An SDN/NFV Based Framework for Management and Deployment of Service Based 5G Core Network

The traffic explosion and the rising of diverse requirements lead to many challenges for traditional mobile network architecture on flexibility, scalability, and deployability. To meet new requirements in the 5 G era, service based architecture is introduced into mobile networks. The monolithic network elements(e.g., MME, PGW, etc.) are split into smaller network functions to provide customized services. However, the management and deployment of network functions in service based 5 G core network are still big challenges. In this paper, we propose a novel management architecture for 5 G service based core network based on NFV and SDN. Combined with SDN, NFV and edge computing, the proposed framework can provide distributed and on-demand deployment of network functions, service guaranteed network slicing, flexible orchestration of network functions and optimal workload allocation. Simulations are conducted to show that the proposed framework and algorithm are effective in terms of reducing network operating cost.

Joint Optimization of Latency and Energy Consumption for Mobile Edge Computing Based Proximity Detection in Road Networks

In recent years, artificial intelligence and automotive industry have developed rapidly, and autonomous driving has gradually become the focus of the industry. In road networks, the problem of proximity detection refers to detecting whether two moving objects are close to each other or not in real time. However, the battery life and computing capability of mobile devices are limited in the actual scene,which results in high latency and energy consumption. Therefore, it is a tough problem to determine the proximity relationship between mobile users with low latency and energy consumption. In this article, we aim at finding a tradeoff between latency and energy consumption. We formalize the computation offloading problem base on mobile edge computing(MEC)into a constrained multiobjective optimization problem(CMOP) and utilize NSGA-II to solve it. The simulation results demonstrate that NSGA-II can find the Pareto set, which reduces the latency and energy consumption effectively. In addition, a large number of solutions provided by the Pareto set give us more choices of the offloading decision according to the actual situation.

Off-Grid Compressed Channel Estimation with Parallel Interference Cancellation for Millimeter Wave Massive MIMO

Millimeter wave(mmWave)massive multiple-input multiple-output(MIMO)plays an important role in the fifth-generation(5G)mobile communications and beyond wireless communication systems owing to its potential of high capacity.However,channel estimation has become very challenging due to the use of massive MIMO antenna array.Fortunately,the mmWave channel has strong sparsity in the spatial angle domain,and the compressed sensing technology can be used to convert the original channel matrix into the sparse matrix of discrete angle grid.Thus the high-dimensional channel matrix estimation is transformed into a sparse recovery problem with greatly reduced computational complexity.However,the path angle in the actual scene appears randomly and is unlikely to be completely located on the quantization angle grid,thus leading to the problem of power leakage.Moreover,multiple paths with the random distribution of angles will bring about serious interpath interference and further deteriorate the performance of channel estimation.To address these off-grid issues,we propose a parallel interference cancellation assisted multi-grid matching pursuit(PIC-MGMP)algorithm in this paper.The proposed algorithm consists of three stages,including coarse estimation,refined estimation,and inter-path cyclic iterative inter-ference cancellation.More specifically,the angular resolution can be improved by locally refining the grid to reduce power leakage,while the inter-path interference is eliminated by parallel interference cancellation(PIC),and the two together improve the estimation accuracy.Simulation results show that compared with the traditional orthogonal matching pursuit(OMP)algorithm,the normalized mean square error(NMSE)of the proposed algorithm decreases by over 14dB in the case of 2 paths.

Analysis of Malware Application Based on Massive Network Traffic

Security and privacy issues are magnified by velocity, volume, and variety of big data. User's privacy is an even more sensitive topic attracting most people's attention. While XcodeGhost, a malware of i OS emerging in late 2015, leads to the privacy-leakage of a large number of users, only a few studies have examined XcodeGhost based on its source code. In this paper we describe observations by monitoring the network activities for more than 2.59 million i Phone users in a provincial area across 232 days. Our analysis reveals a number of interesting points. For example, we propose a decay model for the prevalence rate of Xcode Ghost and we find that the ratio of the infected devices is more than 60%; that a lot of popular applications, such as Wechat, railway 12306, didi taxi, Youku video are also infected; and that the duration as well as the traffic volume of most Xcode Ghost-related HTTP-requests is similar with usual HTTP-request which makes it difficult to be found. Besides, we propose a heuristic model based on fingerprint and its web-knowledge to identify the infected applications. The identifying result shows the efficiency of this model.

An Offloading Scheme Leveraging on Neighboring Node Resources for Edge Computing over Fiber-Wireless (FiWi) Access Networks

The computation resources at a single node in Edge Computing(EC)are commonly limited,which cannot execute large scale computation tasks.To face the challenge,an Offloading scheme leveraging on NEighboring node Resources(ONER)for EC over Fiber-Wireless(FiWi)access networks is proposed in this paper.In the ONER scheme,the FiWi network connects edge computing nodes with fiber and converges wireless and fiber connections seamlessly,so that it can support the offloading transmission with low delay and wide bandwidth.Based on the ONER scheme supported by FiWi networks,computation tasks can be offloaded to edge computing nodes in a wider range of area without increasing wireless hops(e.g.,just one wireless hop),which achieves low delay.Additionally,an efficient Computation Resource Scheduling(CRS)algorithm based on the ONER scheme is also proposed to make offloading decision.The results show that more offloading requests can be satisfied and the average completion time of computation tasks decreases significantly with the ONER scheme and the CRS algorithm.Therefore,the ONER scheme and the CRS algorithm can schedule computation resources at neighboring edge computing nodes for offloading to meet the challenge of large scale computation tasks.

Analysis and Prediction of Content Popularity for Online Video Service:A Youku Case Study

Understanding the characteristics and predicting the popularity of the newly published online videos can provide direct implications in various contexts such as service design, advertisement planning, network management and etc. In this paper, we collect a real-world large-scale dataset from a leading online video service provider in China, namely Youku. We first analyze the dynamics of content publication and content popularity for the online video service. Then, we propose a rich set of features and exploit various effective classification methods to estimate the future popularity level of an individual video in various scenarios. We show that the future popularity level of a video can be predicted even before the video's release, and by introducing the historical popularity information the prediction performance can be improved dramatically. In addition, we investigate the importance of each feature group and each feature in the popularity prediction, and further reveal the factors that may impact the video popularity. We also discuss how the early monitoring period influences the popularity level prediction. Our work provides an insight into the popularity of the newly published online videos, and demonstrates promising practical applications for content publishers,service providers, online advisers and network operators.

WoT/SDN:Web of Things Architecture Using SDN

Wo T(Web of Things) integrates smart devices into Web by reusing and extending Web standards. While Web technology makes the developers' job easier,it faces security,management and efficiency challenges. We propose Wo T/SDN,the architecture of resource oriented Wo T built on SDN(Software Defined Network),in which applications could be developed through resource subscription and Mashup with the programmability provided by SDN. The key components are designed,including Security and Management Controller(SMC),various atomic services and resource subscription syntax. Three applications covering device management,data access and security protection are demonstrated. Compared to traditional resource-oriented Wo T systems,our test results show that SDN,with its logically centralized control capability and awareness of flow forwarding,provides new opportunity to improve performance,simplify management and enhance security for Wo T.

Link Maintenance Scheme for OFDM-Based Cognitive Radio Ad Hoc Networks

Transmission among the cognitive users(CUs)' is always interrupted by the primary users(PUs)' reclaim of the spectrum and the fading effect of wireless channels.To maintain reliable continuous communication among CUs,an efficient scheme for link maintenance in OFDM-based cognitive radio Ad Hoc networks is proposed in this paper.In the scheme,redundant sub-channels(RSC) are employed by CUs to tackle the outage problem caused by the PUs' reclaim of the spectrum and wireless fading effect in the transmission.Meanwhile,backup sub-channels(BSC) are arranged to help select sub-channels with better channel quality.Additionally,to lower the overhead and improve the accuracy of the spectrum sensing,a partial sensing module is designed to enable the CUs to inherit and update the original idle spectrum list until it expires.Simulation results show that the proposed scheme can realize higher throughput and lower sensing overhead with slight reduction of the forced termination probability(FTP) performance compared to the existing approaches.

Reversed and Bidirectional Irrigation Methods for Equal-Scale Virtual Network Embedding Problem

Previous Virtual Network (VN) embedding researches mostly focus on developing heuristic algorithms to enhance the efficiency of a physical resource. However, in the equal-scale condition, where the scale of a VN is similar to that of a substrate network, the number of successfully mapped VNs decreases sharply since bottlenecks form easily in the substrate network and disturb the embedding process. In this paper, reversed and bidirectional irrigation methods are proposed for the equal-scale and all-scale conditions. The two proposed methods can be combined with most of the existing heuristic algorithms and map a relatively large number of VNs by reducing the potential substrate bottlenecks. The simulation results show that the reversed irrigation method almost doubles the successfully mapped Revenue than the traditional one in the equal-scale condition. Meanwhile, the bidirectional irrigation method achieves the synthetically best performance in almost all scale conditions.

Mode switch between SFN and PTP for multicast services in LTE networks

The multimedia broadcast and multicast services (MBMS) in 3GPP LTE is characterized by multicast broadcast single frequency network (MBSFN) operation. The multicast services are transmitted by single frequency network (SFN) mode, and the unicast services are delivered with point-to-point (PTP) mode. To minimize the demanded radio resources for multicast services in the LTE, a novel mode switch (MS) between SFN and PTP for multicast services is proposed. MS takes into account PTP mode for multicast services and the switch is based on the handover threshold. To solve the minimization problem, MS adapts the handover threshold with the difference in the demanded radio resources between SFN mode and PTP mode for multicast services. The simulation results show that the proposed MS achieves less demanded radio resources than SFN mode for all the multicast services.

Building mm Wave on the Evolving C-V2X:mm Wave NR-V2X

Since the evolving standardization of cellular V2X(C-V2X)technologies is continuously developed by 3GPP,research on new radio(NR-V2X)has been on schedule by academic and industries.Though millimeter wave(mm Wave)frequency band is supposed to provide large transmission bandwidth,yet the development of mm Wave band in NR-V2X is still in preliminary stage.In this article,a comprehensive discussion of mm Wave NR-V2X is given covering trends,standardization landscape,and enabling technologies,aiming at tackling the challenge of channel modeling,directional channel access,beamforming and blockage management.The vision of mm Wave NR-V2X is to fully support the development of automated driving,holographic control display and improved in-car infotainment for the future.

A Topology-Cognitive Algorithm Framework for Virtual Network Embedding Problem

The virtual network embedding/mapping problem is an important issue in network virtualization in Software-Defined Networking(SDN).It is mainly concerned with mapping virtual network requests,which could be a set of SDN flows,onto a shared substrate network automatically and efficiently.Previous researches mainly focus on developing heuristic algorithms for general topology virtual network.In practice however,the virtual network is usually generated with specific topology for specific purpose.Thus,it is a challenge to optimize the heuristic algorithms with these topology information.In order to deal with this problem,we propose a topology-cognitive algorithm framework,which is composed of a guiding principle for topology algorithm developing and a compound algorithm.The compound algorithm is composed of several subalgorithms,which are optimized for specific topologies.We develop star,tree,and ring topology algorithms as examples,other subalgorithms can be easily achieved following the same framework.The simulation results show that the topology-cognitive algorithm framework is effective in developing new topology algorithms,and the developed compound algorithm greatly enhances the performance of the Revenue/Cost(R/C) ratio and the Runtime than traditional heuristic algorithms for multi-topology virtual network embedding problem.

Characterizing Internet Backbone Traffic Based on Deep Packets Inpection and Deep Flows Inspection

Based on the massive data collected with a passive network monitoring equipment placed in China's backbone,we present a deep insight into the network backbone traffic and evaluate various ways for improving traffic classifying efficiency in this paper.In particular,the study has scrutinized the network traffic in terms of protocol types and signatures,flow length,and port distribution,from which meaningful and interesting insights on the current Internet of China from the perspective of both the packet and flow levels are derived.We show that the classification efficiency can be greatly improved by using the information of preferred ports of the network applications.Quantitatively,we find two traffic duration thresholds,with which 40% of TCP flows and 70% of UDP flows can be excluded from classification processing while the impact on classification accuracy is trivial,i.e.,the classification accuracy can still reach a high level by saving 85% of the resources.

A Fast Multi-Pattern Matching Algorithm for Mining Big Network Data

The rapid development of mobile network brings opportunities for researchers to analyze user behaviors based on largescale network traffic data. It is important for Internet Service Providers(ISP) to optimize resource allocation and provide customized services to users. The first step of analyzing user behaviors is to extract information of user actions from HTTP traffic data by multi-pattern URL matching. However, the efficiency is a huge problem when performing this work on massive network traffic data. To solve this problem, we propose a novel and accurate algorithm named Multi-Pattern Parallel Matching(MPPM) that takes advantage of HashMap in data searching for extracting user behaviors from big network data more effectively. Extensive experiments based on real-world traffic data prove the ability of MPPM algorithm to deal with massive HTTP traffic with better performance on accuracy, concurrency and efficiency. We expect the proposed algorithm and it parallelized implementation would be a solid base to build a high-performance analysis engine of user behavior based on massive HTTP traffic data processing.

Cluster-Based Interference-Free MAC Protocol with Load Aware in Software Defined VANET

In order to reduce the interference,a novel,cluster-based medium access control(MAC)protocol with load aware for VANETs is proposed in this paper.First,all vehicles on roads are grouped into stable clusters in the light of their direction,number of neighbors,link reliability,and traffic load.By utilizing the advantages of centralized control in software defined VANETs(SDVN),cluster stability can be maintained in real-time.Second,a contention-free MAC mechanism composed of inter-cluster multi-channel allocation and intra-cluster dynamic TDMA frame allocation is proposed to prevent co-channel interference and hidden terminal interference.Simulation results show that the proposed protocol outperforms some existing protocols in cluster stability,delivery ratio,throughput and delay performance.

Best Effort Spectrum Allocation Scheme for Femtocell Networks in Dense Deployment

In orthogonal frequency division multiple access(OFDMA) based femtocell networks,the co-tier interference among femto base stations(FBS) becomes important in multiuser and densely deployed environment.In order to mitigate the co-tier interference and enhance the system total throughput,this paper proposed a best effort spectrum allocation scheme based on the extension of graph theory.In the scheme,a controller was proposed to collect the channel state information(CSI)of all femtocell user equipments(FUEs) in a certain range.Then,the controller evaluated the signal-to-interference Ratio(SIR) of each FUE and determined the set of its interference neighbors.By calculating the received power matrix(RPM) among FUEs and building interference graph matrix(IGM),different spectrum resource blocks(RBs) were assigned to the users with interference relation,while users without interference relation shared the same RBs,which could increase the spectrum efficiency.Simulation results show that the proposed algorithm can significantly improve the RB usage efficiency compared with the basic graph coloring theory,and more than 80% improvement can be acquired in dense deployment scenario.Besides,the throughput of both cell edge macro user equipments(MUEs) and cell edge FUEs is guaranteed on the premise of low interference.

CHRT:Clustering-Based Hybrid Re-Routing System for Traffic Congestion Avoidance

Re-routing system has become an important technology to improve traffic efficiency.The traditional re-routing schemes do not consider the dynamic characteristics of urban traffic,making the planned routes unable to cope with the changing traf-fic conditions.Based on real-time traffic information,it is challenging to dynamically re-route connected vehicles to alleviate traffic congestion.Moreover,how to obtain global traffic information while reducing communication costs and improving travel efficiency poses a challenge to the re-routing system.To deal with these challenges,this paper proposes CHRT,a clustering-based hybrid re-routing system for traffic congestion avoidance.CHRT develops a multi-layer hybrid architecture.The central server accesses the global view of traffic,and the distributed part is composed of vehicles divided into clusters to reduce latency and communication overhead.Then,a clustering-based priority mechanism is proposed,which sets priorities for clusters based on realtime traffic information to avoid secondary congestion.Furthermore,to plan the optimal routes for vehicles while alleviating global traffic congestion,this paper presents a multi-metric re-routing algorithm.Through extensive simulations based on the SUMO traffic simulator,CHRT reduces vehicle traveling time,fuel consumption,and CO2 emissions compared to other systems.In addition,CHRT globally alleviates traffic congestion and improves traffic efficiency.

Probabilistic Top-k Query:Model and Application on Web Traffic Analysis

Top-k ranking of websites according to traffic volume is important for Internet Service Providers(ISPs) to understand network status and optimize network resources. However, the ranking result always has a big deviation with actual rank for the existence of unknown web traffic, which cannot be identified accurately under current techniques. In this paper, we introduce a novel method to approximate the actual rank. This method associates unknown web traffic with websites according to statistical probabilities. Then, we construct a probabilistic top-k query model to rank websites. We conduct several experiments by using real HTTP traffic traces collected from a commercial ISP covering an entire city in northern China. Experimental results show that the proposed techniques can reduce the deviation existing between the ground truth and the ranking results vastly. In addition, we find that the websites providing video service have higher ratio of unknown IP as well as higher ratio of unknown traffic than the websites providing text web page service. Specifically, we find that the top-3 video websites have more than 90% of unknown web traffic. All these findings are helpful for ISPs understanding network status and deploying Content Distributed Network(CDN).

SWN: An SDN Based Framework for Carrier Grade WiFi Networks

With the rapid growth of mobile data traffic and vast traffic offloaded from cellular network, Wi-Fi has been considered as an essential component to cope with the tremendous growth of mobile data traffic. Although operators have deployed a lot of carrier grade Wi-Fi networks, but there are still a multitude of arrears for nowadays Wi-Fi networks, such as supporting seamless handover between APs, automatic network access and unified authentication, etc. In this paper, we propose an SDN based carrier grade Wi-Fi network framework, namely SWN. The key conceptual contribution of SWN is a principled refactoring of Wi-Fi networks into control and data planes. The control plane has a centralized global view of the whole network, can perceive the underlying network state by network situation awareness(NAS) technique, and bundles the perceived information and network management operations into northbound Application Programming Interface(API) for upper applications. In the data plane, we construct software access point(SAP) to abstract the connection between user equipment(UE) and access point(AP). Network operators can design network applications by utilizing these APIs and the SAP abstraction to configure and manage the whole network, which makes carrier grade Wi-Fi networks more flexible, user-friendly, and scalable.