In datacenters, lossless network is very attractive as it can achieve ultra-low latency. In commodity Ethernet, lossless forwarding is achieved by hop-by-hop Priority-based Flow Control (PFC). To avoid buffer overflow, PFC-enabled switches need to reserve some buffer asheadroom, absorbing in-flight packets during the delay for backpressure messages to take effect. However, with the growing link speed in production networks, the buffer becomes increasingly insufficient, and the headroom can occupy a considerable fraction of buffer. As a result, the remaining buffer for absorbing normal traffic bursts is significantly squeezed, leading to frequent PFC messages that degrade the network performance. Worse yet, we find that the current static and queue-independent headroom allocation scheme is quite inefficient, resulting in significant buffer wastage. In light of this, we propose Dynamic and Shared Headroom allocation scheme (DSH), which dynamically allocates headroom to congested queues and enables sharing of allocated headroom among different queues. To achieve this, DSH first introduces port-level flow control, which performs flow control at the granularity of individual ports, guaranteeing lossless forwarding with a small fraction of per-port headroom. With this lossless guarantee, the switch is liberated for dynamic headroom adjustment. DSH dynamically allocates per-queue headroom based on the congestion status of each queue. Meanwhile, DSH preserves the queue-level flow control to protect the non-congested queues from being paused by congested queues, ensuring performance isolation on buffer sharing. Extensive experiments show that DSH can reduce the flow completion time by up to ~78.8%.

Recently, deep learning has emerged as a promising approach for solving challenging resource distribution (RD) problems in vehicle-to-everything (V2X) wireless networks. How-ever, existing neural network architectures lack scalability, in-terpretability, and generalization. To address these limitations, in this study, we propose a new flexible graph neural network (FGNN)-based resource distribution framework for vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) user selection and power management in V2X networks with several next- generation access points (APs) and a cluster of V2V and V2I communication users. In the proposed framework, we formu-lated an optimization problem with each V2V and V2I user with the least power constraint that adapts to V2X wireless network settings through training inactive users. Likewise, we consider the situation when every V2I user shares the band with a different group of V2V users. Moreover, we introduce a parameterization of the RD framework strategy employing a flexible graph neural network (FGNN) context derived from instantaneous channel conditions to learn the low-dimension features of every user/vehicle. To assess the execution of the framework, we conduct simulation experiments comparing it with baseline methods in terms of efficiency, sum rate, and fairness.

Traffic policing is widely used by ISPs to limit their customers’ traffic rates. It has long been believed that a well-tuned traffic policer offers a satisfactory performance for TCP. However, we find this belief breaks with the emergence of new congestion control (CC) algorithms: flows using new CC algorithms can easily occupy the majority of bandwidth, starving traditional TCP flows. We confirm this problem with experiments and reveal its root cause as follows. Without a buffer in traffic policers, congestion only causes packet losses, while new CC algorithms are loss-resilient. When being policed, they will not reduce the sending rate until an unacceptable loss ratio for TCP is reached, resulting in low throughput for competing TCP flows. Simply adding a buffer to the traffic policer improves fairness but incurs high latency. To this end, we propose FairPolicer, which can achieve fair bandwidth allocation without sacrificing latency. FairPolicer regards a token as a basic unit of bandwidth and fairly allocates tokens to active flows in a round-robin manner. To avoid bandwidth waste when flows come and go, FairPolicer puts all available tokens in a global bucket and maintains the amount of residual bucket space rather than the number of available tokens. To scale to massive concurrent flows, FairPolicer uses a Count-Min Sketch structure to maintain per-flow data with a small memory footprint. Testbed experiments show that FairPolicer can allocate bandwidth in a max-min fair manner and achieve much lower latency than other kinds of rate limiters.

In datacenters, lossless network is very attractive as it can achieve ultra-low latency. In commodity Ethernet, lossless forwarding is achieved by hop-by-hop Priority-based Flow Control (PFC). To avoid buffer overflow, PFC-enabled switches need to reserve some buffer as headroom, which is for absorbing in-flight packets during the delay for backpressure messages to take effect. However, with the growing link speed in production networks, the buffer becomes increasingly insufficient, and the headroom can occupy a considerable fraction of buffer. As a result, the remaining buffer for absorbing normal traffic bursts is significantly squeezed, leading to frequent PFC messages that degrade the network performance. However, the current static and queue-independent headroom allocation scheme is inherently inefficient in solving this problem. In light of this, we propose Dynamic and Shared Headroom allocation scheme (DSH), which dynamically allocates headroom to congested queues and enables the allocated headroom to be shared among different queues. By statistical multiplexing, DSH needs much less headroom to ensure lossless forwarding. Furthermore, DSH can be implemented on switching chips with moderate modifications. Extensive simulations show that DSH can absorb 4× more bursts without triggering PFC messages and reduce the flow completion time by up to ~31%.

Traffic shaping is a common function at end hosts. Compared with hardware ones, software shapers are more flexible to be developed and deployed, and thus are very attractive. Nevertheless, software approaches are still unsatisfactory as they struggle to saturate 40Gbps and higher speed.While much effort has been made to reduce the intrinsic overhead of software traffic shaping, we find that it is the extrinsic overhead, such as PCIe communications and interrupts, that hinders shaping from achieving 40Gbps - 100Gbps speed. Batching is an effective way to amortize these overheads. However, blindly batching can degrade the network performance, as it introduces bursts into the network. Diving into the dilemma, we find that intra-flow burst is to blame for harming the network performance, while inter-flow burst, consisting of packets from different flows, can be naturally demultiplexed in the network.Based on the insight, we present FlowBundler, which can achieve efficient traffic shaping by inter-flow batching. Testbed experiments show that FlowBundler can achieve an accurate shaping of 98Gbps with a single CPU core, which is 2.6× better than state-of-the-art approaches. Large-scale simulations show that FlowBundler can batch packet transmissions without harming the network performance.

Summary With the development and popularization of Internet technology, network security has become the focus of attention. Vulnerabilities and back doors are regarded as two of the main reasons of network security problems. Dynamic heterogeneous redundant (DHR) architecture is a typical framework of cyberspace mimic defense. It can make use of untrusted hardware and software components to construct a high reliable and high security information system. In this article, a mathematical model is set up for the DHR architecture, and the system security is characterized by vulnerability consistency rate and success rate of system attack. The antiattack ability of the DHR system is analyzed using the mathematical model.

Botnets are the machines that increasingly controlled by cybercriminals to perform various attacks. They use Domain Generation Algorithm (DGA) to frequently generate their illegitimate domains for preventing detection. To overcome such dynamics, existing solutions try to capture the characteristics of domain names, such that the automatically generated domains can be identified. However, those solutions are not conformed to the linguistic conventions of reading and writing. For a comprehensive understanding of strings of domain names, we present DOmain Linguistic PHonIcs detectioN (DOLPHIN), a novel method that can detect the illegitimate domain names generated by DGAs. Considering the correspondence between pronunciations and spellings, we design the DOLPHIN patterns. They are the classification of vowels and consonants in variable lengths as follow the principles of phonics. DOLPHIN recognizes strings of domain names and reconstructs them with the components of variable-length vowels and consonants following the DOLPHIN patterns. We implement the features used DOLPHIN in supervised learning methods and compare them to the fore-most method FANCI. Experimental results show that, compared to FANCI with RFs, DOLPHIN can achieve higher detection accuracy of 0.0238 in average with lower FPR without much overhead.

Software Defined Network (SDN) enables flexible update of network functions with a well-defined abstraction between the control and the data plane. However, multiple active network functions with the same priority will potentially trigger conflicts among policies with overlapped flow space, causing the flow table explosion. In contrast to the local switch conflict resolution schemes proposed by previous works, this paper tackles the same problem from a different angle and resolves the policy conflict problem by coordinating all switches under a global centralized view. Specifically, we propose COnflict RAzor (CORA), which tremendously reduces the storage cost of conflicting policies leveraging the global network information obtained in the controller. The basic idea of CORA is migrating policies causing large explosions across the network if necessary, while keeping the semantics equivalence. We prove CORA's NP hardness and propose a heuristic to efficiently search a near-optimal policy migration strategy. Our experiments demonstrate that, CORA can effectively reduce the flow table storage occupation by at least 49% within less than 40 seconds.

Identifying a user operating application function can reflect the user behavior, or even can help to improve the user experience.It is the focus of the real application in big data analytics technology.Unlike Coarse-grained Traffic Identification (CTI) which only identify application/protocol that a packet is related to, Fine-grained Function Traffic Identification (FFTI) maps the traffic packet to a meaningful user operation or an application function.In this paper, our focus is to identify the fine-grained function signature.We propose an automatic and stable signature generation method, so-called STAFF, to identify different application functions.STAFF treats data packets as long strings.The aim of our method is to find all the string fragments whose length is longer than a prescribed length and whose occurrence is higher than a prescribed frequency.The final signature will be presented as pairs of string fragments and their corresponding occurrence frequency.The experimental results show that STAFF can automatically generate finegrained function signatures in different applications with average 93.65% identification accuracy and the method is noise insensitive.

As the most competitive solution for next-generation network, SDN and its dominant implementation OpenFlow are attracting more and more interests. But besides convenience and flexibility, SDN/OpenFlow also introduces new kinds of limitations and security issues. Of these limitations, the most obvious and maybe the most neglected one is the flow table capacity of SDN/OpenFlow switches. In this paper, we proposed a novel inference attack targeting at SDN/OpenFlow network, which is motivated by the limited flow table capacities of SDN/OpenFlow switches and the following measurable network performance decrease resulting from frequent interactions between data and control plane when the flow table is full. To the best of our knowledge, this is the first proposed inference attack model of this kind for SDN/OpenFlow. We implemented an inference attack framework according to our model and examined its efficiency and accuracy. The evaluation results demonstrate that our framework can infer the network parameters (flow table capacity and usage) with an accuracy of 80% or higher. We also proposed two possible defense strategies for the discovered vulnerability, including routing aggregation algorithm and multilevel flow table architecture. These findings give us a deeper understanding of SDN/OpenFlow limitations and serve as guidelines to future improvements of SDN/OpenFlow.

Appearance and motion, which are complementary, account for a dominant proportion of visual information. We propose to synergize them using a low-rank representation framework for the estimation and analysis of traffic flow. Taking advantage of the downward-looking camera configuration, we do the processing only on the measure line, called virtual gantry, instead of dealing with the whole frame, resulting in much improved efficiency. Enforcing the low-rank constraint on the spatiotemporal image which is generated via stacking pixels on virtual gantry over time, we introduce the block-sparse robust principal component analysis algorithm, in which the motion cue is leveraged to highlight the foreground and realize vehicle detection with high accuracy. The motion flow is further exploited for size normalization to classify vehicles into lite, small, medium, and large categories. Benefiting from the low-rank representation, our method is parameter insensitive, robust to illumination changes, and requires no training. We perform extensive experiments on the 24/7 videos collected over the highways in China and Singapore, obtaining nearly 100% accuracy. Meanwhile, insightful observations on the obtained traffic information are given, which could be very valuable to the users, especially to the traffic management sectors.

In this paper, robust visual tracking scheme is achieved through a novel sparse tracking via collaborative motion and appearance (TCMA). A coarse-to-fine framework with both motion and holistic appearance information is taken into consideration. In coarse search, we employ an optical flow map for the generation of motion particles. A rough estimation of target image patch is obtained using l2-regularized least square method in coarse search stage. In fine search, a novel smooth term is proposed in the cost function to improve the robustness of the tracker. With this smooth term, the object appearance in the previous frame will also affect the calculation of sparse coefficient in the current frame. It allows the tracker involving temporal information between consecutive frames instead of only considering single frame appearance information as in the conventional sparse coding-based tracking algorithms. In order to reserve the original and latest appearance information simultaneously in the template, a quadratic-function-like weight allocation scheme combining with particle contributed histogrammic correlation is developed in the updating stage. Both qualitative and quantitative studies are conducted on a set of challenging image sequences. The superior performance over other state-of-the-art algorithms is verified through the experiment.

This paper presents a tracking method which decouples tracking process into translation and scale estimation steps. A coarse estimation step in translation is implemented with particle filter first. Then, a two layers of correlation filters is proposed to robustly estimate object variation in translation and scale accurately. The appearance of object is divided into several local blocks. Each block is a basic unit for data updating and it is capable of accurately locating the sub-context of target based on the trained block filters. A local weight vector is developed to structurally and flexibly formulate spatial-temporal transform feature map with online learning framework. The block-updated filters are assembled to a final tracker for the accurate translation estimation. To handle the adaptive scale variation, a sample pyramid based tracker is built to estimate the scale accurately. Experiments on the public benchmark demonstrate the advantage of proposed algorithm over the state-of-the-art approaches.

This paper presents a patch-based keypoints clustering method for long term robust visual tracking. We propose to employ a parallel framework with keypoints matching and estimation for tracking purpose. Patch-based method is implemented in our algorithm to improve the flexibility of system. The template is divided into patches to ensure the spatial constraint of local keypoints. The motion cue of patches is calculated with optical flow for consensus clustering and the outliers are suppressed for the final voting. To eliminate the error, we propose a two-step voting from global to local scope. The effective keypoints in global vote for a center and estimate the patch centers which will be compared with the voting centers from each individial patch keypoints. The final voting is determined by the voting with minimum error, which could robustly reduce the error due to the misclassified outliers. Finally, the experiments will be followed to validate the performance of proposed algorithm on the public benchmark.

Central catadioptric omnidirectional images exhibit serious nonlinear distortions due to the involved quadratic mirrors. Therefore, features based on the conventional pin-hole model are hard to achieve satisfactory performances when directly applied to the distorted omnidirectional images. This paper analyzes the catadioptric geometry to facilitate modeling the nonlinear distortions of omnidirectional images. Different to the conventional imaging model, the prior information is considered in catadioptric system. A parameterized neighborhood mapping model is proposed to efficiently calculate the neighborhood of an object based on its measurable radial distance in the image plane. On the basis of the parameterized nonlinear model, a distortion-invariant fragment-based joint-feature mixture model of Gaussian is presented for human target tracking in omnidirectional vision. Under the framework of Gaussian Mixture Model, the problem of feature matching is converted into the feature clustering. The joint probability distribution of a joint-feature class is modeled by a mixture of Gaussian. A weight contribution mechanism is designed to flexibly weight the fragments contribution based on their responses, which leads to a robust tracking even under serious partial occlusion. Finally, experiments validate the advantage of the proposed algorithm over other conventional approaches. Catadioptric omnidirectional cameras have been widely used in robotics and surveillance fields for visual sensing due to its big field-of-view. However, conventional visual models use large-scale statistical sampling for feature extraction in catadioptric sensor, which may consume lot of computational cost. For practical applications, a parameterized model that can accurately and efficiently formulate distortion of catadioptric image is desirable. Integrating of the priori of system, a parameterized neighborhood model is presented to directly extract distorted image content in image, which can significantly improve the efficiency of algorithm. To robustly handle challenging occlusion in the distorted image, a flexible fragment-based joint-feature framework is presented for robust non-rigid human target tracking. Compared with the conventional tracking methods applied to catadioptric vision, the proposed tracking approaches leads to much better performance from the perspective of efficiency and robustness.

Central catadioptric omnidirectional images exhibit serious nonlinear distortions due to quadratic mirrors involved. Conventional visual features developed based on the perspective model are hard to achieve a satisfactory performance when directly applied to the distorted omnidirectional image. This paper presents a parameterized neighborhood model to efficiently calculate the adaptive neighborhood of an object based on the measurable radial distance in image plane. On the basis of the parameterized neighborhood model, a distortion invariant joint-feature framework implemented with contour-color fragment mixture model of Gaussian is proposed for visual tracking in catadioptric omnidirectional camera system. Under the framework of Gaussian Mixture Model, the problem of feature matching is converted into feature clustering. A weight contribution mechanism is presented to flexibly weight the fragments based on their responses, which makes the system robustly guided by limited visible fragments even when serious partial occlusion happens. The experiments validate the performance of the proposed algorithm.

It has become a common practice that enterprises outsource their networks to the cloud by renting multiple virtual machines (VMs) in cloud datacenters. Due to the multi-tenant nature of cloud datacenter, how to efficiently share the network resources becomes an important issue. Recent studies, e.g., SecondNet and Oktopus, have taken network bandwidth into consideration when allocating VMs. However, these schemes are problematic in that the allocation is not that accurate, and can result in a low multiplexing rate. To this end, we present VirtualRack (VR), a new bandwidth-aware VM allocation scheme in multi-tenant datacenters. VR simultaneously considers intra-datacenter bandwidth and Internet-access-bandwidth requirements in the allocation process. In addition, we introduce a redundancy factor α that can be specified by tenants to accommodate their dynamic requirements. Simulation results show that VR can guarantee the network performance for each of the multiple tenants, and at the same time keep a high acceptance ratio without any false allocation.

Multi-target tracking in video is a challenge due to noisy video data, varying number of targets, and the data association problems. In this paper, a multi-target visual tracking system that incorporates object detection with the Gaussian mixture PHD filter is developed. The main contribution of this paper is to propose a new birth intensity online estimation method that based on the entropy distribution and the coverage rate. First, the birth intensity is initialized by using the previously obtained targets' states and measurements. The measurements are obtained by object detection and classified into the birth measurements and the survival measurements. Then it is updated according to the currently obtained birth measurements. In the update stage, the instability of the entropy distribution is applied to remove components like noises within the birth intensity which are irrelevant with the currently obtained birth measurements. And the coverage rate between each birth intensity component and corresponding birth measurement is computed to further eliminate the noises. Finally, experiments are implemented to show the performance of the proposed visual tracking system, especially to show the good performance for tracking the newborn targets.

We propose a robust visual tracker based on structured sparse representation appearance model. The appearance of tracking target is modeled as a sparse linear combination of Eigen templates plus a sparse error due to occlusions. We address the structured sparse representation that preferably matches the practical visual tracking problem by taking the contiguous spatial distribution of occlusion into account. The sparsity is achieved by Block Orthogonal Matching Pursuit (BOMP) for solving structured sparse representation problem more efficiently. The model update scheme, based on incremental Singular Value Decomposition (SVD), guarantees the Eigen templates that are able to capture the variations of target appearance online. Then the approximation error is adopted to build a probabilistic observation model that integrates with a stochastic affine motion model to form a particle filter framework for visual tracking. Thanks to the block structure of sparse representation and BOMP, our proposed tracker demonstrates superiority on both efficiency and robustness improvement in comparison experiments with publicly available benchmark video sequences.

The management of Extensible Markup Language (XML) Web services executing in mobile and/or embedded environments poses new challenges that result from the limited availability of computing resources and from the need for characteristic management activities, such as handling context sensitivity. This paper briefly describes management requirements for and design of our management infrastructure for monitoring of mobile/embedded Web services. Its architecture is based on extending a SOAP engine with management modules, an approach adopted by several existing tools for monitoring non-mobile/non-embedded Web services. Apart for modules for management of non-mobile/non-embedded Web services, our infrastructure contains additional support for handling context-sensitivity, disruptions in quality of service (QoS), and intermittent connectivity. We have been implementing the proof-of-concept prototype of this infrastructure as an extension of the Web Service Offerings Infrastructure (WSOI).