaussian application traffic on the multicore architectures. The real-time traffic having the marginal distribution are Non-Gaussian in nature, so any analytical studies or simulations will not be accurate, and does not capture the true characteristics of application traffic. Simulation is performed on synthetic generated traces for Gaussian and Non-Gaussian traffic for different traffic patterns. The performance of the two traffics is validated by simulating the parameters of packet loss-probability, average link-utilization & average end-to-end latency shows that the Non-Gaussian traffic captures the burstiness more effectively as compared to the Gaussian traffic for the desired application.

Packet loss and delay in wireless networks is a function of both the traffic characteristics (e.g. load, burstiness) and the characteristic behaviour of the fading channel. In this study, the authors propose and analyse a new queue model in which the server process is controlled by the adaptive modulation and coding (AMC) algorithm, and with aggregated ON–OFF packet arrivals to a finite buffer. In the scenarios, where users require multiple services, aggregated ON–OFF arrivals approximate bursty behaviour better than the Poisson arrivals analysed in many earlier queueing models. They focus on the packet drop probability, the packet loss probability, the average queueing delay and the throughput, showing significant differences in these metrics between the cross-layer models using aggregated ON–OFF arrivals and Poisson arrivals. This indicates that in wireless packet network performance evaluation, the traffic model is at least as important as the effect of the fading channel modelling.

Transmission Control Protocol with Network Coding (TCP/NC) has been a potential proposal to improve the transmission performance of TCP in lossy networks while it still takes advantage of congestion and retransmission control of TCP for reliable end-to-end data transfer. TCP/NC uses additional sub-layer called Network Coding layer below the TCP layer to handle packet losses without sensed by the TCP layer. In our previous work, we introduced some variants of TCP/NC such as TCP/NCwLRLBE (TCP/NC with Loss Rate and Loss Burstiness Estimation) which can improve the retransmission and adapt to the changing of the channel. However, it is just evaluated on the simple packet loss conditions, e.g., random loss or Gilbert loss. On the other hand, reliable end-to-end data transfer in diverse IoT (Internet of Things) environments (e.g., Power Line Communication - PLC) in a cost-efficient manner is an emerging challenge. Therefore, in this paper, we investigate the characteristics of the TCP/NC on PLC networks, to have a more in-depth evaluation of its performance and applicability in a practical PLC environment with many conditions causing packet loss such as noise variants, distance, etc. The result of our simulation on ns-3 (Network Simulation 3) shows that TCP/NC can work well on PLC environment compared to the traditional protocols such as TCP NewReno with Selective Acknowledgment (SACK) and TCP Westwood+.

We study a dynamic resource-allocation problem that arises in various parallel computing scenarios, such as mobile cloud computing, cloud computing systems, Internet of Things systems, and others. Generically, we model the architecture as client mobile devices and static base stations. Each client “arrives” to the system to upload data to base stations by radio transmissions and then “leaves.” The problem, called Station Assignment , is to assign clients to stations so that every client uploads their data under some restrictions, including a target subset of stations, a maximum delay between transmissions, a volume of data to upload, and a maximum bandwidth for each station. We study the solvability of Station Assignment under an adversary that controls the arrival and departure of clients, limited to maximum rate and burstiness of such arrivals. We show upper and lower bounds on the rate and burstiness for various client arrival schedules and protocol classes. To the best of our knowledge, this is the first time that Station Assignment is studied under adversarial arrivals and departures.

Transmission Control Protocol with Network Coding (TCP/NC) is studied to improve the goodput performance of the standard TCP in lossy networks (e.g., wireless networks). TCP/NC uses additional sub-layer called Network Coding layer below TCP layer to handle packet losses without sensed by TCP layer. Basically, n+k combination packets combined from n original packets are sent by the source. When the sink receives enough n combination packets, the sink can calculate n original packet even though k ' packets are lost where k ' is less than or equal to k. Most versions of TCP/NC consider the loss in only the direction of sending data; however, the loss in the reverse direction, i.e., the direction of sending an acknowledgment, affects seriously to goodput performance, especially in TCP/NC with an automatic estimation of Network Coding parameters n and k) case. In this paper, we propose a new scheme for the bi-directional loss issue. The result of our simulation on ns-3 (Network Simulation 3) shows that the proposed scheme can work well when loss happens in both directions compared to the TCP NewReno and our previously proposed, TCP/NC with Loss Rate and Loss Burstiness Estimation (TCP/NCwLRLBE)., 18th International Conference on Electronics, Information, and Communication (ICEIC 2019), January 22 - 25, 2019, Pullman Auckland Hotel, New Zealand

© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. QUIC is a new transport protocol under standardization since 2016. Initially developed by Google as an experiment, the protocol is already deployed in large-scale, thanks to its support in Chromium and Google's servers. In this paper we experimentally analyze the performance of QUIC in WiFi networks. We perform experiments using both a controlled WiFi testbed and a production WiFi mesh network. In particular, we study how QUIC interplays with MAC layer features such as IEEE 802.11 frame aggregation. We show that the current implementation of QUIC in Chromium achieves sub-optimal throughput in wireless networks. Indeed, burstiness in modern WiFi standards may improve network performance, and we show that a Bursty QUIC (BQUIC), i.e., a customized version of QUIC that is targeted to increase its burstiness, can achieve better performance in WiFi. BQUIC outperforms the current version of QUIC in WiFi, with throughput gains ranging between 20% to 30%.

A software-based network emulator is widely used for performance evaluation of communication software and protocols thanks to lower cost and higher extensibility compared with a hardware-based approach. However, NetEm, which is a popular software-based network emulator in Linux, suffers from inaccurate emulation capability on high speed networks. The DPDK-based network emulator (DEMU) is a promising tool to address this problem, but it does not support packet loss emulation. In this paper, we design and implement the Gilbert-Elliott packet loss model on DEMU. Through experiments of TCP performance on a 10 Gigabit network environment, we demonstrate that (1) the proposed method accurately controls the packet loss ratio and burstiness, and (2) the TCP offload engine mechanism can degrade packet loss accuracy. Consequently, the accuracy of DEMU is 305 times higher than that of NetEm for random packet loss emulation.

Interference, a major bottleneck in modern wireless communication, is not always present in many practical situations. In fact, due to the bursty nature of traffic in wireless networks, the corresponding interference is often bursty too. Such burstiness, if properly exploited, can provide significant performance gains. To investigate such potential gains, a multiple-user multiple-input single-output bursty interference channel is considered here. It is assumed that interference between users is present with a certain probability. On the basis of the knowledge of interference status, each transmitter adopts a different beamforming strategy and communication rate. Under this setting, we aim to maximize the average system utility and consider the optimal beamforming design when perfect channel state information is assumed at transmitters. The corresponding optimization problem is nonconvex and difficult to solve. To handle such difficulties, we apply a series of convex approximation techniques such as semidefinite relaxation and first-order approximation. Furthermore, we improve the accuracy of our approximation through solving the approximated problem successively and propose successive convex approximation (SCA) algorithms. The near-optimal performances of our proposed SCA algorithms are demonstrated by simulations. Our results show that significant performance gains can be achieved by exploiting the bursty nature of wireless interference networks.

We propose autonomous dynamic window shaping and rerouting to improve the throughput of the Internet of Things (IoT) on a service-converged layer-2 network with a time-aware shaper (TAS) accommodating mobile fronthaul (MFH) and IoT backhaul. While low latency for MFH is assured by the TAS technique, the maximum throughput of the IoT traffic is limited because the available bandwidth for the IoT is limited. Our dynamic window shaping and rerouting improves the IoT throughput autonomously by utilizing the burstiness feature of the MFH traffic. Experimental results showed that our techniques increased overall throughput from 3.9 to 7.9 Gbps under certain conditions.

Previous works on multicast capacity mainly focus on deriving asymptotic order results in large-scale wireless networks. While they can explore the general scaling laws of throughput capacity, it is also of great interest for practical concern to predict the exact achievable throughput in networks with an arbitrary finite number of nodes. In this paper, we investigate the nonasymptotic throughput and delay of multihop wireless networks for multicast applications wherein for each source node, $k$ nodes are randomly selected as receivers. It is challenging for the exact performance analysis since multicast transmission has a dynamic nature due to the following factors: 1) random distribution of nodes; 2) bursty traffic arrivals; and 3) different timescales for transient analysis. To tackle the problem, we propose an explicit analytical model and develop a multicast routing scheme, which accounts for these aspects. With our proposed model, we derive lower bound (LB) and upper bound (UB) on nonasymptotic multicast throughput and delay using stochastic network calculus. We show that the performance results hold for all timescales and network sizes and are strongly correlated to data burstiness and the number of receivers. While we investigate from a nonasymptotic point of view, our results can also cover the asymptotic scaling laws. Simulations are conducted to further verify the accuracy of the analytical bounds.

In this paper, we study sleeping control (SC) and power matching (PM) for a single cell in cellular networks with bursty traffic. The base station (BS) sleeps whenever the system is empty and wakes up when N users are assembled during the sleep period. The service capacity of the BS in the active mode is controlled by adjusting its transmit power. The total power consumption and average delay are analyzed, and based on this, the impact of parameter N and transmit power on the energy–delay tradeoff is studied. It is shown that, given the average traffic load, the more bursty the traffic is, the less total power consumed, although the delay performance of more bursty traffic is better only under certain circumstances. The optimal energy–delay tradeoff is then obtained through joint SC and PM optimization. The relationship between the optimal control parameters and the asymptotic performance are also provided. Moreover, the influence of the traffic autocorrelation is explored, which shows less impact on the system performance compared with that of the burstiness. Numerical results show the energy saving gain of the joint SC and PM scheme, as well as the impact of burstiness on the optimal energy–delay tradeoff.

We consider a relay-assisted wireless network, where the energy-harvesting buffer-aided relay node is powered by radio-frequency signals from a source node wishing to communicate with its destination. We propose two secure cooperative protocols for a network composed of a source node equipped with a data buffer communicating with its destination in the presence of a buffer-aided relay node and an eavesdropper. Our proposed protocols are designed based on the channel state information and the buffer state information at the source and relay nodes. The burstiness of data at the source node queue (buffer) and the energy recycling process at the relay are taken into account in our analysis. In addition, we take the decoding and signal processing circuit power consumption constraints into consideration. For the limiting case of an infinite-size battery at the relay, we derive a sufficient condition for the energy queue to saturate. Our numerical results demonstrate the throughput gains of our proposed protocols., Published in IEEE Wireless Communications Letters in October 2015, http://ieeexplore.ieee.org/document/7296606/

The gigantic growth of the internet communication technology has illustrated its value and benefits to private businesses, government organizations, worldwide professionals, academic institutes and individuals over the past few years. The size and range of computing devices connected to the internet, substantially increased because of IPv6 and offers the potential to establish a much more powerful internet compared to the IPv4. IPv6 developed by the IETF to deal with a shortage of IP addresses under IPv4. New features of IPv6 enhance packet processing speeds over routers, switches and end systems. These improved features will have different traffic characteristics than IPv4. The internet traffic which was earlier assumed as Poisson is now shown to have fractal characteristics as; heavy tailedness, self-similarity and long range dependency. Internet traffic showing above characteristics are found to have burstiness at multiple timescales. This behavior impacts network performance and degrades it substantially. It also increases complexity for network design and create difficulties to maintain desired QoS. IPv4 traffic has been well established as self-similar traffic. Nowadays, IPv6 forming a larger share of the internet traffic and it is pivotal to asses IPv6 with regards to fractal behavior. This will enable network designers to do necessary changes in the existing network to reconcile with IPv6. In this paper we compared IPv4 and IPv6 with respect to fractal behavioral characteristics. It is found that IPv6 shows higher degree of heavy tailedness, higher values of Hurst parameter values, higher fractal dimension values i.e. it is more self-similar, greater autocorrelation achieved even at larger lag and thus showing more burstiness.

Due to frequency selectivity, long propagation delay caused by low speed of sound, and fast channel variation, multiple-access protocols that work well in terrestrial networks may not perform as well in underwater environment. We take a physical-layer approach and investigate frequency-selective underwater channels with uncoordinated multiple bursty transmissions. We first derive the statistical distribution of the interference as seen by a typical receiver, considering burstiness of interference transmissions, packet length, and spatial distributions of the interferers. We then derive expressions for the probability density functions of the frequency-dependent signal-to-interference-and-noise ratio (SINR). Expressions for the outage probability of a typical link between two nodes are obtained in the frequency-selective scenarios and the flat-fading special cases. The outage probabilities depend on the bursty transmission probability and the number of interfering transmitters. Previous expressions in the literature on outage probability for flat fading channels and interference-limited systems can be recovered as special cases of our results. Analysis on the throughput of network is provided. Optimization over the number of nodes and bursty transmission probability is performed to maximize the network throughput. Simulation results are presented, which validate the theoretical results and illustrate typical features of the interference in an underwater network.

Micro-burst traffic is not uncommon in data centers. It can cause packet dropping, which results in serious performance degradation (e.g., Incast problem). However, current solutions that attempt to suppress micro-burst traffic are extrinsic and ad hoc, since they lack the comprehensive and essential understanding of micro-burst's root cause and dynamic behavior. On the other hand, traditional studies focus on traffic burstiness in a single flow, while in data centers micro-burst traffic could occur with highly fan-in communication pattern, and its dynamic behavior is still unclear. To this end, in this paper, we re-examine the microburst traffic in typical data center scenarios. We find that evolution of micro-burst is determined by both TCP's self-clocking mechanism and bottleneck link. Besides, dynamic behaviors of micro-burst under various scenarios can all be described by the slope of queue length evolution. Our observations also implicate that conventional solutions like absorbing and pacing are ineffective to mitigate micro-burst traffic. Instead, senders need to slow down as soon as possible. Inspired by the findings and insights from experimental observations, we propose S-ECN policy, which is an ECN marking policy leveraging the slope of queue length evolution. Transport protocols utilizing S-ECN policy can suppress the sharp queue length increment by over 2×, and reduce the average query completion time by ~12-27%.

Distributed denial of service (DDoS) attacks has caused huge economic losses to society. They have become one of the main threats to Internet security. Most of the current detection methods based on a single feature and fixed model parameters cannot effectively detect early DDoS attacks in cloud and big data environment. In this paper, an adaptive DDoS attack detection method (ADADM) based on multiple-kernel learning (MKL) is proposed. Based on the burstiness of DDoS attack flow, the distribution of addresses, and the interactivity of communication, we define five features to describe the network flow characteristic. Based on the ensemble learning framework, the weight of each dimension is adaptively adjusted by increasing the interclass mean with a gradient ascent and reducing the intraclass variance with a gradient descent, and the classifier is established to identify an early DDoS attack by training simple multiple-kernel learning (SMKL) models with two characteristics including interclass mean squared difference growth (M-SMKL) and intraclass variance descent (S-SMKL). The sliding window mechanism is used to coordinate the S-SMKL and M-SMKL to detect the early DDoS attack. The experimental results indicate that this method can detect DDoS attacks early and accurately.

We investigate routing on networks modeled as multiple access channels, when packets are injected continually. An energy cap is a component of the system, understood as a bound on the number of stations that can be switched on simultaneously. Each packet is injected into some station and needs to be delivered to its destination station via the channel. A station has to be switched on in order to receive a packet when it is heard on the channel. Each station manages when it is switched on and off by way of a programmable wake-up mechanism, which is scheduled by a routing algorithm. Packet injection is governed by adversarial models that determine upper bounds on injection rates and burstiness. We develop deterministic distributed routing algorithms and assess their performance in the worst-case sense. An algorithm knows the number of stations but does not know the adversary. One of the algorithms maintains bounded queues for the maximum injection rate 1 subject only to the energy cap 3. This energy cap is provably optimal, in that obtaining the same throughput with the energy cap 2 is impossible. We give algorithms subject to the minimum energy cap 2 that have latency polynomial in the total number of stations~n for each fixed adversary of injection rate less than 1. An algorithm is k-energy-oblivious if at most k stations are switched on in a round and for each station the rounds when it will be switched on are determined in advance. We give a k-energy-oblivious algorithm that has packet delay O(n) for adversaries of injection rates less than (k-1)/(n-1), and show that there is no k-energy-oblivious stable algorithm against adversaries with injection rates greater than k/n. An algorithm routes directly when each packet makes only one hop from the station into which it is injected straight to its destination. We give a k-energy-oblivious algorithm routing directly, which has latency O(n^2/k) for adversaries of sufficiently small injection rates that are O(k^2/n^2). We develop a k-energy-oblivious algorithm routing directly, which is stable for injection rate k(k-1)/n(n-1), and show that no k-energy-oblivious algorithm routing directly can be stable against adversaries with injection rates greater than k(k-1)/n(n-1).

Overlay NoCs, such as Hoplite, are cheap to implement on an FPGA but provide no bounds on worst-case routing latency of packets traversing the NoC due to deflection routing. In this paper, we show how to adapt Hoplite to enable calculation of precise upper bounds on routing latency by modifying the routing function to prioritize deflections, and by regulating the injection of packets to meet certain throughput and burstiness constraints. We provide an analytical model for computing end-to-end latency in the form of (1) in-flight time in the network Tf, and (2) waiting time at the source node Ts. To bound in-flight time in an m × m NoC, we modify the routing function and switching crossbar richness in the Hoplite router to deliver Tf = ΔΧ + ΔΥ + (AY × m) + 2 where AX and ΔΥ are differences of the source and destination address co-ordinates of the packet. To bound the waiting time at the source, we add a Token Bucket regulator with rate ρ i and burstiness σ i for each flow f i of node (x, y) to deliver (⌈1/ρ i ⌉ − 1) + Ts: Ts = ⌈σ(ΓC f )/1−σ(ΓC f );⌉ which depends on the regulator period 1/ρ i , burstiness a and the rate ρ of all interfering flows ΓC f . A 64b implementation of our HopliteRT router requires ≈4% fewer LUTs, and similar number of FFs compared to the original Hoplite router. We also need two small counters at each client port for regulating injection. We evaluate our model and RTL implementation across synthetic traffic patterns and observe behavior that conforms with the analytical bounds.

Time- and wavelength- division multiplexed passive optical network (TWDM-PON) have attracted attention as the next step in relation to optical access systems. A major application of TWDM-PON is expected to be fronthaul streams in the centralized radio access network (C-RAN) architecture of mobile networks. In the 5G era, the upstream traffic that OLT receives becomes highly bursty, because of the variable data rate generated by the functional split between baseband unit and remote radio heads, and the global synchronization of data transmission with time-division duplex (TDD). To flexibly allocate upstream bandwidth via the dynamic wavelength and bandwidth allocation (DWBA) scheme has been a hot research topic for TWDM-PON. However, there is no existing DWBA scheme for TWDM-PON that is designed to satisfy the strict delay requirement for fronthaul with minimum number of active wavelength channels. Therefore, in this paper we propose a novel efficient DWBA algorithm that minimizes active wavelength channels considering the high burstiness of fronthaul data transmission. With the proposed algorithm, the allowable delay and constraint for allocation timing for each ONU is formulated based on the different propagation delay between the OLT and ONUs. Then, the wavelength channel and bandwidth is allocated with the simple allocation algorithm based on the descending sort by propagation delay. It was confirmed through computer simulations that the proposed algorithm can reduce the number of active wavelength by 50% by considering propagation delay.

With high variability and correlation in arrival rates and packet sizes, multimedia traffic places a great strain on Wireless Local Area Networks (WLANs) towards provision of satisfactory Quality- of-Service (QoS). Most existing performance models for wireless networks are restricted to unrealistic assumptions where specific traffic characteristics of burstiness, correlation and self-similarity are ignored. This paper proposes an original analytical model as a cost-effective tool to evaluate the performance of WLANs in the presence of heterogeneous multimedia traffic, capturing the burstiness, correlation and self- similarity characteristics using Batch Markovian Arrival Process (BMAP). The model derives important QoS metrics in terms of throughput, end- to-end delay and frame loss probability. Analytical results validated through extensive simulations reveal the degrading effect of burstiness, correlation and heterogeneity of traffic sources on the QoS performance of WLANs.

This paper gives models and analytic techniques for studying shaping and policing data traffic in fog networks. The traffic in these networks is expected to be highly diverse and bursty, and regulation will be required as an integral part of congestion control. We generalize the Leaky Bucket model to shape and police traffic source for rate-based congestion control in high-speed fog networks. In particular, the Markov modulated fluid sources reflect the bursty characteristics of data traffic. To measure the performance of the model in shaping and policing traffic, we derive four performance metrics. The experimental results show that with proper design the Leaky Bucket model effectively controls a 4-way trade-off between throughput, loss probability, delay and burstiness of data traffic. Numerical results also reveal that the model performance is sensitive to certain traffic source characteristics.

In order to simultaneously achieve good energy efficiency and high packet delivery performance, a multihop forwarding scheme should generally involve three design elements: media access mechanism, link estimation scheme, and routing strategy. Disregarding the low-duty-cycle nature of media access often leads to overestimation of link quality. Neglecting the bursty loss characteristic of wireless links inevitably consumes much more energy than necessary and underutilizes wireless channels. The routing strategy, if not well tailored to the above two factors, results in poor packet delivery performance. In this paper, we propose $L^{2}$ , a practical design of data forwarding in low-duty-cycle wireless sensor networks. $L^{2}$ addresses link burstiness by employing multivariate Bernoulli link model. Further incorporated with synchronized rendezvous, $L^{2}$ enables sensor nodes to work in a lazy mode, keep their radios off most of the time, and realize highly reliable forwarding by scheduling very limited packet transmissions. We implement $L^{2}$ on a real sensor network testbed. The results demonstrate that $L^{2}$ outperforms state-of-the-art approaches in terms of energy efficiency and network yield.

The work of Marcelo Bagnulo has been partially supported by project MASSES (TEC2012-35443) funded by the Spanish Ministry of Economy and Competitiveness (MINECO). The advertisement of more-specific prefixes provides network operators with a fine-grained method to control the interdomain ingress traffic. Prefix deaggregation is recognized as a steady long-lived phenomenon at the interdomain level, despite its well-known negative effects for the community. In this paper, we look past the original motivation for deploying deaggregation in the first place, and instead we focus on its aftermath. We identify and analyze here one particular side-effect of deaggregation regarding the economic impact of this type of strategy: decreasing the transit traffic bill. We propose a general Internet model to analyze the effect of advertising more-specific prefixes on the incoming transit traffic burstiness. We show that deaggregation combined with selective advertisements has a traffic stabilization side-effect, which translates into a decrease of the transit traffic bill. Next, we develop a methodology for Internet Service Providers (ISPs) to monitor general occurrences of prefix deaggregation within their customer base. Thus, the ISPs can detect selective advertisements of deaggregated prefixes, and thus identify customers which impact the business of their providers. We apply the proposed methodology on a complete set of data including routing, traffic, topological and billing information provided by a major Japanese ISP and we discuss the obtained results. Publicado

Transmission Control Protocol (TCP) performs poorly over wireless networks. Some research indicates that the TCP congestion control mechanism may cause burstiness in the traffic flow. Numerous TCP segments are delivered simultaneously, while an acknowledgement of a retransmission is successfully received. Burstiness leads to a highly contentious network, which markedly increases the probability of packet loss on wireless networks. TCP pacing is a possible solutions for TCP burstiness on multi-hop networks. In this algorithm, TCP segment transmissions are distributed over the whole Round Trip Time. Most pacing protocols attempt to insert a delay interval into the TCP transmissions. However, there is a similar pacing algorithm in IEEE802.11, known as the contention window mechanism. In this paper, we first measure and analyze the way that the contention window size affects TCP throughput in different scenarios. We propose a cross-layer TCP pacing protocol by contention control in the MAC layer, called TCP Contention Control (TCP-CC). It adjusts the lower bound of the contention window in order to optimize the overall TCP throughput in both one-hop and multi-hop topology. Finally, comparative simulations are conducted in order to verify the improvements of our protocol on both TCP Reno and TCP Vegas.

© 2014, Springer Science+Business Media New York. Large scientific data transfers often occur at high rates causing increased burstiness in Internet traffic. To limit the adverse effects of these high-rate large-sized flows, which are referred to as $$\alpha $$α flows, on delay-sensitive audio/video flows, a network management system called Alpha Flow Traffic Engineering System (AFTES) is proposed for intra-domain traffic engineering. An offline approach is used in which AFTES analyzes NetFlow records collected by routers, extracts source–destination address prefixes of $$\alpha $$α flows, and uses these prefixes to configure firewall filters at ingress routers of a provider’s network to redirect future $$\alpha $$α flows to traffic-engineered paths and isolated queues. The effectiveness of this scheme was evaluated through an analysis of 7 months of NetFlow data obtained from an ESnet router. For this data set, 91 % of bytes generated by $$\alpha $$α flows during high-rate intervals would have been directed had AFTES been deployed. The negative aspect of using address prefixes in firewall filters, i.e., the redirection of $$\beta $$β flows to $$\alpha $$α-flow paths/queues, was also quantified.

Distributed Denial of Service (DDoS) attacks have been significant threats to the Internet. Traditional research in cyber security focuses on detecting emerging DDoS attacks by tracing network package flow. A characteristic of DDoS defense is that rescue time is limited since the launch of attack. More resilient detection and defence models are typically more costly. We aim at predicting the likelihood of DDoS attacks by monitoring relevant text streams in social media, so that the level of defense can be adjusted dynamically for maximizing cost-effect. To our knowledge, this is a novel and challenge research question for DDoS rescue. Because the input of this task is a text stream rather than a document, information should be collected both on the textual content of individual posts. We propose a fine-grained hierarchical stream model to capture semantic information over infinitely long history, and reveal burstiness and trends. Empirical evaluation shows that social text streams are indeed informative for DDoS forecasting, and our proposed hierarchical model is more effective compared to strong baseline text stream models and discrete bag-of-words models.

In order to mitigate interference, a major bottleneck in modern wireless networks, we consider coordinated beamforming design for a multiple-input single-output (MISO) bursty interference channel. In realistic scenarios, the distributed medium access control mechanisms or the decentralized networking protocols across different users contributes to the bursty nature of network traffic. Exploiting such burstiness judiciously can potentially provide significant performance gain. In practice, error may occur when transmitters attempt to determine whether the traffic is bursty or not from the interference state information received from the control center. Here, we model such traffic uncertainties via a hidden Markov model (HMM). Assuming that perfect channel state information (CSI) is available at transmitters, our goal is to maximize the average system utility subject to quality-of-service constraints and average power constraints under traffic uncertainties. The resulting problem is highly non-convex. We hence apply a series of convex approximation techniques (e.g., and semidefinite relaxation, first-order approximation) to handle the non-convex problem. We further improve the approximation accuracy by our proposed successive convex approximation (SCA) algorithm. Simulation results show that the performance of our proposed SCA algorithm is close to that achieved by the optimal beamforming when the interference state information is assumed to be perfectly known. Even under traffic uncertainties, our proposed SCA algorithm still performs better (9.5% improvement) compared with the case where the bursty nature of the interference is not exploited at all. It is hence essential to consider bursty traffic even for a practical wireless system.

In data center networks, many network-intensive applications leverage large fan-in and many-to-one communication to achieve high performance. However, the special traffic patterns, such as micro-burst and high concurrency, easily cause TCP Incast problem and seriously degrade the application performance. To address the TCP Incast problem, we first reveal theoretically and empirically that alleviating packet burstiness is much more effective in reducing the Incast probability than controlling the congestion window. Inspired by the findings and insights from our experimental observations, we further propose a general supporting scheme Adaptive Pacing (AP), which dynamically adjusts burstiness according to the flow concurrency without any change on switch. Another feature of AP is its broad applicability. We integrate AP transparently into different TCP protocols (i.e., DCTCP, L2DCT and D2TCP). Through a series of large-scale NS2 simulations, we show that AP significantly reduces the Incast probability across different TCP protocols and the network goodput can be increased consistently by on average 7x under severe congestion.

Proliferation of mobile digital devices has posed major challenges for design and implementation of Wireless Local Area Networks (WLANs), leading to stringent quality of service (QoS) requirements. Extreme burstiness and correlation characteristics of multimedia traffics generated by digital devices require accurate modelling and analysis to provide satisfactory end user experience. Most existing performance models for WLANs are developed under unrealistic assumptions where burstiness and correlation characteristics of network traffic are ignored. This paper proposes an original analytical model as a cost-effective tool to evaluate the performance of WLANs in the presence of realistic video traffic, taking into account burstiness and correlation characteristics using Batch Markovian Arrival Process (BMAP). The model derives important QoS metrics in terms of throughput, end-to-end delay and frame loss probability. The accuracy of the obtained results are validated via extensive simulation experiments executed in NS2 subject to traffic parameters obtained from real-world video sources generated by practical multimedia applications. The results reveal how burstiness and correlation of video traffic affects QoS performance of WLANs.