Software Defined Space Information Networks (SDSIN) is increasingly applied to life and production, the reason is that it can cope with various complex environments and tasks in the future communication environment. Owing to the performance advantages of the Software Defined Networking (SDN), some advanced technologies based on SDN are more and more applied to satellite network. In order to better provide users with high-quality services, network providers must predict and analyze End-to-End (E2E) traffic. Hence, this poses the natural question of how to accurately predict the future trend of E2E traffic to load balance, network optimization, and network security. Different from the previous 2-dimension terrestrial network, we study a 3-dimension SDSIN in this paper. Firstly, we analyze the difficulties and challenges of traffic engineering in SDSIN. Subsequently, an improved Hidden Markov Model (HMM) to E2E traffic prediction method is proposed. Finally, simulation results show that our improved HMM can be well applied for E2E traffic prediction in SDSIN.

A Prediction Approach to End-to-End Traffic in Space Information Networks

A traffic matrix is generally used by several network management tasks in a data center network, such as traffic engineering and anomaly detection. It gives a flow-level view of the network traffic volume. Despite the explicit importance of the traffic matrix, it is significantly difficult to implement a large-scale measurement to build an absolute traffic matrix. Generally, the traffic matrix obtained by the operators is imperfect, i.e., some traffic data may be lost. Hence, we focus on the problems of recovering these missing traffic data in this paper. To recover these missing traffic data, we propose the spatio-temporal Kronecker compressive sensing method, which draws on Kronecker compressive sensing. In our method, we account for the spatial and temporal properties of the traffic matrix to construct a sparsifying basis that can sparsely represent the traffic matrix. Simultaneously, we consider the low-rank property of the traffic matrix and propose a novel recovery model. We finally assess the estimation error of the proposed method by recovering real traffic.

Spatio-Temporal Kronecker Compressive Sensing for Traffic Matrix Recovery

Due to the prevalence of social network services, more and more attentions are paid to explore how information diffuses and users affect each other in these networks, which has a wide range of applications, such as viral marketing, reposting prediction and social recommendation. Therefore, in this paper, we review the recent advances on information diffusion analysis in social networks and its applications. Specifically, we first shed light on several popular models to describe the information diffusion process in social networks, which enables three practical applications, i.e., influence evaluation, influence maximization and information source detection. Then, we discuss how to evaluate the authority and influence based on network structures. After that, current solutions to influence maximization and information source detection are discussed in detail, respectively. Finally, some possible research directions of information diffusion analysis are listed for further study.

Study on Information Diffusion Analysis in Social Networks and Its Applications

Software defined networking (SDN) with separated control plane and data plane brings new opportunities for traffic measurement in data center networks. However, in the SDNenabled switches, available TCAM (Ternary Content Addressable Memory) resources for traffic measurement are limited. Thus, it is necessary to utilize traffic matrix (TM) estimation to derive a hybrid network monitoring scheme through combining the partial direct measurement offered by SDN with some inference techniques. Although large size flows play an important role in improving TM estimation accuracy, directly monitoring each flow and finding out large size flows consume massive channel bandwidth resource between control plane and data plane. Therefore, in this paper, we identify large size flows from multiple historical TMs instead of monitoring each flow. First, we analyze multiple historical TMs and observe that origin-todestination (OD) pair whose flow size is selected as large size flow at last time slot is most likely to be selected for per-flow monitoring at next time slot, so these OD pairs are identified by gradient boosting machine and are directly regarded as sampled OD pairs in order to reduce resource consumption. Then, we propose a greedy heuristic algorithm to solve SDN-enabled switch selection problem to best utilize the TCAM resources and guarantee that most of sampled OD pairs are measured in the flow table. We also present a source node prefix tree based bit merging aggregation (SPTBMA) scheme to design feasible forwarding rules to be inserted in TCAM of SDN-enabled switches and reserve more TCAM space for sampled OD pairs. Finally, the experimental results based on real traffic dataset demonstrate that our proposed scheme outperforms the existing algorithms in terms of improving TM estimation accuracy and overcoming limitation of TCAM resources.

SDN-based Traffic Matrix Estimation in Data Center Networks Through Large Size Flow Identification

We consider problems of traffic matrix (TM) estimation and anomaly detection utilizing link-load traffic measurements. Models including structure regularized traffic monitoring (SRTM) and dynamic SRTM (DSRTM) are presented to realize traffic monitoring under static and dynamic routing configurations, respectively. Considering that real traffic data are usually approximately low-rank but exhibit strong spatial and temporal dependencies, we define spatial and temporal regularization matrices based on Moore–Penrose pseudoinverse and Laplacian matrix to structurally regularize the TM variables. Besides, in view of the fact that anomalies in traffic usually happen rarely and last briefly, sparsity-regularization is further implemented on traffic volume anomalies. This enables our models to jointly deal with the traffic monitoring issues of TM estimation and anomaly detection. The SRTM model is designed for static routing configurations, and the online traffic monitoring model DSRTM is presented for dynamic settings. In DSRTM, a forgetting parameter is introduced to incorporate information from both the latest and previous estimations, getting rid of the problem that storing a large amount of traffic data is a huge burden for computers. Furthermore, real-time monitoring enables DSRTM to be applied in scenarios that usually experience nonstationary. Efficient algorithms that are based on accelerated proximal gradient, gradient descent, and block coordinate descent methods are proposed to solve the related SRTM and DSRTM optimization problems, with experiments in synthetic and real networks under both static and dynamic routing configurations, verifying their feasibility and effectiveness.

Structure Regularized Traffic Monitoring for Traffic Matrix Estimation and Anomaly Detection by Link-Load Measurements

In this paper, we study the problem of identifying the source of network diffusion processes by using the contact networks and sampled infections. The dynamic processes are described by the susceptible-infected (SI) model and a maximum likelihood (ML) estimator is formulated for identification of the source. We propose a two-stage method that first locates a set of suspected source nodes and then identifies the infection source from the candidate source nodes by using Markov random field method. Experiments on regular tree and real-world networks verify the effectiveness of our approach.

Source identification of network diffusion processes with partial observations

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.

Structure regularized traffic monitoring model for traffic matrix estimation and anomaly detection

We consider the problem of semi-supervised graph-based learning. Since in semi-supervised settings, the labeled information is limited, we first propose l α -norm-based label propagation (α-SLP) model to estimate the soft labels by using small set of labeled and large amount of unlabeled training data, and thereby enrich the supervised information. Based on the a-SLP results, we conduct semi-supervised discriminant analysis and present graph-based embedding (SGE) approach by incorporating the estimated soft labels with the local geometric information of both the within-class and between-class training data. Within-class affinity matrices and between-class weight matrix are introduced to preserve the propagated label information and local geometric information of data. This gets rid of the problem that merely concerning about the soft labels may lead to errors. By minimizing the locality-preserved within-class distances and maximizing the weighted between-class separability, subspaces that characterize the intrinsic data structure can be well captured. Experiments in face recognition verify the validity and effectiveness of the proposed methods.

Semi-supervised discriminant analysis based on sparse-coding theory

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Source identification of network diffusion processes with partial observations

Structure regularized traffic monitoring model for traffic matrix estimation and anomaly detection

Semi-supervised discriminant analysis based on sparse-coding theory