The emergence of beyond 5G networks has the potential for seamless and intelligent connectivity on a global scale.Network slicing is crucial in delivering services for different,demanding vertical applications in this...The emergence of beyond 5G networks has the potential for seamless and intelligent connectivity on a global scale.Network slicing is crucial in delivering services for different,demanding vertical applications in this context.Next-generation applications have time-sensitive requirements and depend on the most efficient routing path to ensure packets reach their intended destinations.However,the existing IP(Internet Protocol)over a multi-domain network faces challenges in enforcing network slicing due to minimal collaboration and information sharing among network operators.Conventional inter-domain routing methods,like Border Gateway Protocol(BGP),cannot make routing decisions based on performance,which frequently results in traffic flowing across congested paths that are never optimal.To address these issues,we propose CoopAI-Route,a multi-agent cooperative deep reinforcement learning(DRL)system utilizing hierarchical software-defined networks(SDN).This framework enforces network slicing in multi-domain networks and cooperative communication with various administrators to find performance-based routes in intra-and inter-domain.CoopAI-Route employs the Distributed Global Topology(DGT)algorithm to define inter-domain Quality of Service(QoS)paths.CoopAI-Route uses a DRL agent with a message-passing multi-agent Twin-Delayed Deep Deterministic Policy Gradient method to ensure optimal end-to-end routes adapted to the specific requirements of network slicing applications.Our evaluation demonstrates CoopAI-Route’s commendable performance in scalability,link failure handling,and adaptability to evolving topologies compared to state-of-the-art methods.展开更多
The 6th generation mobile networks(6G)network is a kind of multi-network interconnection and multi-scenario coexistence network,where multiple network domains break the original fixed boundaries to form connections an...The 6th generation mobile networks(6G)network is a kind of multi-network interconnection and multi-scenario coexistence network,where multiple network domains break the original fixed boundaries to form connections and convergence.In this paper,with the optimization objective of maximizing network utility while ensuring flows performance-centric weighted fairness,this paper designs a reinforcement learning-based cloud-edge autonomous multi-domain data center network architecture that achieves single-domain autonomy and multi-domain collaboration.Due to the conflict between the utility of different flows,the bandwidth fairness allocation problem for various types of flows is formulated by considering different defined reward functions.Regarding the tradeoff between fairness and utility,this paper deals with the corresponding reward functions for the cases where the flows undergo abrupt changes and smooth changes in the flows.In addition,to accommodate the Quality of Service(QoS)requirements for multiple types of flows,this paper proposes a multi-domain autonomous routing algorithm called LSTM+MADDPG.Introducing a Long Short-Term Memory(LSTM)layer in the actor and critic networks,more information about temporal continuity is added,further enhancing the adaptive ability changes in the dynamic network environment.The LSTM+MADDPG algorithm is compared with the latest reinforcement learning algorithm by conducting experiments on real network topology and traffic traces,and the experimental results show that LSTM+MADDPG improves the delay convergence speed by 14.6%and delays the start moment of packet loss by 18.2%compared with other algorithms.展开更多
Reliable estimation of deformation and failure behaviors of fractured rock mass is important for practical engineering design.This study proposes a multi-domain equivalent method for fracture network to estimate the d...Reliable estimation of deformation and failure behaviors of fractured rock mass is important for practical engineering design.This study proposes a multi-domain equivalent method for fracture network to estimate the deformation properties of complex fractured rock mass.It comprehends both the advantages of the discrete fracture network model and the equivalent continuum model to capture the features of discontinuities explicitly while reducing computational intensity.The complex fracture network is stochastically split into a number of subfracture networks according to the domain,length or angle.An analytical solution is derived to infer theoretically the relationship between the elastic moduli of the original complex fractured rock mass and the split subfractured rock masses by introducing a correction term based on the deformation superposition principle.Numerical simulations are conducted to determine the elastic moduli of split subfractured rock masses using universal distinct element code(UDEC),while the elastic modulus of the original model is estimated based on the currently proposed analytical relationship.The results show that the estimation accuracy with the current domainbased splitting model is far superior compared to those with the other two splitting models.Thus,the estimation method of elastic modulus of complex fractured rock mass based on domain splitting mode of fracture network is identified as the multi-domain equivalent method proposed in this paper.The reliability of this method is evaluated,and its high computational efficiency is demonstrated through exemplification with regard to different geometric configurations for stochastically artificial discrete fracture network.The proposed multi-domain equivalent method constructs the theoretical framework except for the regression analysis hypothesis compared to the density-reduced model equivalent method.展开更多
Due to the many types of distributed denial-of-service attacks(DDoS)attacks and the large amount of data generated,it becomes a chal-lenge to manage and apply the malicious behavior knowledge generated by DDoS attacks...Due to the many types of distributed denial-of-service attacks(DDoS)attacks and the large amount of data generated,it becomes a chal-lenge to manage and apply the malicious behavior knowledge generated by DDoS attacks.We propose a malicious behavior knowledge base framework for DDoS attacks,which completes the construction and application of a multi-domain malicious behavior knowledge base.First,we collected mali-cious behavior traffic generated by five mainstream DDoS attacks.At the same time,we completed the knowledge collection mechanism through data pre-processing and dataset design.Then,we designed a malicious behavior category graph and malicious behavior structure graph for the characteristic information and spatial structure of DDoS attacks and completed the knowl-edge learning mechanism using a graph neural network model.To protect the data privacy of multiple multi-domain malicious behavior knowledge bases,we implement the knowledge-sharing mechanism based on federated learning.Finally,we store the constructed knowledge graphs,graph neural network model,and Federated model into the malicious behavior knowledge base to complete the knowledge management mechanism.The experimental results show that our proposed system architecture can effectively construct and apply the malicious behavior knowledge base,and the detection capability of multiple DDoS attacks occurring in the network reaches above 0.95,while there exists a certain anti-interference capability for data poisoning cases.展开更多
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,t...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.展开更多
It's promising to use Software-Defined Networking(SDN) and Network Functions Virtualization(NFV) to integrate satellite and terrestrial networks. To construct network service function chains in such a multi-domain...It's promising to use Software-Defined Networking(SDN) and Network Functions Virtualization(NFV) to integrate satellite and terrestrial networks. To construct network service function chains in such a multi-domain environment, we propose a horizontal-based Multi-domain Service Function Chaining(Md-SFC) orchestration framework. In this framework, multi-domain orchestrators can coordinate with each other to guarantee the end-to-end service quality. Intra-domain orchestrators also coordinate SDN controllers and NFV management components to implement intra-domain service function chains. Based on this, we further propose a heuristic SFC mapping algorithm with a cooperative inter-domain path calculation method to map service function chains to infrastructures. In this method, master multi-domain orchestrator and intra-domain orchestrators coordinate to select proper inter-domain links. We compare the cooperative method with a naive uncooperative way that domains' topology information is provided to the master multi-domain orchestrator and it calculates the shortest inter-domain path between intra-domain service function chains directly. Simulation results demonstrate that our solution is feasible. It is able to construct end-to-end performance guaranteed service function chain by horizontal-based cooperation. The cooperative inter-domain path calculation method decreasesthe mapping load for the master orchestrator and gets the same end-to-end performance.展开更多
Most researches focused on the analytical stabilized algorithm for the modular simulation of single domain, e.g., pure mechanical systems. Only little work has been performed on the problem of multi-domain simulation ...Most researches focused on the analytical stabilized algorithm for the modular simulation of single domain, e.g., pure mechanical systems. Only little work has been performed on the problem of multi-domain simulation stability influenced by algebraic loops. In this paper, the algebraic loop problem is studied by a composite simulation method to reveal the internal relationship between simulation stability and system topologies and simulation unit models. A stability criterion of multi-domain composite simulation is established, and two algebraic loop compensation algorithms are proposed using numerical iteration and approximate function in multi-domain simulation. The numerical stabilized algorithm is the Newton method for the solution of the set of nonlinear equations, and it is used here in simulation of the system composed of mechanical system and hydraulic system. The approximate stabilized algorithm is the construction of response surface for inputs and outputs of unknown unit model, and it is utilized here in simulation of the system composed of forging system, mechanical and hydraulic system. The effectiveness of the algorithms is verified by a case study of multi-domain simulation for forging system composed of thermoplastic deformation of workpieces, mechanical system and hydraulic system of a manipulator. The system dynamics simulation results show that curves of motion and force are continuous and convergent. This paper presents two algorithms, which are applied to virtual reality simulation of forging process in a simulation platform for a manipulator, and play a key role in simulation efficiency and stability.展开更多
The pursuit of the higher performance mobile communications forces the emergence of the fifth generation mobile communication(5G). 5G network, integrating wireless and wired domain, can be qualified for the complex vi...The pursuit of the higher performance mobile communications forces the emergence of the fifth generation mobile communication(5G). 5G network, integrating wireless and wired domain, can be qualified for the complex virtual network work oriented to the cross-domain requirement. In this paper, we focus on the multi-domain virtual network embedding in a heterogeneous 5G network infrastructure, which facilitates the resource sharing for diverse-function demands from fixed/mobile end users. We proposed the mathematical ILP model for this problem.And based on the layered-substrate-resource auxiliary graph and an effective six-quadrant service-type-judgment method, 5G embedding demands can be classified accurately to match different user access densities. A collection of novel heuristic algorithms of virtual 5G network embedding are proposed. A great deal of numerical simulation results testified that our algorithm performed better in terms of average blocking rate, routing latency and wireless/wired resource utilization, compared with the benchmark.展开更多
A multi-domain nonlinear dynamic model of a proportional solenoid valve was presented.The electro-magnetic,mechanical and fluid subsystems of the valve were investigated,including their interactions.Governing equation...A multi-domain nonlinear dynamic model of a proportional solenoid valve was presented.The electro-magnetic,mechanical and fluid subsystems of the valve were investigated,including their interactions.Governing equations of the valve were derived in the form of nonlinear state equations.By comparing the simulated and measured data,the simulation model is validated with a deviation less than 15%,which can be used for the structural design and control algorithm optimization of proportional solenoid valves.展开更多
This paper proposed a multi-domain virtual network embedding algorithm based on multi-controller SDN architecture. The local controller first selects candidate substrate nodes for each virtual node in the domain. Then...This paper proposed a multi-domain virtual network embedding algorithm based on multi-controller SDN architecture. The local controller first selects candidate substrate nodes for each virtual node in the domain. Then the global controller abstracts substrate network topology based on the candidate nodes and boundary nodes of each domain, and applies Particle Swarm Optimization Algorithm on it to divide virtual network requests. Each local controller then embeds the virtual nodes of the divided single-domain virtual network requests in the domain, and cooperates with other local controllers to embed the inter-domain virtual links. Simulation experimental results show that the proposed algorithm has good performance in reducing embedding cost with good stability and scalability.展开更多
文摘The emergence of beyond 5G networks has the potential for seamless and intelligent connectivity on a global scale.Network slicing is crucial in delivering services for different,demanding vertical applications in this context.Next-generation applications have time-sensitive requirements and depend on the most efficient routing path to ensure packets reach their intended destinations.However,the existing IP(Internet Protocol)over a multi-domain network faces challenges in enforcing network slicing due to minimal collaboration and information sharing among network operators.Conventional inter-domain routing methods,like Border Gateway Protocol(BGP),cannot make routing decisions based on performance,which frequently results in traffic flowing across congested paths that are never optimal.To address these issues,we propose CoopAI-Route,a multi-agent cooperative deep reinforcement learning(DRL)system utilizing hierarchical software-defined networks(SDN).This framework enforces network slicing in multi-domain networks and cooperative communication with various administrators to find performance-based routes in intra-and inter-domain.CoopAI-Route employs the Distributed Global Topology(DGT)algorithm to define inter-domain Quality of Service(QoS)paths.CoopAI-Route uses a DRL agent with a message-passing multi-agent Twin-Delayed Deep Deterministic Policy Gradient method to ensure optimal end-to-end routes adapted to the specific requirements of network slicing applications.Our evaluation demonstrates CoopAI-Route’s commendable performance in scalability,link failure handling,and adaptability to evolving topologies compared to state-of-the-art methods.
文摘The 6th generation mobile networks(6G)network is a kind of multi-network interconnection and multi-scenario coexistence network,where multiple network domains break the original fixed boundaries to form connections and convergence.In this paper,with the optimization objective of maximizing network utility while ensuring flows performance-centric weighted fairness,this paper designs a reinforcement learning-based cloud-edge autonomous multi-domain data center network architecture that achieves single-domain autonomy and multi-domain collaboration.Due to the conflict between the utility of different flows,the bandwidth fairness allocation problem for various types of flows is formulated by considering different defined reward functions.Regarding the tradeoff between fairness and utility,this paper deals with the corresponding reward functions for the cases where the flows undergo abrupt changes and smooth changes in the flows.In addition,to accommodate the Quality of Service(QoS)requirements for multiple types of flows,this paper proposes a multi-domain autonomous routing algorithm called LSTM+MADDPG.Introducing a Long Short-Term Memory(LSTM)layer in the actor and critic networks,more information about temporal continuity is added,further enhancing the adaptive ability changes in the dynamic network environment.The LSTM+MADDPG algorithm is compared with the latest reinforcement learning algorithm by conducting experiments on real network topology and traffic traces,and the experimental results show that LSTM+MADDPG improves the delay convergence speed by 14.6%and delays the start moment of packet loss by 18.2%compared with other algorithms.
基金financial support by the National Natural Science Foundation of China(Grant Nos.52008152,U1965204,52061160367,U2067203 and 52008153)Natural Science Foundation of Hebei Province of China(Grant No.E2021202087)Hebei Department of Human Resource(Grant No.E2020050015)。
文摘Reliable estimation of deformation and failure behaviors of fractured rock mass is important for practical engineering design.This study proposes a multi-domain equivalent method for fracture network to estimate the deformation properties of complex fractured rock mass.It comprehends both the advantages of the discrete fracture network model and the equivalent continuum model to capture the features of discontinuities explicitly while reducing computational intensity.The complex fracture network is stochastically split into a number of subfracture networks according to the domain,length or angle.An analytical solution is derived to infer theoretically the relationship between the elastic moduli of the original complex fractured rock mass and the split subfractured rock masses by introducing a correction term based on the deformation superposition principle.Numerical simulations are conducted to determine the elastic moduli of split subfractured rock masses using universal distinct element code(UDEC),while the elastic modulus of the original model is estimated based on the currently proposed analytical relationship.The results show that the estimation accuracy with the current domainbased splitting model is far superior compared to those with the other two splitting models.Thus,the estimation method of elastic modulus of complex fractured rock mass based on domain splitting mode of fracture network is identified as the multi-domain equivalent method proposed in this paper.The reliability of this method is evaluated,and its high computational efficiency is demonstrated through exemplification with regard to different geometric configurations for stochastically artificial discrete fracture network.The proposed multi-domain equivalent method constructs the theoretical framework except for the regression analysis hypothesis compared to the density-reduced model equivalent method.
基金supported by the NationalKeyR&DProgramof China underGrant No.2018YFA0701604.
文摘Due to the many types of distributed denial-of-service attacks(DDoS)attacks and the large amount of data generated,it becomes a chal-lenge to manage and apply the malicious behavior knowledge generated by DDoS attacks.We propose a malicious behavior knowledge base framework for DDoS attacks,which completes the construction and application of a multi-domain malicious behavior knowledge base.First,we collected mali-cious behavior traffic generated by five mainstream DDoS attacks.At the same time,we completed the knowledge collection mechanism through data pre-processing and dataset design.Then,we designed a malicious behavior category graph and malicious behavior structure graph for the characteristic information and spatial structure of DDoS attacks and completed the knowl-edge learning mechanism using a graph neural network model.To protect the data privacy of multiple multi-domain malicious behavior knowledge bases,we implement the knowledge-sharing mechanism based on federated learning.Finally,we store the constructed knowledge graphs,graph neural network model,and Federated model into the malicious behavior knowledge base to complete the knowledge management mechanism.The experimental results show that our proposed system architecture can effectively construct and apply the malicious behavior knowledge base,and the detection capability of multiple DDoS attacks occurring in the network reaches above 0.95,while there exists a certain anti-interference capability for data poisoning cases.
基金This work was supported by the Fundamental Research Funds for the Central Universities(2021RC239)the Postdoctoral Science Foundation of China(2021 M690338)+3 种基金the Hainan Provincial Natural Science Foundation of China(620RC562,2019RC096,620RC560)the Scientific Research Setup Fund of Hainan University(KYQD(ZR)1877)the Program of Hainan Association for Science and Technology Plans to Youth R&D Innovation(QCXM201910)the National Natural Science Foundation of China(61802092,62162021).
文摘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.
基金supported by National High Technology of China ("863 program") under Grant No. 2015AA015702NSAF under Grant No.U1530118+1 种基金NSFC under Grant No.61602030National Basic Research Program of China ("973 program")under Grant No. 2013CB329101
文摘It's promising to use Software-Defined Networking(SDN) and Network Functions Virtualization(NFV) to integrate satellite and terrestrial networks. To construct network service function chains in such a multi-domain environment, we propose a horizontal-based Multi-domain Service Function Chaining(Md-SFC) orchestration framework. In this framework, multi-domain orchestrators can coordinate with each other to guarantee the end-to-end service quality. Intra-domain orchestrators also coordinate SDN controllers and NFV management components to implement intra-domain service function chains. Based on this, we further propose a heuristic SFC mapping algorithm with a cooperative inter-domain path calculation method to map service function chains to infrastructures. In this method, master multi-domain orchestrator and intra-domain orchestrators coordinate to select proper inter-domain links. We compare the cooperative method with a naive uncooperative way that domains' topology information is provided to the master multi-domain orchestrator and it calculates the shortest inter-domain path between intra-domain service function chains directly. Simulation results demonstrate that our solution is feasible. It is able to construct end-to-end performance guaranteed service function chain by horizontal-based cooperation. The cooperative inter-domain path calculation method decreasesthe mapping load for the master orchestrator and gets the same end-to-end performance.
基金supported by National Natural Science Foundation of China(Grant Nos.51075259,51121063,51305256)National Basic Research Program of China(973 Program,Grant No.2006CB705400)
文摘Most researches focused on the analytical stabilized algorithm for the modular simulation of single domain, e.g., pure mechanical systems. Only little work has been performed on the problem of multi-domain simulation stability influenced by algebraic loops. In this paper, the algebraic loop problem is studied by a composite simulation method to reveal the internal relationship between simulation stability and system topologies and simulation unit models. A stability criterion of multi-domain composite simulation is established, and two algebraic loop compensation algorithms are proposed using numerical iteration and approximate function in multi-domain simulation. The numerical stabilized algorithm is the Newton method for the solution of the set of nonlinear equations, and it is used here in simulation of the system composed of mechanical system and hydraulic system. The approximate stabilized algorithm is the construction of response surface for inputs and outputs of unknown unit model, and it is utilized here in simulation of the system composed of forging system, mechanical and hydraulic system. The effectiveness of the algorithms is verified by a case study of multi-domain simulation for forging system composed of thermoplastic deformation of workpieces, mechanical system and hydraulic system of a manipulator. The system dynamics simulation results show that curves of motion and force are continuous and convergent. This paper presents two algorithms, which are applied to virtual reality simulation of forging process in a simulation platform for a manipulator, and play a key role in simulation efficiency and stability.
基金supported in part by Open Foundation of State Key Laboratory of Information Photonics and Optical Communications (Grant No. IPOC2014B009)Fundamental Research Funds for the Central Universities (Grant Nos. N130817002, N150401002)+1 种基金Foundation of the Education Department of Liaoning Province (Grant No. L2014089)National Natural Science Foundation of China (Grant Nos. 61302070, 61401082, 61471109, 61502075, 91438110)
文摘The pursuit of the higher performance mobile communications forces the emergence of the fifth generation mobile communication(5G). 5G network, integrating wireless and wired domain, can be qualified for the complex virtual network work oriented to the cross-domain requirement. In this paper, we focus on the multi-domain virtual network embedding in a heterogeneous 5G network infrastructure, which facilitates the resource sharing for diverse-function demands from fixed/mobile end users. We proposed the mathematical ILP model for this problem.And based on the layered-substrate-resource auxiliary graph and an effective six-quadrant service-type-judgment method, 5G embedding demands can be classified accurately to match different user access densities. A collection of novel heuristic algorithms of virtual 5G network embedding are proposed. A great deal of numerical simulation results testified that our algorithm performed better in terms of average blocking rate, routing latency and wireless/wired resource utilization, compared with the benchmark.
基金Project(2008ZHZX1A0502) supported by the Independence Innovation Achievements Transformation Crucial Special Program of Shandong Province,China
文摘A multi-domain nonlinear dynamic model of a proportional solenoid valve was presented.The electro-magnetic,mechanical and fluid subsystems of the valve were investigated,including their interactions.Governing equations of the valve were derived in the form of nonlinear state equations.By comparing the simulated and measured data,the simulation model is validated with a deviation less than 15%,which can be used for the structural design and control algorithm optimization of proportional solenoid valves.
基金supported by "the Fundamental Research Funds for the Central Universities" of China University of Petroleum (East China) (Grant No. 18CX02139A)the National Natural Science Foundation of China (Grant No. 61471056)
文摘This paper proposed a multi-domain virtual network embedding algorithm based on multi-controller SDN architecture. The local controller first selects candidate substrate nodes for each virtual node in the domain. Then the global controller abstracts substrate network topology based on the candidate nodes and boundary nodes of each domain, and applies Particle Swarm Optimization Algorithm on it to divide virtual network requests. Each local controller then embeds the virtual nodes of the divided single-domain virtual network requests in the domain, and cooperates with other local controllers to embed the inter-domain virtual links. Simulation experimental results show that the proposed algorithm has good performance in reducing embedding cost with good stability and scalability.
