In the ELMy H-mode experiment,naturally occurring dust originating at the high-field side is clearly observed using the high-speed vacuum ultraviolet imaging system developed on the Experimental Advanced Superconducti...In the ELMy H-mode experiment,naturally occurring dust originating at the high-field side is clearly observed using the high-speed vacuum ultraviolet imaging system developed on the Experimental Advanced Superconducting Tokamak(EAST).The main ablation cloud shape is similar to the classical shape observed in pellet fueling experiments.However,during the dust penetration,an erupted secondary cloudlet with a bent‘cigar’shape is observed and moves upwards along the direction perpendicular to the magnetic field line,which is different to the obviation in the pellet fueling experiments.This may be due to the ion diamagnetic drift effect.The velocities of the secondary cloudlet are estimated to be 50–80 m s^(-1).In addition,a significant degradation of the plasma confinement is observed during the dust penetration.展开更多
This paper presents a novel fuzzy firefly-based intelligent algorithm for load balancing in mobile cloud computing while reducing makespan.The proposed technique implicitly acts intelligently by using inherent traits ...This paper presents a novel fuzzy firefly-based intelligent algorithm for load balancing in mobile cloud computing while reducing makespan.The proposed technique implicitly acts intelligently by using inherent traits of fuzzy and firefly.It automatically adjusts its behavior or converges depending on the information gathered during the search process and objective function.It works for 3-tier architecture,including cloudlet and public cloud.As cloudlets have limited resources,fuzzy logic is used for cloudlet selection using capacity and waiting time as input.Fuzzy provides human-like decisions without using any mathematical model.Firefly is a powerful meta-heuristic optimization technique to balance diversification and solution speed.It balances the load on cloud and cloudlet while minimizing makespan and execution time.However,it may trap in local optimum;levy flight can handle it.Hybridization of fuzzy fireflywith levy flight is a novel technique that provides reduced makespan,execution time,and Degree of imbalance while balancing the load.Simulation has been carried out on the Cloud Analyst platform with National Aeronautics and Space Administration(NASA)and Clarknet datasets.Results show that the proposed algorithm outperforms Ant Colony Optimization Queue Decision Maker(ACOQDM),Distributed Scheduling Optimization Algorithm(DSOA),andUtility-based Firefly Algorithm(UFA)when compared in terms of makespan,Degree of imbalance,and Figure of Merit.展开更多
As accessing computing resources from the remote cloud inherently incurs high end-to-end(E2E)delay for mobile users,cloudlets,which are deployed at the edge of a network,can potentially mitigate this problem.Although ...As accessing computing resources from the remote cloud inherently incurs high end-to-end(E2E)delay for mobile users,cloudlets,which are deployed at the edge of a network,can potentially mitigate this problem.Although some research works focus on allocating workloads among cloudlets,the cloudlet placement aiming to minimize the deployment cost(i.e.,consisting of both the cloudlet cost and average E2E delay cost)has not been addressed effectively so far.The locations and number of cloudlets have a crucial impact on both the cloudlet cost in the network and average E2E delay of users.Therefore,in this paper,we propose the Cost Aware cloudlet PlAcement in moBiLe Edge computing(CAPABLE)strategy,where both the cloudlet cost and average E2E delay are considered in the cloudlet placement.To solve this problem,a Lagrangian heuristic algorithm is developed to achieve the suboptimal solution.After cloudlets are placed in the network,we also design a workload allocation scheme to minimize the E2E delay between users and their cloudlets by considering the user mobility.The performance of CAPABLE has been validated by extensive simulations.展开更多
Despite the expanded efforts,the vehicular ad-hoc networks(VANETs)are still facing many challenges such as network performances,network scalability and context-awareness.Many solutions have been proposed to overcome t...Despite the expanded efforts,the vehicular ad-hoc networks(VANETs)are still facing many challenges such as network performances,network scalability and context-awareness.Many solutions have been proposed to overcome these obstacles,and the edge computing,an extension of the cloud computing,is one of them.With edge computing,communication,storage and computational capabilities are brought closer to end users.This could offer many benefits to the global vehicular network including,for example,lower latency,network off-loading and context-awareness(location,environment factors,etc.).Different approaches of edge computing have been developed:mobile edge computing(MEC),fog computing(FC)and cloudlet are the main ones.After introducing the vehicular environment background,this paper aims to study and compare these different technologies.For that purpose their main features are compared and the state-of-the-art applications in VANETs are analyzed.In addition,MEC,FC,and cloudlet are classified and their suitability level is debated for different types of vehicular applications.Finally,some challenges and future research directions in the fields of edge computing and VANETs are discussed.展开更多
In this study, we present a framework based on a prediction model that facilitates user access to a number of services in a smart living environment. Users must be able to access all available services continuously eq...In this study, we present a framework based on a prediction model that facilitates user access to a number of services in a smart living environment. Users must be able to access all available services continuously equipped with mobile devices or smart objects without being impacted by technical constraints such as performance or memory issues, regardless of their physical location and mobility. To achieve this goal, we propose the use of cloudlet-based architecture that serves as distributed cloud resources with specific ranges of influence and a realtime processing framework that tracks events and preferences of the end consumers, predicts their requirements,and recommends services to optimize resource utilization and service response time.展开更多
基金supported in part by the National Key R&D Program of China(Nos.2019YFE03080200,2022YFE03030001 and2022YFE03050003)National Natural Science Foundation of China(Nos.11975271,12075284,12075283 and 12175277)+1 种基金the Chinese Academy of President’s International Fellowship Initiative(No.2021VMA0022)the Post-CUP program,JSPS-CAS Bilateral Joint Research Projects,‘Control of wall recycling on metallic plasma facing materials in fusion reactor’,2019–2022,(No.GJHZ201984)。
文摘In the ELMy H-mode experiment,naturally occurring dust originating at the high-field side is clearly observed using the high-speed vacuum ultraviolet imaging system developed on the Experimental Advanced Superconducting Tokamak(EAST).The main ablation cloud shape is similar to the classical shape observed in pellet fueling experiments.However,during the dust penetration,an erupted secondary cloudlet with a bent‘cigar’shape is observed and moves upwards along the direction perpendicular to the magnetic field line,which is different to the obviation in the pellet fueling experiments.This may be due to the ion diamagnetic drift effect.The velocities of the secondary cloudlet are estimated to be 50–80 m s^(-1).In addition,a significant degradation of the plasma confinement is observed during the dust penetration.
基金funded by University Grant Commission with UGC-Ref.No.:3364/(NET-JUNE 2015).
文摘This paper presents a novel fuzzy firefly-based intelligent algorithm for load balancing in mobile cloud computing while reducing makespan.The proposed technique implicitly acts intelligently by using inherent traits of fuzzy and firefly.It automatically adjusts its behavior or converges depending on the information gathered during the search process and objective function.It works for 3-tier architecture,including cloudlet and public cloud.As cloudlets have limited resources,fuzzy logic is used for cloudlet selection using capacity and waiting time as input.Fuzzy provides human-like decisions without using any mathematical model.Firefly is a powerful meta-heuristic optimization technique to balance diversification and solution speed.It balances the load on cloud and cloudlet while minimizing makespan and execution time.However,it may trap in local optimum;levy flight can handle it.Hybridization of fuzzy fireflywith levy flight is a novel technique that provides reduced makespan,execution time,and Degree of imbalance while balancing the load.Simulation has been carried out on the Cloud Analyst platform with National Aeronautics and Space Administration(NASA)and Clarknet datasets.Results show that the proposed algorithm outperforms Ant Colony Optimization Queue Decision Maker(ACOQDM),Distributed Scheduling Optimization Algorithm(DSOA),andUtility-based Firefly Algorithm(UFA)when compared in terms of makespan,Degree of imbalance,and Figure of Merit.
基金supported in part by the National Science Foundation(CNS-1647170)
文摘As accessing computing resources from the remote cloud inherently incurs high end-to-end(E2E)delay for mobile users,cloudlets,which are deployed at the edge of a network,can potentially mitigate this problem.Although some research works focus on allocating workloads among cloudlets,the cloudlet placement aiming to minimize the deployment cost(i.e.,consisting of both the cloudlet cost and average E2E delay cost)has not been addressed effectively so far.The locations and number of cloudlets have a crucial impact on both the cloudlet cost in the network and average E2E delay of users.Therefore,in this paper,we propose the Cost Aware cloudlet PlAcement in moBiLe Edge computing(CAPABLE)strategy,where both the cloudlet cost and average E2E delay are considered in the cloudlet placement.To solve this problem,a Lagrangian heuristic algorithm is developed to achieve the suboptimal solution.After cloudlets are placed in the network,we also design a workload allocation scheme to minimize the E2E delay between users and their cloudlets by considering the user mobility.The performance of CAPABLE has been validated by extensive simulations.
文摘Despite the expanded efforts,the vehicular ad-hoc networks(VANETs)are still facing many challenges such as network performances,network scalability and context-awareness.Many solutions have been proposed to overcome these obstacles,and the edge computing,an extension of the cloud computing,is one of them.With edge computing,communication,storage and computational capabilities are brought closer to end users.This could offer many benefits to the global vehicular network including,for example,lower latency,network off-loading and context-awareness(location,environment factors,etc.).Different approaches of edge computing have been developed:mobile edge computing(MEC),fog computing(FC)and cloudlet are the main ones.After introducing the vehicular environment background,this paper aims to study and compare these different technologies.For that purpose their main features are compared and the state-of-the-art applications in VANETs are analyzed.In addition,MEC,FC,and cloudlet are classified and their suitability level is debated for different types of vehicular applications.Finally,some challenges and future research directions in the fields of edge computing and VANETs are discussed.
基金supported by the National Institute of Standards and Technologies(NIST)
文摘In this study, we present a framework based on a prediction model that facilitates user access to a number of services in a smart living environment. Users must be able to access all available services continuously equipped with mobile devices or smart objects without being impacted by technical constraints such as performance or memory issues, regardless of their physical location and mobility. To achieve this goal, we propose the use of cloudlet-based architecture that serves as distributed cloud resources with specific ranges of influence and a realtime processing framework that tracks events and preferences of the end consumers, predicts their requirements,and recommends services to optimize resource utilization and service response time.
