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
As energy efficiency is one of the key essentials towards sustainability, the development of an energy-resource efficient manufacturing system is among the great challenges facing the current industry. Meanwhile, the ...As energy efficiency is one of the key essentials towards sustainability, the development of an energy-resource efficient manufacturing system is among the great challenges facing the current industry. Meanwhile, the availability of advanced technological innovation has created more complex manufacturing systems that involve a large variety of processes and machines serving different functions. To extend the limited knowledge on energy-efficient scheduling, the research presented in this paper attempts to model the production schedule at an operation process by considering the balance of energy consumption reduction in production, production work flow （productivity） and quality. An innovative systematic approach to manufacturing energy-resource efficiency is proposed with the virtual simulation as a predictive modelling enabler, which provides real-time manufacturing monitoring, virtual displays and decision-makings and consequentially an analytical and multidimensional correlation analysis on interdependent relationships among energy consumption, work flow and quality errors. The regression analysis results demonstrate positive relationships between the work flow and quality errors and the work flow and energy consumption. When production scheduling is controlled through optimization of work flow, quality errors and overall energy consumption, the energy-resource efficiency can be achieved in the production. Together, this proposed multidimensional modelling and analysis approach provides optimal conditions for the production scheduling at the manufacturing system by taking account of production quality, energy consumption and resource efficiency, which can lead to the key competitive advantages and sustainability of the system operations in the industry.展开更多
This paper firstly analyses the necessity of virtual management in small-sized enterprises, then presents two main modes to implement virtual training management in small-sized enterprises. The paper also points out t...This paper firstly analyses the necessity of virtual management in small-sized enterprises, then presents two main modes to implement virtual training management in small-sized enterprises. The paper also points out the best mode for the small-sized enterprises＇ training to implement virtual management.展开更多
In order to improve the energy efficiency of large-scale data centers, a virtual machine(VM) deployment algorithm called three-threshold energy saving algorithm(TESA), which is based on the linear relation between the...In order to improve the energy efficiency of large-scale data centers, a virtual machine(VM) deployment algorithm called three-threshold energy saving algorithm(TESA), which is based on the linear relation between the energy consumption and(processor) resource utilization, is proposed. In TESA, according to load, hosts in data centers are divided into four classes, that is,host with light load, host with proper load, host with middle load and host with heavy load. By defining TESA, VMs on lightly loaded host or VMs on heavily loaded host are migrated to another host with proper load; VMs on properly loaded host or VMs on middling loaded host are kept constant. Then, based on the TESA, five kinds of VM selection policies(minimization of migrations policy based on TESA(MIMT), maximization of migrations policy based on TESA(MAMT), highest potential growth policy based on TESA(HPGT), lowest potential growth policy based on TESA(LPGT) and random choice policy based on TESA(RCT)) are presented, and MIMT is chosen as the representative policy through experimental comparison. Finally, five research directions are put forward on future energy management. The results of simulation indicate that, as compared with single threshold(ST) algorithm and minimization of migrations(MM) algorithm, MIMT significantly improves the energy efficiency in data centers.展开更多
After the global pandemic,DaaS(desktop as a service)has become the first choice of many companies’remote working solution.As the desktops are usually deployed in the public cloud when using DaaS,customers are more co...After the global pandemic,DaaS(desktop as a service)has become the first choice of many companies’remote working solution.As the desktops are usually deployed in the public cloud when using DaaS,customers are more cost-sensitive which boosts the requirement of proactive power management.Prior researches in this area focus on virtual desktop infrastructure(VDI)session logon behavior modeling,but for the remote desktop service host(RDSH)-shared desktop pools,logoff optimization is also important.Existing systems place sessions by round-robin or in a pre-defined order without considering their logoff time.However,these approaches usually suffer from the situation that few left sessions prevent RDSH servers from being powered-off which introduces cost waste.In this paper,we propose session placement via adaptive user logoff prediction(SODA),an innovative compound model towards proactive RDSH session placement.Specifically,an ensemble machine learning model that can predict session logoff time is combined with a statistical session placement bucket model to place RDSH sessions with similar logoff time in a more centralized manner on RDSH hosts.Consequently,the infrastructure cost-saving can be improved by reducing the resource waste introduced by those RDSH hosts with very few hanging sessions left for a long time.Experiments on real RDSH pool data demonstrate the effectiveness of the proposed proactive session placement approach against existing static placement techniques.展开更多
In the demand response process involving multi-agent participation,multiple parties’interests are involved and response execution status supervision is required.Traditional centralized demand response systems lack tr...In the demand response process involving multi-agent participation,multiple parties’interests are involved and response execution status supervision is required.Traditional centralized demand response systems lack trust attributes.At the same time,traditional centralized cloud management can no longer support massive terminal services,resulting in delays in demand response services.We build a distributed trusted demand response architecture based on blockchain,illustrating the information interaction process in the demand bidding process and container-based edge-side heterogeneous resource management.We also propose a demand bidding algorithm that takes into account both the day-ahead market and the intraday market,aiming to maximize the aggregator’s benefits.In addition,a virtual resource management algorithm to support demand response tasks is also proposed to optimize computing resource allocation and meet business latency requirements.Simulation results demonstrate that compared with only cloud computing or edge computing,the solution we proposed can reduce response delay by more than 39% for the sample system.Energy cost is saved by about 10.25%during container scheduling.展开更多
基金This work was funded by the Deanship of Scientific Research at Jouf University under Grant Number(DSR2022-RG-0102).
文摘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.
基金Supported by the EU 7th Framework ICT Programme under Euro Energest Project(Contract No.288102)
文摘As energy efficiency is one of the key essentials towards sustainability, the development of an energy-resource efficient manufacturing system is among the great challenges facing the current industry. Meanwhile, the availability of advanced technological innovation has created more complex manufacturing systems that involve a large variety of processes and machines serving different functions. To extend the limited knowledge on energy-efficient scheduling, the research presented in this paper attempts to model the production schedule at an operation process by considering the balance of energy consumption reduction in production, production work flow （productivity） and quality. An innovative systematic approach to manufacturing energy-resource efficiency is proposed with the virtual simulation as a predictive modelling enabler, which provides real-time manufacturing monitoring, virtual displays and decision-makings and consequentially an analytical and multidimensional correlation analysis on interdependent relationships among energy consumption, work flow and quality errors. The regression analysis results demonstrate positive relationships between the work flow and quality errors and the work flow and energy consumption. When production scheduling is controlled through optimization of work flow, quality errors and overall energy consumption, the energy-resource efficiency can be achieved in the production. Together, this proposed multidimensional modelling and analysis approach provides optimal conditions for the production scheduling at the manufacturing system by taking account of production quality, energy consumption and resource efficiency, which can lead to the key competitive advantages and sustainability of the system operations in the industry.
文摘This paper firstly analyses the necessity of virtual management in small-sized enterprises, then presents two main modes to implement virtual training management in small-sized enterprises. The paper also points out the best mode for the small-sized enterprises＇ training to implement virtual management.
基金Project(61272148) supported by the National Natural Science Foundation of ChinaProject(20120162110061) supported by the Doctoral Programs of Ministry of Education of China+1 种基金Project(CX2014B066) supported by the Hunan Provincial Innovation Foundation for Postgraduate,ChinaProject(2014zzts044) supported by the Fundamental Research Funds for the Central Universities,China
文摘In order to improve the energy efficiency of large-scale data centers, a virtual machine(VM) deployment algorithm called three-threshold energy saving algorithm(TESA), which is based on the linear relation between the energy consumption and(processor) resource utilization, is proposed. In TESA, according to load, hosts in data centers are divided into four classes, that is,host with light load, host with proper load, host with middle load and host with heavy load. By defining TESA, VMs on lightly loaded host or VMs on heavily loaded host are migrated to another host with proper load; VMs on properly loaded host or VMs on middling loaded host are kept constant. Then, based on the TESA, five kinds of VM selection policies(minimization of migrations policy based on TESA(MIMT), maximization of migrations policy based on TESA(MAMT), highest potential growth policy based on TESA(HPGT), lowest potential growth policy based on TESA(LPGT) and random choice policy based on TESA(RCT)) are presented, and MIMT is chosen as the representative policy through experimental comparison. Finally, five research directions are put forward on future energy management. The results of simulation indicate that, as compared with single threshold(ST) algorithm and minimization of migrations(MM) algorithm, MIMT significantly improves the energy efficiency in data centers.
文摘After the global pandemic,DaaS(desktop as a service)has become the first choice of many companies’remote working solution.As the desktops are usually deployed in the public cloud when using DaaS,customers are more cost-sensitive which boosts the requirement of proactive power management.Prior researches in this area focus on virtual desktop infrastructure(VDI)session logon behavior modeling,but for the remote desktop service host(RDSH)-shared desktop pools,logoff optimization is also important.Existing systems place sessions by round-robin or in a pre-defined order without considering their logoff time.However,these approaches usually suffer from the situation that few left sessions prevent RDSH servers from being powered-off which introduces cost waste.In this paper,we propose session placement via adaptive user logoff prediction(SODA),an innovative compound model towards proactive RDSH session placement.Specifically,an ensemble machine learning model that can predict session logoff time is combined with a statistical session placement bucket model to place RDSH sessions with similar logoff time in a more centralized manner on RDSH hosts.Consequently,the infrastructure cost-saving can be improved by reducing the resource waste introduced by those RDSH hosts with very few hanging sessions left for a long time.Experiments on real RDSH pool data demonstrate the effectiveness of the proposed proactive session placement approach against existing static placement techniques.
基金This work was supported by the project named Research on Distributed Trusted Intelligent Interactive Security Protection Technology for New Power System of State Grid Liaoning Electric Power Research Institute,Shenyang,China(No.2022YF-99(SGLNDK00NYJS2200142)).
文摘In the demand response process involving multi-agent participation,multiple parties’interests are involved and response execution status supervision is required.Traditional centralized demand response systems lack trust attributes.At the same time,traditional centralized cloud management can no longer support massive terminal services,resulting in delays in demand response services.We build a distributed trusted demand response architecture based on blockchain,illustrating the information interaction process in the demand bidding process and container-based edge-side heterogeneous resource management.We also propose a demand bidding algorithm that takes into account both the day-ahead market and the intraday market,aiming to maximize the aggregator’s benefits.In addition,a virtual resource management algorithm to support demand response tasks is also proposed to optimize computing resource allocation and meet business latency requirements.Simulation results demonstrate that compared with only cloud computing or edge computing,the solution we proposed can reduce response delay by more than 39% for the sample system.Energy cost is saved by about 10.25%during container scheduling.
