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25 August 2019, Volume 40 Issue 8

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Topics:Novel Network Architecture

Research on the full-dimensional defined polymorphic smart network

Yuxiang HU,Peng YI,Penghao SUN,Jiangxing WU

2019, 40(8):  1-12.  doi:10.11959/j.issn.1000-436x.2019192

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For current Internet is confronted with some defects such as rigid structure,single IP bearing,and disability in suppressing unknown threats,the functions,performance,efficiency and security of the Internet were promoted from the perspective of network structure,the “structure definition” was introduced through all levels of the Internet,and a network architecture of multi-modal presentation of network functions was provided in all levels:full-dimensional defined polymorphic smart network (PINet),which supported full-dimensional definition and multi-modal presentation of addressing and routing,switching mode,interconnection mode,network element,transmission protocol,service properties and so on.Then the vision and goal,architecture and model,as well as the key technologies of PINet were discussed.The proposed architecture can provide a possible solution to the development of new network technologies.

Next generation converged media network architecture

Wenjun ZHANG,Yunfeng GUAN,Dazhi HE,Zhiyong CHEN,Li SONG,Yiling XU,Bin XIA

2019, 40(8):  13-21.  doi:10.11959/j.issn.1000-436x.2019164

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In order to cope with the huge impact of the integration of media and network on the broadcast and mobile communication networks that carry media services,and the new requirements for network transmission efficiency,capacity expansion,resource utilization and service quality of new converged media services,a new generation media network system architecture for the development of converged media was proposed based on the summary of media development and network evolution in the past two decades.The new architecture can meet the application requirements of denser connectivity,higher reliability,lower latency and better interactivity.

Low-latency networking:architecture,key scenarios and research prospect

Xutong ZUO,Mowei WANG,Yong CUI

2019, 40(8):  22-35.  doi:10.11959/j.issn.1000-436x.2019175

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With the advent of delay-sensitive applications and ultra-low latency scenarios,research on low-latency networking is attracting attention from academia,industry,and standards organizations.Understanding the causes of latency and designing corresponding techniques to reduce latency enable the development of emerging applications.The sources of latency according to the layered architecture of the network was analyzed,and summarizes the techniques for reducing the latency.After that,three typical low-latency key scenarios and delay optimization techniques for data center network,5G and edge computing was analyzed.Finally,the opportunities and challenges that may be encountered in the development of low latency networks were presented from the perspectives of network architecture innovation,data-driven latency optimization algorithm and the design of new protocols.

Analysis and modeling of Internet backbone traffic with 5G/B5G

Yuan YANG,Mingwei XU,Hao CHEN

2019, 40(8):  36-44.  doi:10.11959/j.issn.1000-436x.2019182

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The effects that the 5th generation mobile network (5G) bring to Internet backbone were investigated qualitatively and quantitatively.First,the challenges that the characteristics of 5G,i.e.ultra-high data rate,ultra-low latency,and ultra-large number of connections,introduce to Internet backbone in terms of traffic,latency,and security were analyzed.Second,a model was proposed to capture the characteristics of 5G users and Internet traffic with the coordination of 5G,edge computing,and cloud computing.Then,numerical simulations were used to evaluate the model.The QoS requirements that Internet backbone faces under different extent of 5G deployment were evaluated.According to the study,increment of backbone traffic,increment of the ratio of propagation delay,and movement of bandwidth bottleneck are predicted after 5G/B5G deployment.

Papers

Beamforming for heterogeneous cloud radio access network

Jiakuo ZUO,Longxiang YANG,Nan BAO

2019, 40(8):  45-53.  doi:10.11959/j.issn.1000-436x.2019170

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To overcome the problem that previous researches for heterogeneous cloud radio access network (H-CRAN) mainly focus on single macro cell,and only considered the intracell interference in the one macro cell,while the inter cell interferences among different macro cells are neglected,H-CRAN with multiple macro-cells was studied,and the objective was to maximize system sum-rate through jointly optimizing the beamforming vectors of macro base stations (MBS) and remote radio heads (RRH).Based on alternating optimization and Lagrangian dual method,a joint MBS and RRH beamforming algorithm was proposed.The original problem was first divided into two subproblems.Then,the two sub-problem were solved alternately to obtain the final solutions of the original problem.In addition,the closed expression solutions of the two sub-problem were derived based on Lagrangian dual method.The proposed algorithm was compared with some beamforming algorithms in the simulation.The experimental results demonstrate the proposed algorithm has a better performance in improving the sum-rate of H-CRAN.

MicroNF:a microservice-based hybrid framework for NFV

Chen SUN,Jun BI,Zhilong ZHENG,Shuhe WANG,Hongxin HU

2019, 40(8):  54-59.  doi:10.11959/j.issn.1000-436x.2019131

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Network function virtualization (NFV) brought significant flexibility.However,such flexibility came with considerable compromises,since virtual machine carried monolithic functions could introduce significant performance overhead.A novel high-performance and programmable framework called MicroNF was proposed,which combines programmable hardware infrastructure and traditional software infrastructure in NFV to achieve both high performance and flexibility.In particular,microservice,a new design approach in software architecture,was leveraged by MicroNF to re-architect NFV to enable functional reusability among services and improve performance.MicroNF was implemented in a test bed based on OpenStack and ONetCard.Experimental results show that MicroNF reduces the forwarding latency of a service chain by an average of 70% compared with DPDK-based software implementation.

Function encoding based approach for App clone detection in cloud environment

Jia YANG,Cai FU,Lansheng HAN,Hongwei LU,Jingliang LIU

2019, 40(8):  60-71.  doi:10.11959/j.issn.1000-436x.2019106

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An efficient function-based encoding scheme in the cloud environment for detecting the cloned Apps was designed,called Pentagon.Firstly,a basic block feature extraction method was proposed.Secondly,a monotonic encoding algorithm for the App function was designed,which encoded the function based on the control flow graph structure and basic block attributes.Finally,a three-party libraries filtering method was proposed by using an efficient clustering algorithm based on the function feature.Experiments verified the effectiveness of the proposed scheme.The average search time is close to 79 ms,and the clone detection accuracy achieves 97.6%.

Radar marine maneuvering target detection via high resolution sparse fractional ambiguity function

Xiaohan YU,Xiaolong CHEN,Jian GUAN,Yong HUANG

2019, 40(8):  72-84.  doi:10.11959/j.issn.1000-436x.2019156

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To solve the problem of fast detection of high maneuvering targets in complex ocean background,a radar target detection algorithm via sparse fractional ambiguity function (SFRAF) was proposed.The sparse Fourier transform (SFT) was introduced after instantaneous autocorrelation function calculation,which combines the advantages of SFT and fractional ambiguity function (FRAF).Therefore,SFRAF has good processing performance on high maneuvering signals and can achieve lower computational complexity.The simulation experiments and the measured data processing results show that compared with classical detection method,the detection performance of maneuvering target can be significantly improved,and compared with FRAF,the proposed method can achieve higher detection efficiency.

Flow-network based auto rescale strategy for Flink

Ziyang LI,Jiong YU,Chen BIAN,Yitian ZHANG,Yonglin PU,Yuefei WANG,Liang LU

2019, 40(8):  85-101.  doi:10.11959/j.issn.1000-436x.2019173

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In order to solve the problem that the load of big data stream computing platform is increasing with fluctuation while the cluster was not able to rescale efficiently,the Flow-network based auto rescale strategy for Flink was proposed.Firstly,the flow-network model was set up and the capacity of each edge that was calculated by self-learning algorithm.Secondly,the bottleneck of the cluster was acquired by maximum-flow algorithm and the resource rescheduling plan was drawn up.Finally,the resource rescheduling plan was executed and the stateful data was migrated efficiently by the data migration algorithm based on the strategy of data partitioning by bulk and bucket.The experimental results show that the strategy can effectively provide performance promotion in the application with complex stateful data.It improved the throughput of the cluster and reduced the time overhead of the data migration on the premise of satisfying the latency constrain of the application,which means that the strategy promotes the scalability of the cluster efficiently.

Improved SMC cardinality-balanced multi-Bernoulli forwardbackward smoothing track-before-detect algorithm

Jiazheng PEI,Yong HUANG,Yunlong DONG,Xiaolong CHEN

2019, 40(8):  102-113.  doi:10.11959/j.issn.1000-436x.2019098

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For the tracking problem of multiple maneuvering targets in radar observation,the sequential Monte-Carlo cardinality-balanced multi-Bernoulli track-before-detect (SMC-CBMeMBer-TBD) algorithm is inaccurate in the estimation of the number of targets and the precision of state estimation.An improved algorithm based on SMC-CBMeMBer forward backward smoothing track-before-detect algorithm was proposed.In the algorithm,the multi target particle swarm optimization (MOPSO) was added between the process of prediction and update,and the fitness function was set up based on the observation value to make the particle set move to the position of the larger posterior probability density distribution,and solve the particle poverty in the heavy sampling process.In the update step,the algorithm was used.Then the smoothing recursive method was added,and the arithmetic operation time was prolonged,but the number and the state estimation precision were improved.The simulation results show that compared with the CBMeMBer-TBD method,the proposed algorithm improves the accuracy of the estimation of the number of maneuvering targets and the accuracy of the target state estimation.

Method of network slicing deployment based on performance-aware

Kaizhi HUANG,Qirun PAN,Quan YUAN,Wei YOU,Hongbo TANG

2019, 40(8):  114-122.  doi:10.11959/j.issn.1000-436x.2019169

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In order to deal with the performance degradation caused by resource contention due to the sharing of physical resources between VNF in the network slicing,a network slicing deployment method based on performance-awareness was proposed.When deploying network slice instances,first two-phase deployment that mapping virtual nodes was adopted,and then virtual links were mapped.In the virtual nodes mapping phase,the VNF performance influences factor was defined from the perspective of resource supply and demand to quantify the degree of VNF performance impact.Then the sum of the performance influence factors of all VNF on the deployable physical server was found in the network slice instance,the physical server with the smallest sum of performance influence factors was used as the mapping location,and the simulated annealing-discrete particle swarm algorithm was used to find the nodes mapping result.In the virtual links mapping phase,the shortest path algorithm was used to obtain the link mapping result.The simulation results show that the proposed method reduces the impact of other network slices on its own service performance.

Proactive migration model of SWIM service based on situation awareness

Zhijun WU,Shengyan ZHOU,Jin LEI

2019, 40(8):  123-132.  doi:10.11959/j.issn.1000-436x.2019171

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To solve the problem that in the system wide information management (SWIM) network,the SWIM service provider suffers from SWIM service interruption,service delay increase or service quality degradation due to malicious attack or self-failure.Therefore,a proactive migration model of SWIM service was proposed on the basis of situation awareness,which used the random forest algorithm to timely judge the SWIM service provider security situation.SWIM service authority was actively migrated according to security situation,and the emergencies impact on SWIM services were reduces.Experimental results show that the proposed model can guarantee services continuity in an emergency event,which has higher reliability and stability than SWIM network in which the service migration model is not deployed.

Anomaly detection model based on multi-grained cascade isolation forest algorithm

Xiaohui YANG,Shengchang ZHANG

2019, 40(8):  133-142.  doi:10.11959/j.issn.1000-436x.2019132

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The isolation-based anomaly detector,isolation forest has two weaknesses,its inability to detect anomalies that were masked by axis-parallel clusters,and anomalies in high-dimensional data.An isolation mechanism based on random hyperplane and a multi-grained scanning was proposed to overcome these weaknesses.The random hyperplane generated by a linear combination of multiple dimensions was used to simplify the isolation boundary of the data model which was a random linear classifier that can detect more complex data patterns,so that the isolation mechanism was more consistent with data distribution characteristics.The multi-grained scanning was used to perform dimensional sub-sampling which trained multiple forests to generate a hierarchical ensemble anomaly detection model.Experiments show that the improved isolation forest has better robustness to different data patterns and improves the efficiency of anomaly points in high-dimensional data.

Cloud resource prediction model based on triple exponential smoothing method and temporal convolutional network

Xiaolan XIE,Zhengzheng ZHANG,Jianwei WANG,Xiaochun GHENG

2019, 40(8):  143-150.  doi:10.11959/j.issn.1000-436x.2019172

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The container cloud represented by Docker and Kubernetes has the advantages of less additional resource overhead and shorter start-up and destruction time.However there are still resource management issues such as over-supply and under-supply.In order to allow the Kubernetes cluster to respond “in advance” to the resource usage of the applications deployed on it,and then to schedule and allocate resources in a timely,accurate and dynamic manner based on the predicted value,a cloud resource prediction model based on triple exponential smoothing method and temporal convolutional network was proposed,based on historical data to predict future demand for resources.To find the optimal combination of parameters,the parameters were optimized using TPOT thought.Experiments on the CPU and memory of the Google dataset show that the model has better prediction performance than other models.

2-adic complexity of SLCE sequence

Yan WANG,Shunbo LI,Gaina XUE

2019, 40(8):  151-156.  doi:10.11959/j.issn.1000-436x.2019143

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Aiming at the 2-adic complexity of Sidelnikov-Lempel-Cohn-Eastman sequences,autocorrelation function value of this kind of sequence was obtained by using the cyclotomic number.Based on the relationship between 2-adic complexity and autocorrelation function,properties of 2-adic complexity value were analyzed.According to the greatest common divisor between the autocorrelation function value and the period of SLCE sequence,the condition that the 2-adic complexity of a SLCE sequence reaches its maximum value was given.The results show that 2-adic complexity of SLCE sequence on many finite field can reach the maximum value.

Comprehensive Review

Survey of mobility prediction in wireless network

Ying WANG,Zhuang SU

2019, 40(8):  157-168.  doi:10.11959/j.issn.1000-436x.2019159

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Smart cities based on cloud computing,big data and artificial intelligence have become important development trends.Mobile prediction is the key technology of smart city.Firstly,in order to summarize the mobile prediction methods and its applications in wireless network,the importance and feasibility of mobile prediction were stated.And the datasets of mobile prediction were introduced.Secondly,the users’ trajectory characteristics and the method of mobile prediction were summarized and compared.Finally,the problems and challenges for mobility prediction were pointed out.

Correspondences

Decentralized credit system based on blockchain and its application

Mingsheng WANG,Heyang CAO,Peiyao LI

2019, 40(8):  169-177.  doi:10.11959/j.issn.1000-436x.2019126

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A decentralized credit system was proposed and its construction application was given.Decentralized credit system could be constructed by expanding the transaction structure of “digital currency commodity” based on block chain,expanding the function of consensus protocol and adding specific accounts.Also,the decentralized credit system was applied to the PKI enhancement and supervision system IKP so that IKP system did not need global fund in operation.Moreover,by introducing the hierarchical “virtual currency commodity” supply adjustment mechanism in the decentralized credit system to alleviate the difficulty of regulating the “virtual currency commodity” supply of “digital currency commodity” based on block chain,the precise “virtual currency commodity” supply regulation can be achieved.

Low-latency neighbor discovery algorithm based on multi-beacon message in mobile low-duty-cycle sensor network

Junbin LIANG,Xiang ZHOU,Fangqiang MA,Chan JIANG,Zongjian HE

2019, 40(8):  178-188.  doi:10.11959/j.issn.1000-436x.2019139

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Neighbor discovery enables nodes in the networks to discover each other through simple information interaction,which was suitable for the new mobile low duty cycle sensor network (MLDC-WSN).However,because the nodes in MLDC-WSN can move randomly and sleep,the network topology was changed frequently,which results in that some nodes need a lot of energy and time to find their neighbors.How to realize fast neighbor discovery for all nodes in the network was a difficult problem in current research.To solve this problem,a new low-latency neighbor discovery algorithm based on multi-beacon messages was proposed.In this algorithm,the nodes were discovered by sending a short beacon message through their neighbor nodes,and by adjusting the time and frequency of beacon message sent,a lower neighbor discovery delay was obtained.Eventually,through quantitative analysis and simulation experiments,it is found that compared with existing algorithms,this algorithm can find all neighbor nodes in MLDC-WSN with less energy consumption,lower latency and greater probability.

Contact plan design based on bi-directional particle swarm optimization in satellite network

Cuiqin DAI,Huang TANG,Linfeng GUO

2019, 40(8):  189-199.  doi:10.11959/j.issn.1000-436x.2019180

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Aiming at the problems of time-varying topology,intermittent connection,and constrained resource in satellite network,a contact plan design (CPD) scheme based on bi-directional particle optimization (BPSO) algorithm was proposed.Firstly,the task-based time-expanded graph (TEG) model was constructed through the analysis of resource-constrained time-varying satellite network.Next,available contact plan (CP) in satellite network were generated through initialization,coding and repairing by considering the discreteness of network topology and the limitation of node resources.Then,an evaluation function was designed according to the characteristics of the execution task to distinguish whether the generated available CP was good or bad.Finally,the bits to be corrected in the worst location were determined according to the sparse characteristics of links in CP,and the CP was continuously modified by the proposed BPSO algorithm to optimize the performance of spatial data transmission.The simulation results show that the proposed BPSO-based CPD scheme can effectively reduce the task delivery time and increase the task arrival rate.

Automatic modulation recognition algorithm for MQAM signal

Huadi ZHANG,Huaxun LOU

2019, 40(8):  200-211.  doi:10.11959/j.issn.1000-436x.2019168

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An automatic modulation recognition algorithm for MQAM signal was proposed.Firstly,the feature parameter F based on the fourth order cumulants was constructed to classify the square QAM and the cross QAM.Secondly,the compactness of zero center normalized instantaneous amplitude was calculated to identify the 16QAM from the square QAM.Thirdly,the baud rate was estimated by frequency spectrum of amplitude square,and timing was synchronized to delete the ISI and resume the relatively ideal constellations.And aiming at the 32QAM and the 128QAM,two different clustering radii were set,and clustering point density was got respectively by the subtractive clustering algorithm,and then the 32QAM and the 128QAM was classified depending on the difference of density value.In the same way,the 64QAM and the 256QAM were classified.The proposed algorithm can recognize five kinds of QAM signals，including 16QAM signals,32QAM signals,64QAM signals,128QAM signal and 256QAM signal without prior knowledge of frequency and baud rate.Furthermore,the proposed algorithm does not need complex iterative process,which can be applied in practical signal recognition.

Resource allocation strategy based on optimal matching auction in the enterprise network

Xin CONG,Lingling ZI,Xueli SHEN

2019, 40(8):  212-222.  doi:10.11959/j.issn.1000-436x.2019186

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To address the issue that the owners of computer are selfish in the enterprise networks,which caused the low available number of resource nodes and low efficiency of resource allocation,an optimized matching resource allocation strategy OMRA was proposed and its core was the auction mechanism.Selfishness was restrained and the number of available resources was increased by OMRA,so as the operating efficiency of the whole auction market was improved.First,the initial prices were determined by normalizing the costs of different type of resources on the beginning of auction.Secondly,an optimal matching auction algorithm was designed to maximize the interests of the auction markets.Then,service perfecting algorithm was performed such that the sellers could get more services at the current transaction value,thus ensuring the benefits of resource providers.At last,a request price updating algorithm was adopted to assurance that both sellers and buyers could get priorities in the next auction processing.Compared with the cloud resource allocating algorithm via fitness-enabled auction (CRAA/FA),the experiment results indicate that the efficiency of resource allocation improves by 10% and the benefits of market increase by 11.4%.