Baselining network-wide traffic by time-frequency constrained Stable Principal Component Pursuit

Defect detection is to find unexpected peak regions in an inspection image. Stable Principal Component Pursuit (SPCP) decomposes a given image into three matrices, low-rank, sparsity, and noise which are used for detecting defects. Each of them contains repeated pattern, spatially narrow abnormal elements which are regarded as defects, and small magnitude elements respectively. However, if the noise level of the image is spatially varied, it is hard to separate noise appropriately using naive SPCP. To overcome the difficulty, we propose a novel sliding-window based SPCP algorithm. First, a repeated pattern of each sliding-window is converted to a matrix for SPCP. The noise level based on rank-one approximation is then estimated, and the matrix decomposition is performed. Finally, the sparsity values of all sliding-windows are merged by averaging, and then the averaged term is used for defect detection. The experimental results show that our algorithm outperforms the traditional approaches.

The problem of recovering the low-rank and sparse components of a matrix is known as the stable principal component pursuit (SPCP) problem. It has found many applications in compressed sensing, image processing, and web data ranking. This paper proposes a generalized inexact Uzawa method for SPCP with nonnegative constraints. The main advantage of our method is that the resulting subproblems all have closed-form solutions and can be executed in distributed manners. Global convergence of the method is proved from variational inequalities perspective. Numerical experiments show that our algorithm converges to the optimal solution as other distributed methods, with better performances.

The reconstruction of the microvasculature from contrast enhanced ultrasound sequences by ultrasound localization microscopy offers the opportunity to detect early pathological changes in microvessels. It has been applied successfully to preclinical and clinical data, e.g., for the characterization and differentiation of tumors. The localization of the injected microbubbles with subwavelength accuracy is the basic principle of the reconstruction. However, tissue motion has a strong influence on the microbubble positions and reduces the quality of the final vessel reconstructions. Therefore, an estimation and correction of the tissue motion is necessary that has to be accurate at the bubble positions and should not be disturbed by the superimposed, independent MB motion. In this paper, we present a method to get an accurate estimation of the tissue motion without the disturbance of MB motion, by applying a stable principal component pursuit to the estimated vector field. Simulations confirm the accurate motion estimation, and the application to an ultrasound sequence of a mouse brain shows an improved reconstruction of vessel trees.

In process industries, multivariate statistical process monitoring (MSPM) has become an important technique for enhancing product quality and operation safety. Among the family of MSPM methods, principal component analysis (PCA) may be the most commonly used one, due to its capabilities of dimensionality reduction and highlighting variation in a data set. However, the performance of a PCA model often degrades significantly when there are gross sparse errors, i.e. outliers, contained in the training data, since PCA assumes that the training data matrix only contains an underlying low-rank structure corrupted by dense noise. In this paper, a robust matrix recovery method named stable principal component pursuit (SPCP) is adopted to solve this problem, based on which a process modeling and online monitoring procedure is developed. Such a method inherits the benefits of PCA while being robust to outliers. The effectiveness of the SPCP-based monitoring method is illustrated using the benchmark Tennessee Eastma...

Network traffic prediction is critical in wireless network management by allowing a good estimate of the traffic trend, which is also an important approach for detecting traffic anomalies in order to enhance network security. Deep-learning-based method has been widely adopted to predict network traffic matrix (TM) though with the main drawbacks in high complexity and low efficiency. In this paper, we propose a traffic prediction model based on Particle Swarm Optimization (PSO) and LightGBM (PSO-LightGBM-TM), which optimizes the LightGBM parameters for each network flow by PSO so that LightGBM can adapt to each of the network traffic flow. Compared with existing commonly used deep learning models, our model has a more straightforward structure and yet outperforms existing deep learning models. Sufficient comparison tests on three real network traffic datasets, Abilene, GÉANT, and CERNET have been conducted, and the results show that our model provides more accurate results and higher prediction efficiency.

Traffic Matrix (TM) prediction is defined as the problem of estimating future network traffic matrices based on historical network traffic data. It is widely applied in network planning, resource management, and network security. In this paper, we propose a CAGNN (Cross-aggregate GNN) model for predicting the traffic size of OD (Origin-Destination) pairs, and develop a TM prediction framework based on this model.Unlike traditional GNN models, our CAGNN model leverages the cross-aggregation of information from the network node graph and edge graph to obtain node representations and edge representations in the network. This cross-aggregation approach allows us to integrate different forms of traffic information in the network, providing a foundation for training downstream prediction models. We validate our framework on real traffic data from the Abilene network and compare our prediction performance with that of the traditional LSTM-RNN framework. The experimental results demonstrate that our model achieves superior TM prediction performance.

We consider the network traffic monitoring issue, including traffic matrix estimation and anomaly detection problems. New model called structure regularized traffic monitoring (SRTM) is presented by incorporating the spatial and temporal properties of the traffic matrices, with the sparsity of the traffic volume anomalies. The proposed SRTM model adapts more naturally to the engineering nature of the real traffic data and imposes structural regularizations. This stems from the fact that the real traffic data is usually approximately instead of exactly low-rank and exhibits strong spatial and temporal dependencies. In addition, we take advantages of the sparsity of the traffic volume anomalies and impose sparse constraints on the variables to detect the traffic anomalies. Efficient algorithm that based on the proximal gradient and gradient descent methods are proposed to solve the SRTM optimization model. Experiments in synthetic networks are carried out to verify the feasibility and effectiveness of the proposed model.

Traffic Matrix (TM) is a representation of all traffic flows in a network. It is helpful for traffic engineering and network management. It contains the traffic measurement for all parts of a network and thus for larger network it is difficult to measure precisely. Link load are easily obtainable but they fail to provide a complete TM representation. Also link load and TM relationship forms an under-determined system with infinite set of solutions. One of the well known traffic models Gravity model provides a rough estimation of the TM. We have proposed a Genetic algorithm (GA) based optimization method to further the solutions of the Gravity model. The Gravity model is applied as an initial solution and then GA model is applied taking the link load-TM relationship as a objective function. Results shows improvement over Gravity model.

Despite a large body of literature and methods devoted to the Traffic Matrix (TM) estimation problem, the inference of traffic flows volume from aggregated data still represents a major issue for network operators. Directly and frequently measuring a complete TM in a large-scale network is costly and difficult to perform due to routers limited capacities. In this paper we introduce and evaluate a new method to estimate a TM from easily available link load measurements. The method uses a novel statistical learning technique to unveil the relation between links traffic volume and origin-destination flows volume. By training a system based on Random Neural Networks, we provide a fast and accurate TM estimation tool that attains proper results without assuming any traffic model or particular behavior. Using real data from an operational backbone network, we compare this new method to the most well known and accepted TM estimation techniques, including in the evaluation some more accurate and up-to-date methods developed in recent works. Results show that current TM estimation techniques can still be improved.

Internet traffic analysis, models simulation and prediction play a very important part in the network management and design.Combining wavelet techniques and time-series ARIMA model, the establishment of a network traffic prediction model in this paper. First, time series of wavelet decomposition of flow to gets detail coefficients and approximation coefficients. On the details coefficients, applying the stationary series model, on the approximation coefficients, applying difference method, then using the stationary series ARIMA model to predict. At last, applying the actual network traffic to verify the model. The results show that the model has higher prediction accuracy.

A Delay based MILP for Network Planning in Optical Networks

Robust PCA-based Walking Direction Estimation via Stable Principal Component Pursuit for Pedestrian Dead Reckoning

The complexity of computer systems, networks and applications, as well as the advancements in computer technology, continue to grow at a rapid pace. Mathematical analysis, modeling and optimization have been playing, and continue to play, an important role in research studies to investigate fundamental issues and trade-offs at the core of performance problems in the design and implementation of complex computer systems, networks and applications. On June 20, 2014, the 16th Workshop on MAthematical performance Modeling and Analysis (MAMA 2014) was held in Austin TX, USA, sponsored by ACM SIGMETRICS, and held in conjunction with SIGMETRICS 2014. This workshop seeks to bring together researchers working on the mathematical, methodological and theoretical aspects of performance analysis, modeling and optimization. It is intended to provide a forum at SIGMETRICS conferences for talks on early research in the more mathematical areas of computer performance analysis. These talks tend to be based on very recent research results (including work in progress) or on new research results that will be otherwise submitted only to a journal (or recently have been submitted to a journal). Thus, part of the goal is to complement and supplement the SIGMETRICS Conference program with such talks without removing any theoretical contributions from the main technical program. Furthermore, we continue to experience the desired result of having abstracts from previous MAMA workshops appear as full papers in the main program of subsequent SIGMETRICS and related conferences. All submissions were reviewed by at least 4 members of the program committee, from which a total of 13 were selected for presentation at the MAMA 2014 workshop. This special issue of Performance Evaluation Review includes extended abstracts relating to these presentations (arranged in the order of their presentation), which cover a wide range of topics in the area of mathematical performance analysis, modeling and optimization. The study of Gelenbe examines the backlog of energy and of data packets in a sensor node that harvests energy, computing the properties of energy and data backlogs and discussing system stability. Meyfroyt derives asymptotic results for the coverage ratio under a specific class of spatial stochastic models (Cooperative Sequential Adsorption) and investigates the scalability of the Trickle communication protocol algorithm. The study of Tune and Roughan applies the principle of maximum entropy to develop fast traffic matrix synthesis models, with the future goal of developing realistic spatio-temporal traffic matrices. Bradonjić et al. compare and contrast the capacity, congestion and reliability requirements for alternative connectivity models of large-scale data centers relative to fat trees. The study of Rochman et al. considers the problem of resource placement in network applications, based on a largescale service faced with regionally distributed demands for various resources in cloud computing. Xie and Lui investigate the design and analysis of a rating system and a mechanism to encourage users to participate in crowdsourcing and to incentivize workers to develop high-quality solutions. The study of Asadi et al. formulates a general problem for the joint per-user mode selection, connection activation and resource scheduling of connections using both LTE and WiFi resources within the context of device-todevice communications. Zheng and Tan consider a nonconvex joint rate and power control optimization to achieve egalitarian fairness (max-min weighted fairness) in wireless networks, exploiting the nonlinear Perron-Frobenius theory and nonnegative matrix theory. The study of Goldberg et al. derives an asymptotically optimal control policy for a stochastic capacity problem of dynamically matching supply resources and uncertain demand, based on connections with lost-sales inventory models. Ghaderi et al. investigate a dynamic stochastic bin packing problem, analyzing the fluid limits of the system under an asymptotic best-fit algorithm and showing it asymptotically minimizes the number of servers used in steady state. The study of Tizghadam and Leon-Garcia examines the impact of overlaying or removing a subgraph on the Moore-Penrose inverse of the Laplacian matrix of an existing network topology and proposes an iterative method to find key performance measures. Miyazawa considers a two-node generalized Jackson network in a phase-type setting as a special case of a Markov-modulated twodimensional reflecting random walk and analyzes the tail asymptotics for this reflecting process. The study of Squillante et al. investigates improvement in scalability of search in networks through the use of multiple random walks, deriving bounds on the hitting time to a set of nodes and on various performance metrics.

Virtual topologies of WDM-based multihop networks can be arranged by assigning wavelengths to tunable transceivers of nodes. An optimal node arrangement problem in multihop lightwave networks with linearly virtual topologies is considered. For a given traffic matrix, this problem is to arrange node locations to adapt traffic patterns and optimise network performance. However, the problem of node arrangement for constructing optimised linear virtual topologies has been proved as NP-complete. Two lower bounds are proposed for this problem according to the traffic-weighted mean hop distance and the maximum link flow, respectively. An efficient node arrangement algorithm, called k-ENA, is also proposed. The performance of the k-ENA algorithm is compared with the best algorithms in previous work. Simulation results indicate that the k-ENA algorithm generally yields the best solutions.

In this paper, we study resource allocation design for secure communication in intelligent reflecting surface (IRS)-assisted multiuser multiple-input single-output (MISO) communication systems. To enhance physical layer security, artificial noise (AN) is transmitted from the base station (BS) to deliberately impair the channel of an eavesdropper. In particular, we jointly optimize the phase shift matrix at the IRS and the beamforming vectors and AN covariance matrix at the BS for maximization of the system sum secrecy rate. To handle the resulting non-convex optimization problem, we develop an efficient suboptimal algorithm based on alternating optimization, successive convex approximation, semidefinite relaxation, and manifold optimization. Our simulation results reveal that the proposed scheme substantially improves the system sum secrecy rate compared to two baseline schemes.