Performance Evaluation of Topologies for Multi-Domain Software-Defined Networking

Software-defined networking(SDN)is widely used in multiple types of data center networks,and these distributed data center networks can be integrated into a multi-domain SDN by utilizing multiple controllers.However,the network topology of each control domain of SDN will affect the performance of the multidomain network,so performance evaluation is required before the deployment of the multi-domain SDN.Besides,there is a high cost to build real multi-domain SDN networks with different topologies,so it is necessary to use simulation testing methods to evaluate the topological performance of the multi-domain SDN network.As there is a lack of existing methods to construct a multi-domain SDN simulation network for the tool to evaluate the topological performance automatically,this paper proposes an automated multi-domain SDN topology performance evaluation framework,which supports multiple types of SDN network topologies in cooperating to construct a multi-domain SDN network.The framework integrates existing single-domain SDN simulation tools with network performance testing tools to realize automated performance evaluation of multidomain SDN network topologies.We designed and implemented a Mininet-based simulation tool that can connect multiple controllers and run user-specified topologies in multiple SDN control domains to build and test multi-domain SDN networks faster.Then,we used the tool to perform performance tests on various data center network topologies in single-domain and multi-domain SDN simulation environments.Test results show that Space Shuffle has the most stable performance in a single-domain environment,and Fat-tree has the best performance in a multi-domain environment.Also,this tool has the characteristics of simplicity and stability,which can meet the needs of multi-domain SDN topology performance evaluation.

From 0D to 3D:Hierarchical structured high-performance free-standing silicon anodes based on binder-induced topological network architecture

Free-standing silicon anodes with high proportion of active materials have aroused great attention;however,the mechanical stability and electrochemical performance are severely suppressed.Herein,to resolve the appeal issues,a free-standing anode with a"corrugated paper"shape on micro-scale and a topological crosslinking network on the submicron and nano-scale is designed.Essentially,an integrated three-dimensional electrode structure is constructed based on robust carbon nanotubes network with firmly anchored SiNPs via forming interlocking junctions.In which,the hierarchical interlocking structure is achieved by directional induction of the binder,which ensures well integration during cycling so that significantly enhances mechanical stability as well as electronic and ionic conductivity of electrodes.Benefiting from it,this anode exhibits outsta nding performance under harsh service conditions including high Si loading,ultrahigh areal capacity(33.2 mA h cm^(-2)),and high/low temperatures(-15-60℃),which significantly extends its practical prospect.Furthermore,the optimization mechanism of this electrode is explored to verify the crack-healing and structure-integration maintaining along cycling via a unique self-stabilization process.Thus,from both the fundamental and engineering views,this strategy offers a promising path to produce high-performance free-standing electrodes for flexible device applications especially facing volume effect challenges.

Power-efficient topologies for wireless sensor networks with fixed communication range

The expectations for sensor networks are growing. The performance of wireless sensor networks （WSNs） is greatly influenced by their network topology. In this paper, we consider four patterned topologies that best support connectivity among these deployed sensor nodes in two-tiered WSNs. The theoretical and simulation results show that the triangle-based topology has smaller cell number, shorter maximum hop length, less total energy consumption, and better performance than other topologies. The analysis carried out in this paper could provide the guidelines for network deployment and protocol design in the future applications.

Consensus for Heterogeneous Multi-Agent Systems with Directed Network Topologies

In this paper, the consensus problem for heterogeneous multi-agent systems composed of first-order and second-order agents is investigated with directed network topologies. Based on a system transformation method, this consensus problem is turned into a consensus problem for homogeneous multi-agent systems. With certain assumption on the control parameters, firstly, necessary and sufficient condition for consensus is proposed with fixed topology. Secondly, sufficient condition is proposed for heterogeneous multi-agent systems to achieve consensus with switching topologies. Finally, simulation examples are presented to verify the effectiveness of the theoretical results.

Distributed Containment Control of Networked Nonlinear Second-order Systems With Unknown Parameters

In this paper, we study the containment control problem for nonlinear second-order systems with unknown parameters and multiple stationary/dynamic leaders. The topologies that characterize the interaction among the leaders and the followers are directed graphs. Necessary and sufficient criteria which guarantee the control objectives are established for both stationary leaders(regulation case) and dynamic leaders(dynamic tracking case) based protocols. The final states of all the followers are exclusively determined by the initial values of the leaders and the topology structures. In the regulation case, all the followers converge into the convex hull spanned by the leaders,while in the dynamic tracking case, not only the positions of the followers converge into the convex hull but also the velocities of the followers converge into the velocity convex hull of the leaders.Finally, all the theoretical results are illustrated by numerical simulations.

Distributed Fault-Tolerant Containment Control for Nonlinear Multi-Agent Systems Under Directed Network Topology via Hierarchical Approach

This paper investigates the distributed fault-tolerant containment control(FTCC)problem of nonlinear multi-agent systems(MASs)under a directed network topology.The proposed control framework which is independent on the global information about the communication topology consists of two layers.Different from most existing distributed fault-tolerant control(FTC)protocols where the fault in one agent may propagate over network,the developed control method can eliminate the phenomenon of fault propagation.Based on the hierarchical control strategy,the FTCC problem with a directed graph can be simplified to the distributed containment control of the upper layer and the fault-tolerant tracking control of the lower layer.Finally,simulation results are given to demonstrate the effectiveness of the proposed control protocol.

Multi-constraint quality of service routing algorithm for dynamic topology networks

An adaptive multi-QoS routing algorithm called AMQRA is proposed for dynamic topology networks, such as satellite networks and Ad-hoc networks. The AMQRA is a distributed and mobile-agents-based routing algorithm, which combines ant quantity system （AQS） with ant colony optimization （ACO） that is used in AntNet routing algorithm. In dynamic topology networks, the AMQRA achieves timely optimization for concave metric QoS constraint and fast convergence. The proposed routing algorithm is simulated in Iridium satellite constellation on OPNET. The results show that AMQRA not only outperforms the AntNet in convergence rate in dynamic topology networks but also can optimize concave metric QoS constraint and reasonably allot bandwidth to the load to avoid networks congestion.

Network dynamics and its relationships to topology and coupling structure in excitable complex networks

All dynamic complex networks have two important aspects, pattern dynamics and network topology. Discovering different types of pattern dynamics and exploring how these dynamics depend or/network topologies are tasks of both great theoretical importance and broad practical significance. In this paper we study the oscillatory behaviors of excitable complex networks （ECNs） and find some interesting dynamic behaviors of ECNs in oscillatory probability, the multiplicity of oscillatory attractors, period distribution, and different types of oscillatory patterns （e.g., periodic, quasiperiodic, and chaotic）. In these aspects, we further explore strikingly sharp differences among network dynamics induced by different topologies （random or scale-free topologies） and different interaction structures （symmetric or asymmetric couplings）. The mechanisms behind these differences are explained physically.

Data Center Network Architecture

1 Introduction The history of data centers can be traced back to the 1960s. Early data centers were deployed on main- frames that were time-shared by users via remote terminals. The boom in data centers came duringthe internet era. Many companies started building large inter- net-connected facililies,

Bitcoin Network Measurement and a New Approach to Infer the Topology

Bitcoin has made an increasing impact on the world's economy and financial order,which attracted extensive attention of researchers and regulators from all over the world.Most previous studies had focused more on the transaction layer,but less on the network layer.In this paper,we developed BNS(Bitcoin Network Sniffer),which could find and connect nodes in the Bitcoin network,and made a measurement in detail.We collected nearly 4.1 million nodes in 1.5 hours and identified 9,515 reachable nodes.We counted the reachable nodes'properties such as:service type,port number,client version and geographic distribution.In addition,we analyzed the stability of the reachable nodes in depth and found nearly 60%kept stable during 15 days.Finally,we proposed a new approach to infer the Bitcoin network topology by analyzing the Neighbor Addresses of Adjacent Nodes and their timestamps,which had an accuracy over 80%.

An improved de-interleaving algorithm of radar pulses based on SOFM with self-adaptive network topology

As a core part of the electronic warfare(EW) system,de-interleaving is used to separate interleaved radar signals. As interleaved radar pulses become more complex and denser, intelligent classification of radar signals has become very important. The self-organizing feature map(SOFM) is an excellent artificial neural network, which has huge advantages in intelligent classification of complex data. However, the de-interleaving process based on SOFM is faced with the problems that the initialization of the map size relies on prior information and the network topology cannot be adaptively adjusted. In this paper, an SOFM with self-adaptive network topology(SANT-SOFM) algorithm is proposed to solve the above problems. The SANT-SOFM algorithm first proposes an adaptive proliferation algorithm to adjust the map size, so that the initialization of the map size is no longer dependent on prior information but is gradually adjusted with the input data. Then,structural optimization algorithms are proposed to gradually optimize the topology of the SOFM network in the iterative process,constructing an optimal SANT. Finally, the optimized SOFM network is used for de-interleaving radar signals. Simulation results show that SANT-SOFM could get excellent performance in complex EW environments and the probability of getting the optimal map size is over 95% in the absence of priori information.

Subdivision surface modeling system based on arbitrary topological curves network and combined subdivision

Arbitrary topological curve network has no restriction in topology structure,so it has more powerful representing ability in defining complex surfaces.A complex surface modeling system is presented based on arbitrary topological curve network and the improved combined subdivision method,its functions including creating and editing curve network,and generating and modifying curve network＇s interpolated surface.This modeling system can be used to the process of products＇concept design,and its applications is also significant to the development of subdivision method.

Network topology optimization of supply chain information systems

Network topology optimization has been widely researched. Since market competition has gradually developed into competition among the supply chain information systems, the network to- pology optimization of supply chain information systems has been in urgent need. However, the net- work topology optimization of supply chain information systems is still in its early stages and still has some challenges. So a description of typical seven network topologies for various supply chain infor- mation systems has been given. The generic characteristics of each network topology can be summa- rized. To analyze the optimization of network topology optimization of supply chain information sys- tems, a numeric model has been established based on these general characteristics. A genetic algo- rithm is applied in the network topology optimization of supply chain information systems model to a- chieve the minimum cost and shortest path. Finally, our experiment results are provided to demon- strate the robustness and effectiveness of the proposed model.

INTS-MFS:A novel method to predict microRNA-disease associations by integrating network topology similarity and microRNA function similarity

Identifying associations between microRNAs(miRNAs)and diseases is very important to understand the occurrence and development of human diseases.However,these existing methods suffer from the following limitation:first,some disease-related miRNAs are obtained from the miRNA functional similarity networks consisting of heterogeneous data sources,i.e.,disease similarity,protein interaction network,gene expression.Second,little approaches infer disease-related miRNAs depending on the network topological features without the functional similarity of miRNAs.In this paper,we develop a novel model of Integrating Network Topology Similarity and MicroRNA Function Similarity(INTS-MFS).The integrated miRNA similarities are calculated based on miRNA functional similarity and network topological characteristics.INTS-MFS obtained AUC of 0.872 based on five-fold cross-validation and was applied to three common human diseases in case studies.As a results,30 out of top 30 predicted Prostatic Neoplasm-related miRNAs were included in the two databases of dbDEMC and PhenomiR2.0.29 out of top 30 predicted Lung Neoplasm-related miRNAs and Breast Neoplasm-related miRNAs were included in dbDEMC,PhenomiR2.0 and experimental reports.Moreover,INTS-MFS found unknown association with hsa-mir-371a in breast cancer and lung cancer,which have not been reported.It provides biologists new clues for diagnosing breast and lung cancer.

Network-topology-adaptive quantum conference protocols

As an important application of the quantum network communication, quantum multiparty conference has made multiparty secret communication possible. Previous quantum multiparty conference schemes based on quantum data encryption are insensitive to network topology. However, the topology of the quantum network significantly affects the communication efficiency, e.g., parallel transmission in a channel with limited bandwidth. We have proposed two distinctive protocols, which work in two basic network topologies with efficiency higher than the existing ones. We first present a protocol which works in the reticulate network using Greeberger-Horne-Zeilinger states and entanglement swapping. Another protocol, based on quantum multicasting with quantum data compression, which can improve the efficiency of the network, works in the star-like network. The security of our protocols is guaranteed by quantum key distribution and one-time-pa^t eucryption. In general, the two protocols can be applied to any quantum network where the topology can be equivalently transformed to one of the two structures we propose in our protocols.

Unicast Network Topology Inference Algorithm Based on Hierarchical Clustering

Network topology inference is one of the important applications of network tomography.Traditional network topology inference may impact network normal operation due to its generation of huge data traffic.A unicast network topology inference is proposed to use time to live(TTL)for layering and classify nodes layer by layer based on the similarity of node pairs.Finally,the method infers logical network topology effectively with self-adaptive combination of previous results.Simulation results show that the proposed method holds a high accuracy of topology inference while decreasing network measuring flow,thus improves measurement efficiency.

Design, Implementation and Verification of Topology Network Architecture of Smart Home Tree

Smart home technology provides consumers with network connectivity,automation or enhanced services for home devices.With the Internet of Things era,a vast data flow makes business platforms have to own the same computing power to match their business services.It achieves computing power through implementing big data algorithms deployed in the cloud data center.However,because of the far long geographical distance between the client and the data center or the massive data capacity gap,potentially high latency and high packet loss will reduce the usability of smart home systems if service providers deploy all services in the cloud data center.Edge computing and fog computing can significantly improve the utilization of network resources and reconstruct the network architecture for the user’s home.This article enables a fog resource-based resource allocation management technology.It provides a method that can more reasonably allocate network resources through a virtualized middle-tier method to ensure low response time and configure Quality of Service to ensure the use of delay-sensitive critical applications to improve the reliability of smart home communication system.Besides,the proposed method has is tested and verified by adjusting the variables of the network environment.We realize the optimization of resource allocation of client network without changing the hardware of client.

Day-ahead Voltage-stability-constrained Network Topology Optimization with Uncertainties

A day-ahead voltage-stability-constrained network topology optimization(DVNTO)problem is proposed to find the day-ahead topology schemes with the minimum number of operations(including line switching and bus-bar splitting)while ensuring the sufficient hourly voltage stability margin and the engineering operation requirement of power systems.The AC continuation power flow and the uncertainty from both renewable energy sources and loads are incorporated into the formulation.The proposed DVNTO problem is a stochastic,largescale,nonlinear integer programming problem.To solve it tractably,a tailored three-stage solution methodology,including a scenario generation and reduction stage,a dynamic period partition stage,and a topology identification stage,is presented.First,to address the challenges posed by uncertainties,a novel problem-specified scenario reduction process is proposed to obtain the representative scenarios.Then,to obtain the minimum number of necessary operations to alter the network topologies for the next 24-hour horizon,a dynamic period partition strategy is presented to partition the hours into several periods according to the hourly voltage information based on the voltage stability problem.Finally,a topology identification stage is performed to identify the final network topology scheme.The effectiveness and robustness of the proposed three-stage solution methodology under different loading conditions and the effectiveness of the proposed partition strategy are evaluated on the IEEE 118-bus and 3120-bus power systems.

Theory and Experiments on Enclosing Control of Multi-Agent Systems

This paper proposes a control strategy called enclosing control.This strategy can be described as follows:the followers design their control inputs based on the state information of neighbor agents and move to specified positions.The convex hull formed by these followers contains the leaders.We use the single-integrator model to describe the dynamics of the agents and proposes a continuous-time control protocol and a sampled-data based protocol for multi-agent systems with stationary leaders with fixed network topology.Then the state differential equations are analyzed to obtain the parameter requirements for the system to achieve convergence.Moreover,the conditions achieving enclosing control are established for both protocols.A special enclosing control with no leader located on the convex hull boundary under the protocols is studied,which can effectively prevent enclosing control failures caused by errors in the system.Moreover,several simulations are proposed to validate theoretical results and compare the differences between the three control protocols.Finally,experimental results on the multi-robot platform are provided to verify the feasibility of the protocol in the physical system.

An optimization method for shale gas gathering system-Consideration of reliability enhancement under earthquake-related uncertainties

Shale gas gathering systems are of great importance in gas fields for the efficient and reliable transportation of gas.In traditional design methods,pipeline layouts are optimized only from an economic point of view,where reliability is evaluated after optimization.However,reliability can be enhanced by spare pipelines so that both economic and reliable aspects are evaluated simultaneously.Based on the idea of enhancing reliability,this research proposes a methodology for optimizing the pipeline layout problem including well clustering,stations site selecting,and piping.Different topology arrangements and spare pipelines are investigated to enhance the production efficiency and reliability of the pipeline network under earthquake-related uncertainties.Reliability evaluation is converted into an economic one so that the objective of this work is to minimize the total annual cost.To solve such a complex problem,genetic algorithm,K-means algorithm,Geo Steiner algorithm,Kruskal algorithm,linear programming and Monte Carlo simulations are combined.A real-world case study illustrates the effectiveness of the proposed methodology and shows a 36.53%reduction in the total annual cost compared with the initial scheme.