Aqueous zinc-iodine batteries represent a compelling technology for largescale,sustainable energy storage,yet their practical application is severely hampered by the simultaneous interfacial challenges of uncontrolled...Aqueous zinc-iodine batteries represent a compelling technology for largescale,sustainable energy storage,yet their practical application is severely hampered by the simultaneous interfacial challenges of uncontrolled dendrite growth on the zinc anode and the parasitic polyiodide shuttle.Herein,we introduce a dual-site functional orchestration strategy by employing a single electrolyte additive,2-imidazolidone(ELA),to concurrently stabilize both the anode and cathode interfaces.On the anode side,the carbonyl(C=O)functional group of ELA initiates an effective anodic modulation,regulating the Zn2+solvation environment and facilitating a dynamic adsorption layer.This homogenizes the ion flux and guides preferential Zn deposition along the(002)plane,effectively suppressing dendrite formation.Concurrently,at the cathode,the imino(N-H)group immobilizes soluble polyiodide species via hydrogen bonding,realizing an effective cathodic mooring.This targeted confinement arrests the shuttle effect without impeding the intrinsic redox kinetics.This synergistic stabilization translates into exceptional electrochemical performance,with symmetric cells achieving an ultra-long lifespan of over 5500 h at a high current density of 8 m A cm-2 and the full Zn||I2 cells demonstrating robust cycling with 79.4%capacity retention after 2500 cycles.This work introduces a dual-site functional orchestration strategy,offering a pathway toward more durable aqueous batteries.展开更多
Software.defined networking(SDN) enables third.part companies to participate in the network function innovations. A number of instances for one network function will inevitably co.exist in the network. Although some o...Software.defined networking(SDN) enables third.part companies to participate in the network function innovations. A number of instances for one network function will inevitably co.exist in the network. Although some orchestration architecture has been proposed to chain network functions, rare works are focused on how to optimize this process. In this paper, we propose an optimized model for network function orchestration, function combination model(FCM). Our main contributions are as following. First, network functions are featured with a new abstraction, and are open to external providers. And FCM identifies network functions using unique type, and organizes their instances distributed over the network with the appropriate way. Second, with the specialized demands, we can combine function instances under the global network views, and formulate it into the problem of Boolean linear program(BLP). A simulated annealing algorithm is designed to approach optimal solution for this BLP. Finally, the numerical experiment demonstrates that our model can create outstanding composite schemas efficiently.展开更多
Network function virtualization is a new network concept that moves network functions from dedicated hardware to software-defined applications running on standard high volume severs. In order to accomplish network ser...Network function virtualization is a new network concept that moves network functions from dedicated hardware to software-defined applications running on standard high volume severs. In order to accomplish network services, traffic flows are usually processed by a list of network functions in sequence which is defined by service function chain. By incorporating network function virtualization in inter-data center(DC) network, we can use the network resources intelligently and deploy network services faster. However, orchestrating service function chains across multiple data centers will incur high deployment cost, including the inter-data center bandwidth cost, virtual network function cost and the intra-data center bandwidth cost. In this paper, we orchestrate SFCs across multiple data centers, with a goal to minimize the overall cost. An integer linear programming(ILP) model is formulated and we provide a meta-heuristic algorithm named GBAO which contains three modules to solve it. We implemented our algorithm in Python and performed side-by-side comparison with prior algorithms. Simulation results show that our proposed algorithm reduces the overall cost by at least 21.4% over the existing algorithms for accommodating the same service function chain requests.展开更多
The ITU in joint collaboration with ETSI NFV is organizing a workshop on“AI-Enabled Network Evolution:Cloud Native&AI Innovation for Telco Future”,which will take place on June 24,2026.The workshop will focus on...The ITU in joint collaboration with ETSI NFV is organizing a workshop on“AI-Enabled Network Evolution:Cloud Native&AI Innovation for Telco Future”,which will take place on June 24,2026.The workshop will focus on:·the integration of AI and telecom cloud/NFV based on the latest technological trends on telecom cloud architecture,network function orchestration,cloud-native and AI-native evolution.展开更多
The future Sixth-Generation (6G) wireless systems are expected to encounter emerging services with diverserequirements. In this paper, 6G network resource orchestration is optimized to support customized networkslicin...The future Sixth-Generation (6G) wireless systems are expected to encounter emerging services with diverserequirements. In this paper, 6G network resource orchestration is optimized to support customized networkslicing of services, and place network functions generated by heterogeneous devices into available resources.This is a combinatorial optimization problem that is solved by developing a Particle Swarm Optimization (PSO)based scheduling strategy with enhanced inertia weight, particle variation, and nonlinear learning factor, therebybalancing the local and global solutions and improving the convergence speed to globally near-optimal solutions.Simulations show that the method improves the convergence speed and the utilization of network resourcescompared with other variants of PSO.展开更多
Software Defined Network(SDN)and Network Function Virtualization(NFV)technology promote several benefits to network operators,including reduced maintenance costs,increased network operational performance,simplified ne...Software Defined Network(SDN)and Network Function Virtualization(NFV)technology promote several benefits to network operators,including reduced maintenance costs,increased network operational performance,simplified network lifecycle,and policies management.Network vulnerabilities try to modify services provided by Network Function Virtualization MANagement and Orchestration(NFV MANO),and malicious attacks in different scenarios disrupt the NFV Orchestrator(NFVO)and Virtualized Infrastructure Manager(VIM)lifecycle management related to network services or individual Virtualized Network Function(VNF).This paper proposes an anomaly detection mechanism that monitors threats in NFV MANO and manages promptly and adaptively to implement and handle security functions in order to enhance the quality of experience for end users.An anomaly detector investigates these identified risks and provides secure network services.It enables virtual network security functions and identifies anomalies in Kubernetes(a cloud-based platform).For training and testing purpose of the proposed approach,an intrusion-containing dataset is used that hold multiple malicious activities like a Smurf,Neptune,Teardrop,Pod,Land,IPsweep,etc.,categorized as Probing(Prob),Denial of Service(DoS),User to Root(U2R),and Remote to User(R2L)attacks.An anomaly detector is anticipated with the capabilities of a Machine Learning(ML)technique,making use of supervised learning techniques like Logistic Regression(LR),Support Vector Machine(SVM),Random Forest(RF),Naïve Bayes(NB),and Extreme Gradient Boosting(XGBoost).The proposed framework has been evaluated by deploying the identified ML algorithm on a Jupyter notebook in Kubeflow to simulate Kubernetes for validation purposes.RF classifier has shown better outcomes(99.90%accuracy)than other classifiers in detecting anomalies/intrusions in the containerized environment.展开更多
基金the financial support by the National Natural Science Foundation of China(Grant Nos.52402279,52572239)China Postdoctoral Science Foundation(Certificate Number:2024M751753,2025T180002)。
摘要Aqueous zinc-iodine batteries represent a compelling technology for largescale,sustainable energy storage,yet their practical application is severely hampered by the simultaneous interfacial challenges of uncontrolled dendrite growth on the zinc anode and the parasitic polyiodide shuttle.Herein,we introduce a dual-site functional orchestration strategy by employing a single electrolyte additive,2-imidazolidone(ELA),to concurrently stabilize both the anode and cathode interfaces.On the anode side,the carbonyl(C=O)functional group of ELA initiates an effective anodic modulation,regulating the Zn2+solvation environment and facilitating a dynamic adsorption layer.This homogenizes the ion flux and guides preferential Zn deposition along the(002)plane,effectively suppressing dendrite formation.Concurrently,at the cathode,the imino(N-H)group immobilizes soluble polyiodide species via hydrogen bonding,realizing an effective cathodic mooring.This targeted confinement arrests the shuttle effect without impeding the intrinsic redox kinetics.This synergistic stabilization translates into exceptional electrochemical performance,with symmetric cells achieving an ultra-long lifespan of over 5500 h at a high current density of 8 m A cm-2 and the full Zn||I2 cells demonstrating robust cycling with 79.4%capacity retention after 2500 cycles.This work introduces a dual-site functional orchestration strategy,offering a pathway toward more durable aqueous batteries.
基金supported by the China Postdoctoral Fund Project (No.44603)the National Natural Science Foundation of China (No.61309020)+1 种基金the National key Research and Development Program of China (No.2016YFB0800100, 2016YFB0800101)the National Natural Science Fund for Creative Research Groups Project(No.61521003)
摘要Software.defined networking(SDN) enables third.part companies to participate in the network function innovations. A number of instances for one network function will inevitably co.exist in the network. Although some orchestration architecture has been proposed to chain network functions, rare works are focused on how to optimize this process. In this paper, we propose an optimized model for network function orchestration, function combination model(FCM). Our main contributions are as following. First, network functions are featured with a new abstraction, and are open to external providers. And FCM identifies network functions using unique type, and organizes their instances distributed over the network with the appropriate way. Second, with the specialized demands, we can combine function instances under the global network views, and formulate it into the problem of Boolean linear program(BLP). A simulated annealing algorithm is designed to approach optimal solution for this BLP. Finally, the numerical experiment demonstrates that our model can create outstanding composite schemas efficiently.
基金supported by the National Natural Science Foundation of China(61501044)
摘要Network function virtualization is a new network concept that moves network functions from dedicated hardware to software-defined applications running on standard high volume severs. In order to accomplish network services, traffic flows are usually processed by a list of network functions in sequence which is defined by service function chain. By incorporating network function virtualization in inter-data center(DC) network, we can use the network resources intelligently and deploy network services faster. However, orchestrating service function chains across multiple data centers will incur high deployment cost, including the inter-data center bandwidth cost, virtual network function cost and the intra-data center bandwidth cost. In this paper, we orchestrate SFCs across multiple data centers, with a goal to minimize the overall cost. An integer linear programming(ILP) model is formulated and we provide a meta-heuristic algorithm named GBAO which contains three modules to solve it. We implemented our algorithm in Python and performed side-by-side comparison with prior algorithms. Simulation results show that our proposed algorithm reduces the overall cost by at least 21.4% over the existing algorithms for accommodating the same service function chain requests.
摘要The ITU in joint collaboration with ETSI NFV is organizing a workshop on“AI-Enabled Network Evolution:Cloud Native&AI Innovation for Telco Future”,which will take place on June 24,2026.The workshop will focus on:·the integration of AI and telecom cloud/NFV based on the latest technological trends on telecom cloud architecture,network function orchestration,cloud-native and AI-native evolution.
基金supported by the Social Scientific Research Foundation of China(21VSZ126).
摘要The future Sixth-Generation (6G) wireless systems are expected to encounter emerging services with diverserequirements. In this paper, 6G network resource orchestration is optimized to support customized networkslicing of services, and place network functions generated by heterogeneous devices into available resources.This is a combinatorial optimization problem that is solved by developing a Particle Swarm Optimization (PSO)based scheduling strategy with enhanced inertia weight, particle variation, and nonlinear learning factor, therebybalancing the local and global solutions and improving the convergence speed to globally near-optimal solutions.Simulations show that the method improves the convergence speed and the utilization of network resourcescompared with other variants of PSO.
基金This work was funded by the Deanship of Scientific Research at Jouf University under Grant Number(DSR2022-RG-0102).
摘要Software Defined Network(SDN)and Network Function Virtualization(NFV)technology promote several benefits to network operators,including reduced maintenance costs,increased network operational performance,simplified network lifecycle,and policies management.Network vulnerabilities try to modify services provided by Network Function Virtualization MANagement and Orchestration(NFV MANO),and malicious attacks in different scenarios disrupt the NFV Orchestrator(NFVO)and Virtualized Infrastructure Manager(VIM)lifecycle management related to network services or individual Virtualized Network Function(VNF).This paper proposes an anomaly detection mechanism that monitors threats in NFV MANO and manages promptly and adaptively to implement and handle security functions in order to enhance the quality of experience for end users.An anomaly detector investigates these identified risks and provides secure network services.It enables virtual network security functions and identifies anomalies in Kubernetes(a cloud-based platform).For training and testing purpose of the proposed approach,an intrusion-containing dataset is used that hold multiple malicious activities like a Smurf,Neptune,Teardrop,Pod,Land,IPsweep,etc.,categorized as Probing(Prob),Denial of Service(DoS),User to Root(U2R),and Remote to User(R2L)attacks.An anomaly detector is anticipated with the capabilities of a Machine Learning(ML)technique,making use of supervised learning techniques like Logistic Regression(LR),Support Vector Machine(SVM),Random Forest(RF),Naïve Bayes(NB),and Extreme Gradient Boosting(XGBoost).The proposed framework has been evaluated by deploying the identified ML algorithm on a Jupyter notebook in Kubeflow to simulate Kubernetes for validation purposes.RF classifier has shown better outcomes(99.90%accuracy)than other classifiers in detecting anomalies/intrusions in the containerized environment.