The unmanned aerial vehicle(UAV)-assisted mobile edge computing(MEC)has been deemed a promising solution for energy-constrained devices to run smart applications with computationintensive and latency-sensitive require...The unmanned aerial vehicle(UAV)-assisted mobile edge computing(MEC)has been deemed a promising solution for energy-constrained devices to run smart applications with computationintensive and latency-sensitive requirements,especially in some infrastructure-limited areas or some emergency scenarios.However,the multi-UAVassisted MEC network remains largely unexplored.In this paper,the dynamic trajectory optimization and computation offloading are studied in a multi-UAVassisted MEC system where multiple UAVs fly over a target area with different trajectories to serve ground users.By considering the dynamic channel condition and random task arrival and jointly optimizing UAVs'trajectories,user association,and subchannel assignment,the average long-term sum of the user energy consumption minimization problem is formulated.To address the problem involving both discrete and continuous variables,a hybrid decision deep reinforcement learning(DRL)-based intelligent energyefficient resource allocation and trajectory optimization algorithm is proposed,named HDRT algorithm,where deep Q network(DQN)and deep deterministic policy gradient(DDPG)are invoked to process discrete and continuous variables,respectively.Simulation results show that the proposed HDRT algorithm converges fast and outperforms other benchmarks in the aspect of user energy consumption and latency.展开更多
As an essential element of intelligent trans-port systems,Internet of vehicles(IoV)has brought an immersive user experience recently.Meanwhile,the emergence of mobile edge computing(MEC)has enhanced the computational ...As an essential element of intelligent trans-port systems,Internet of vehicles(IoV)has brought an immersive user experience recently.Meanwhile,the emergence of mobile edge computing(MEC)has enhanced the computational capability of the vehicle which reduces task processing latency and power con-sumption effectively and meets the quality of service requirements of vehicle users.However,there are still some problems in the MEC-assisted IoV system such as poor connectivity and high cost.Unmanned aerial vehicles(UAVs)equipped with MEC servers have become a promising approach for providing com-munication and computing services to mobile vehi-cles.Hence,in this article,an optimal framework for the UAV-assisted MEC system for IoV to minimize the average system cost is presented.Through joint consideration of computational offloading decisions and computational resource allocation,the optimiza-tion problem of our proposed architecture is presented to reduce system energy consumption and delay.For purpose of tackling this issue,the original non-convex issue is converted into a convex issue and the alternat-ing direction method of multipliers-based distributed optimal scheme is developed.The simulation results illustrate that the presented scheme can enhance the system performance dramatically with regard to other schemes,and the convergence of the proposed scheme is also significant.展开更多
Non-panoramic virtual reality(VR)provides users with immersive experiences involving strong interactivity,thus attracting growing research and development attention.However,the demand for high bandwidth and low latenc...Non-panoramic virtual reality(VR)provides users with immersive experiences involving strong interactivity,thus attracting growing research and development attention.However,the demand for high bandwidth and low latency in VR services presents greater challenges to existing networks.Inspired by mobile edge computing(MEC),VR users can offload rendering tasks to other devices.The main challenge of task offloading is to minimize latency and energy consumption.Yet,in non-panoramic VR scenarios,it is essential to consider the Quality of Perceptual Experience(QOPE)for users.Simultaneously,one must also take into account the diverse requirements of users in real-world scenarios.Therefore,this paper proposes a QOPE model to measure the visual quality of non-panoramic VR users and models the non-panoramic VR task offloading problem based on MEC as a constrained multi-objective optimization problem(CMOP)that minimizes latency and energy consumption while providing a satisfied QOPE.And we propose an evolutionary algorithm(EA),GNSGA-II,to solve the CMOP.Simulation results show that the algorithm can effectively find various trade-off solutions among the objectives,satisfying the requirements of different users.展开更多
Mobile Edge Computing(MEC) is an emerging technology in 5G era which enables the provision of the cloud and IT services within the close proximity of mobile subscribers.It allows the availability of the cloud servers ...Mobile Edge Computing(MEC) is an emerging technology in 5G era which enables the provision of the cloud and IT services within the close proximity of mobile subscribers.It allows the availability of the cloud servers inside or adjacent to the base station.The endto-end latency perceived by the mobile user is therefore reduced with the MEC platform.The context-aware services are able to be served by the application developers by leveraging the real time radio access network information from MEC.The MEC additionally enables the compute intensive applications execution in the resource constraint devices with the collaborative computing involving the cloud servers.This paper presents the architectural description of the MEC platform as well as the key functionalities enabling the above features.The relevant state-of-the-art research efforts are then surveyed.The paper finally discusses and identifies the open research challenges of MEC.展开更多
By Mobile Edge Computing(MEC), computation-intensive tasks are offloaded from mobile devices to cloud servers, and thus the energy consumption of mobile devices can be notably reduced. In this paper, we study task off...By Mobile Edge Computing(MEC), computation-intensive tasks are offloaded from mobile devices to cloud servers, and thus the energy consumption of mobile devices can be notably reduced. In this paper, we study task offloading in multi-user MEC systems with heterogeneous clouds, including edge clouds and remote clouds. Tasks are forwarded from mobile devices to edge clouds via wireless channels, and they can be further forwarded to remote clouds via the Internet. Our objective is to minimize the total energy consumption of multiple mobile devices, subject to bounded-delay requirements of tasks. Based on dynamic programming, we propose an algorithm that minimizes the energy consumption, by jointly allocating bandwidth and computational resources to mobile devices. The algorithm is of pseudo-polynomial complexity. To further reduce the complexity, we propose an approximation algorithm with energy discretization, and its total energy consumption is proved to be within a bounded gap from the optimum. Simulation results show that, nearly 82.7% energy of mobile devices can be saved by task offloading compared with mobile device execution.展开更多
Mobile edge computing （MEC） is a novel technique that can reduce mobiles＇ com- putational burden by tasks offioading, which emerges as a promising paradigm to provide computing capabilities in close proximity to mo...Mobile edge computing （MEC） is a novel technique that can reduce mobiles＇ com- putational burden by tasks offioading, which emerges as a promising paradigm to provide computing capabilities in close proximity to mobile users. In this paper, we will study the scenario where multiple mobiles upload tasks to a MEC server in a sing cell, and allocating the limited server resources and wireless chan- nels between mobiles becomes a challenge. We formulate the optimization problem for the energy saved on mobiles with the tasks being dividable, and utilize a greedy choice to solve the problem. A Select Maximum Saved Energy First （SMSEF） algorithm is proposed to realize the solving process. We examined the saved energy at different number of nodes and channels, and the results show that the proposed scheme can effectively help mobiles to save energy in the MEC system.展开更多
The rapid growth of mobile internet services has yielded a variety of computation-intensive applications such as virtual/augmented reality. Mobile Edge Computing (MEC), which enables mobile terminals to offload comput...The rapid growth of mobile internet services has yielded a variety of computation-intensive applications such as virtual/augmented reality. Mobile Edge Computing (MEC), which enables mobile terminals to offload computation tasks to servers located at the edge of the cellular networks, has been considered as an efficient approach to relieve the heavy computational burdens and realize an efficient computation offloading. Driven by the consequent requirement for proper resource allocations for computation offloading via MEC, in this paper, we propose a Deep-Q Network (DQN) based task offloading and resource allocation algorithm for the MEC. Specifically, we consider a MEC system in which every mobile terminal has multiple tasks offloaded to the edge server and design a joint task offloading decision and bandwidth allocation optimization to minimize the overall offloading cost in terms of energy cost, computation cost, and delay cost. Although the proposed optimization problem is a mixed integer nonlinear programming in nature, we exploit an emerging DQN technique to solve it. Extensive numerical results show that our proposed DQN-based approach can achieve the near-optimal performance。展开更多
This article establishes a three-tier mobile edge computing(MEC) network, which takes into account the cooperation between unmanned aerial vehicles(UAVs). In this MEC network, we aim to minimize the processing delay o...This article establishes a three-tier mobile edge computing(MEC) network, which takes into account the cooperation between unmanned aerial vehicles(UAVs). In this MEC network, we aim to minimize the processing delay of tasks by jointly optimizing the deployment of UAVs and offloading decisions,while meeting the computing capacity constraint of UAVs. However, the resulting optimization problem is nonconvex, which cannot be solved by general optimization tools in an effective and efficient way. To this end, we propose a two-layer optimization algorithm to tackle the non-convexity of the problem by capitalizing on alternating optimization. In the upper level algorithm, we rely on differential evolution(DE) learning algorithm to solve the deployment of the UAVs. In the lower level algorithm, we exploit distributed deep neural network(DDNN) to generate offloading decisions. Numerical results demonstrate that the two-layer optimization algorithm can effectively obtain the near-optimal deployment of UAVs and offloading strategy with low complexity.展开更多
The unmanned aerial vehicle(UAV)-enabled mobile edge computing(MEC) architecture is expected to be a powerful technique to facilitate 5 G and beyond ubiquitous wireless connectivity and diverse vertical applications a...The unmanned aerial vehicle(UAV)-enabled mobile edge computing(MEC) architecture is expected to be a powerful technique to facilitate 5 G and beyond ubiquitous wireless connectivity and diverse vertical applications and services, anytime and anywhere. Wireless power transfer(WPT) is another promising technology to prolong the operation time of low-power wireless devices in the era of Internet of Things(IoT). However, the integration of WPT and UAV-enabled MEC systems is far from being well studied, especially in dynamic environments. In order to tackle this issue, this paper aims to investigate the stochastic computation offloading and trajectory scheduling for the UAV-enabled wireless powered MEC system. A UAV offers both RF wireless power transmission and computation services for IoT devices. Considering the stochastic task arrivals and random channel conditions, a long-term average energyefficiency(EE) minimization problem is formulated.Due to non-convexity and the time domain coupling of the variables in the formulated problem, a lowcomplexity online computation offloading and trajectory scheduling algorithm(OCOTSA) is proposed by exploiting Lyapunov optimization. Simulation results verify that there exists a balance between EE and the service delay, and demonstrate that the system EE performance obtained by the proposed scheme outperforms other benchmark schemes.展开更多
Through enabling the IT and cloud computation capacities at Radio Access Network(RAN),Mobile Edge Computing(MEC) makes it possible to deploy and provide services locally.Therefore,MEC becomes the potential technology ...Through enabling the IT and cloud computation capacities at Radio Access Network(RAN),Mobile Edge Computing(MEC) makes it possible to deploy and provide services locally.Therefore,MEC becomes the potential technology to satisfy the requirements of 5G network to a certain extent,due to its functions of services localization,local breakout,caching,computation offloading,network context information exposure,etc.Especially,MEC can decrease the end-to-end latency dramatically through service localization and caching,which is key requirement of 5G low latency scenario.However,the performance of MEC still needs to be evaluated and verified for future deployment.Thus,the concept of MEC is introduced into5 G architecture and analyzed for different 5G scenarios in this paper.Secondly,the evaluation of MEC performance is conducted and analyzed in detail,especially for network end-to-end latency.In addition,some challenges of the MEC are also discussed for future deployment.展开更多
For the mobile edge computing network consisting of multiple base stations and resourceconstrained user devices,network cost in terms of energy and delay will incur during task offloading from the user to the edge ser...For the mobile edge computing network consisting of multiple base stations and resourceconstrained user devices,network cost in terms of energy and delay will incur during task offloading from the user to the edge server.With the limitations imposed on transmission capacity,computing resource,and connection capacity,the per-slot online learning algorithm is first proposed to minimize the time-averaged network cost.In particular,by leveraging the theories of stochastic gradient descent and minimum cost maximum flow,the user association is jointly optimized with resource scheduling in each time slot.The theoretical analysis proves that the proposed approach can achieve asymptotic optimality without any prior knowledge of the network environment.Moreover,to alleviate the high network overhead incurred during user handover and task migration,a two-timescale optimization approach is proposed to avoid frequent changes in user association.With user association executed on a large timescale and the resource scheduling decided on the single time slot,the asymptotic optimality is preserved.Simulation results verify the effectiveness of the proposed online learning algorithms.展开更多
To reduce the transmission latency and mitigate the backhaul burden of the centralized cloud-based network services,the mobile edge computing(MEC)has been drawing increased attention from both industry and academia re...To reduce the transmission latency and mitigate the backhaul burden of the centralized cloud-based network services,the mobile edge computing(MEC)has been drawing increased attention from both industry and academia recently.This paper focuses on mobile users’computation offloading problem in wireless cellular networks with mobile edge computing for the purpose of optimizing the computation offloading decision making policy.Since wireless network states and computing requests have stochastic properties and the environment’s dynamics are unknown,we use the modelfree reinforcement learning(RL)framework to formulate and tackle the computation offloading problem.Each mobile user learns through interactions with the environment and the estimate of its performance in the form of value function,then it chooses the overhead-aware optimal computation offloading action(local computing or edge computing)based on its state.The state spaces are high-dimensional in our work and value function is unrealistic to estimate.Consequently,we use deep reinforcement learning algorithm,which combines RL method Q-learning with the deep neural network(DNN)to approximate the value functions for complicated control applications,and the optimal policy will be obtained when the value function reaches convergence.Simulation results showed that the effectiveness of the proposed method in comparison with baseline methods in terms of total overheads of all mobile users.展开更多
The demand for digital media services is increasing as the number of wireless subscriptions is growing exponentially.In order to meet this growing need,mobile wireless networks have been advanced at a tremendous pace ...The demand for digital media services is increasing as the number of wireless subscriptions is growing exponentially.In order to meet this growing need,mobile wireless networks have been advanced at a tremendous pace over recent days.However,the centralized architecture of existing mobile networks,with limited capacity and range of bandwidth of the radio access network and low bandwidth back-haul network,can not handle the exponentially increasing mobile traffic.Recently,we have seen the growth of new mechanisms of data caching and delivery methods through intermediate caching servers.In this paper,we present a survey on recent advances in mobile edge computing and content caching,including caching insertion and expulsion policies,the behavior of the caching system,and caching optimization based on wireless networks.Some of the important open challenges in mobile edge computing with content caching are identified and discussed.We have also compared edge,fog and cloud computing in terms of delay.Readers of this paper will get a thorough understanding of recent advances in mobile edge computing and content caching in mobile wireless networks.展开更多
In this manuscript, a cooperative non-orthogonal multiple access based intelligent mobile edge computing(NOMA-MEC) communication system is constructed in detail. The nearby user is viewed as a decoding and forwarding ...In this manuscript, a cooperative non-orthogonal multiple access based intelligent mobile edge computing(NOMA-MEC) communication system is constructed in detail. The nearby user is viewed as a decoding and forwarding relay, which can assist a distant user in offloading tasks to the intelligent MEC server. Then, the closed-form expressions of offloading outage probability for a pair of users are derived in detail to evaluate the performance of the cooperative NOMA-MEC system. Furthermore, the approximate expressions of offloading outage probability are provided in the high signal-to-noise ratio region. Based on the asymptotic analyses, the diversity order of distant user and nearby user is n+m+1 and n+1, respectively. The system throughput and energy efficiency of cooperative NOMA-MEC are analyzed in delay-limited transmission mode. Numerical results show that 1) Cooperative NOMA-MEC is better than orthogonal multiple access(OMA) in terms of offload performance;2) The offload performance of cooperative NOMA-MEC system improves as the number of transmission task decreases;and 3) Cooperative NOMA-MEC performs better than OMA in energy efficiency.展开更多
With the arrival of 5G,latency-sensitive applications are becoming increasingly diverse.Mobile Edge Computing(MEC)technology has the characteristics of high bandwidth,low latency and low energy consumption,and has att...With the arrival of 5G,latency-sensitive applications are becoming increasingly diverse.Mobile Edge Computing(MEC)technology has the characteristics of high bandwidth,low latency and low energy consumption,and has attracted much attention among researchers.To improve the Quality of Service(QoS),this study focuses on computation offloading in MEC.We consider the QoS from the perspective of computational cost,dimensional disaster,user privacy and catastrophic forgetting of new users.The QoS model is established based on the delay and energy consumption and is based on DDQN and a Federated Learning(FL)adaptive task offloading algorithm in MEC.The proposed algorithm combines the QoS model and deep reinforcement learning algorithm to obtain an optimal offloading policy according to the local link and node state information in the channel coherence time to address the problem of time-varying transmission channels and reduce the computing energy consumption and task processing delay.To solve the problems of privacy and catastrophic forgetting,we use FL to make distributed use of multiple users’data to obtain the decision model,protect data privacy and improve the model universality.In the process of FL iteration,the communication delay of individual devices is too large,which affects the overall delay cost.Therefore,we adopt a communication delay optimization algorithm based on the unary outlier detection mechanism to reduce the communication delay of FL.The simulation results indicate that compared with existing schemes,the proposed method significantly reduces the computation cost on a device and improves the QoS when handling complex tasks.展开更多
By pushing computation,cache,and network control to the edge,mobile edge computing(MEC)is expected to play a leading role in fifth generation(5G)and future sixth generation(6G).Nevertheless,facing ubiquitous fast-grow...By pushing computation,cache,and network control to the edge,mobile edge computing(MEC)is expected to play a leading role in fifth generation(5G)and future sixth generation(6G).Nevertheless,facing ubiquitous fast-growing computational demands,it is impossible for a single MEC paradigm to effectively support high-quality intelligent services at end user equipments(UEs).To address this issue,we propose an air-ground collaborative MEC(AGCMEC)architecture in this article.The proposed AGCMEC integrates all potentially available MEC servers within air and ground in the envisioned 6G,by a variety of collaborative ways to provide computation services at their best for UEs.Firstly,we introduce the AGC-MEC architecture and elaborate three typical use cases.Then,we discuss four main challenges in the AGC-MEC as well as their potential solutions.Next,we conduct a case study of collaborative service placement for AGC-MEC to validate the effectiveness of the proposed collaborative service placement strategy.Finally,we highlight several potential research directions of the AGC-MEC.展开更多
Mobile Edge Computing (MEC) has been considered a promising solution that can address capacity and performance challenges in legacy systems such as Mobile Cloud Computing (MCC). In particular, such challenges include ...Mobile Edge Computing (MEC) has been considered a promising solution that can address capacity and performance challenges in legacy systems such as Mobile Cloud Computing (MCC). In particular, such challenges include intolerable delay, congestion in the core network, insufficient Quality of Experience (QoE), high cost of resource utility, such as energy and bandwidth. The aforementioned challenges originate from limited resources in mobile devices, the multi-hop connection between end-users and the cloud, high pressure from computation-intensive and delay-critical applications. Considering the limited resource setting at the MEC, improving the efficiency of task offloading in terms of both energy and delay in MEC applications is an important and urgent problem to be solved. In this paper, the key objective is to propose a task offloading scheme that minimizes the overall energy consumption along with satisfying capacity and delay requirements. Thus, we propose a MEC-assisted energy-efficient task offloading scheme that leverages the cooperative MEC framework. To achieve energy efficiency, we propose a novel hybrid approach established based on Particle Swarm Optimization (PSO) and Grey Wolf Optimizer (GWO) to solve the optimization problem. The proposed approach considers efficient resource allocation such as sub-carriers, power, and bandwidth for offloading to guarantee minimum energy consumption. The simulation results demonstrate that the proposed strategy is computational-efficient compared to benchmark methods. Moreover, it improves energy utilization, energy gain, response delay, and offloading utility.展开更多
The development of intelligent connected vehicles(ICVs)has tremendously inspired the emergence of a new computing paradigm called mobile edge computing(MEC),which meets the demands of delay-sensitive on-vehicle applic...The development of intelligent connected vehicles(ICVs)has tremendously inspired the emergence of a new computing paradigm called mobile edge computing(MEC),which meets the demands of delay-sensitive on-vehicle applications.Most existing studies focusing on the issue of task offloading in ICVs assume that the MEC server can directly complete computation tasks without considering the necessity of service caching.However,this is unrealistic in practice because a large number of tasks require the use of corresponding third-party libraries and databases,that is,service caching.Therefore,we investigate the delay optimization in an MEC-enabled ICVs system with multiple mobile vehicles,resource-limited base stations(BSs),and one cloud server.We aim to determine the optimal service caching and task offloading decisions to minimize the overall system delay using mixed-integer nonlinear programming.To address this problem,we first convert it into a quadratically constrained quadratic program and then propose an efficient semidefinite relaxation-based joint service caching and task offloading(JSCTO)algorithm to obtain the service caching and task offloading decisions.In the simulations,we validate the efficiency of our proposed method by setting different numbers of vehicles and the storage capacity of BSs.The results show that our proposed JSCTO algorithm can significantly decrease the total delay of all offloaded tasks compared with the cloud processing only scheme.展开更多
Mobile Edge Computing(MEC)has been envisioned as an efficient solution to provide computation-intensive yet latency-sensitive services for wireless devices.In this paper,we investigate the optimal dynamic spectrum all...Mobile Edge Computing(MEC)has been envisioned as an efficient solution to provide computation-intensive yet latency-sensitive services for wireless devices.In this paper,we investigate the optimal dynamic spectrum allocation-assisted multiuser computation offloading in MEC for overall latency minimization.Specifically,we first focus on a static multiuser computation offloading scenario and jointly optimize users'offloading decisions,transmission durations,and Edge Servers'(ESs)resource allocations.Owing to the nonconvexity of our joint optimization problem,we identify its layered structure and decompose it into two problems:a subproblem and a top problem.For the subproblem,we propose a bisection search-based algorithm to efficiently find the optimal users'offloading decisions and ESs’resource allocations under a given transmission duration.Second,we use a linear search-based algorithm for solving the top problem to obtain the optimal transmission duration based on the result of the subproblem.Further,after solving the static scenario,we consider a dynamic scenario of multiuser computation offloading with time-varying channels and workload.To efficiently address this dynamic scenario,we propose a deep reinforcement learning-based online algorithm to determine the near-optimal transmission duration in a real-time manner.Numerical results are provided to validate our proposed algorithms for minimizing the overall latency in both static and dynamic offloading scenarios.We also demonstrate the advantages of our proposed algorithms compared to the conventional multiuser computation offloading schemes.展开更多
Limited by battery and computing re-sources,the computing-intensive tasks generated by Internet of Things(IoT)devices cannot be processed all by themselves.Mobile edge computing(MEC)is a suitable solution for this pro...Limited by battery and computing re-sources,the computing-intensive tasks generated by Internet of Things(IoT)devices cannot be processed all by themselves.Mobile edge computing(MEC)is a suitable solution for this problem,and the gener-ated tasks can be offloaded from IoT devices to MEC.In this paper,we study the problem of dynamic task offloading for digital twin-empowered MEC.Digital twin techniques are applied to provide information of environment and share the training data of agent de-ployed on IoT devices.We formulate the task offload-ing problem with the goal of maximizing the energy efficiency and the workload balance among the ESs.Then,we reformulate the problem as an MDP problem and design DRL-based energy efficient task offloading(DEETO)algorithm to solve it.Comparative experi-ments are carried out which show the superiority of our DEETO algorithm in improving energy efficiency and balancing the workload.展开更多
基金supported by National Natural Science Foundation of China(No.62471254)National Natural Science Foundation of China(No.92367302)。
文摘The unmanned aerial vehicle(UAV)-assisted mobile edge computing(MEC)has been deemed a promising solution for energy-constrained devices to run smart applications with computationintensive and latency-sensitive requirements,especially in some infrastructure-limited areas or some emergency scenarios.However,the multi-UAVassisted MEC network remains largely unexplored.In this paper,the dynamic trajectory optimization and computation offloading are studied in a multi-UAVassisted MEC system where multiple UAVs fly over a target area with different trajectories to serve ground users.By considering the dynamic channel condition and random task arrival and jointly optimizing UAVs'trajectories,user association,and subchannel assignment,the average long-term sum of the user energy consumption minimization problem is formulated.To address the problem involving both discrete and continuous variables,a hybrid decision deep reinforcement learning(DRL)-based intelligent energyefficient resource allocation and trajectory optimization algorithm is proposed,named HDRT algorithm,where deep Q network(DQN)and deep deterministic policy gradient(DDPG)are invoked to process discrete and continuous variables,respectively.Simulation results show that the proposed HDRT algorithm converges fast and outperforms other benchmarks in the aspect of user energy consumption and latency.
基金in part by the National Natural Science Foundation of China(NSFC)under Grant 62371012in part by the Beijing Natural Science Foundation under Grant 4252001.
文摘As an essential element of intelligent trans-port systems,Internet of vehicles(IoV)has brought an immersive user experience recently.Meanwhile,the emergence of mobile edge computing(MEC)has enhanced the computational capability of the vehicle which reduces task processing latency and power con-sumption effectively and meets the quality of service requirements of vehicle users.However,there are still some problems in the MEC-assisted IoV system such as poor connectivity and high cost.Unmanned aerial vehicles(UAVs)equipped with MEC servers have become a promising approach for providing com-munication and computing services to mobile vehi-cles.Hence,in this article,an optimal framework for the UAV-assisted MEC system for IoV to minimize the average system cost is presented.Through joint consideration of computational offloading decisions and computational resource allocation,the optimiza-tion problem of our proposed architecture is presented to reduce system energy consumption and delay.For purpose of tackling this issue,the original non-convex issue is converted into a convex issue and the alternat-ing direction method of multipliers-based distributed optimal scheme is developed.The simulation results illustrate that the presented scheme can enhance the system performance dramatically with regard to other schemes,and the convergence of the proposed scheme is also significant.
基金supported by National Natural Science Foundation of China(No.62101499)Science and National Key Research and Development Program of China(2019YFB1803200).
文摘Non-panoramic virtual reality(VR)provides users with immersive experiences involving strong interactivity,thus attracting growing research and development attention.However,the demand for high bandwidth and low latency in VR services presents greater challenges to existing networks.Inspired by mobile edge computing(MEC),VR users can offload rendering tasks to other devices.The main challenge of task offloading is to minimize latency and energy consumption.Yet,in non-panoramic VR scenarios,it is essential to consider the Quality of Perceptual Experience(QOPE)for users.Simultaneously,one must also take into account the diverse requirements of users in real-world scenarios.Therefore,this paper proposes a QOPE model to measure the visual quality of non-panoramic VR users and models the non-panoramic VR task offloading problem based on MEC as a constrained multi-objective optimization problem(CMOP)that minimizes latency and energy consumption while providing a satisfied QOPE.And we propose an evolutionary algorithm(EA),GNSGA-II,to solve the CMOP.Simulation results show that the algorithm can effectively find various trade-off solutions among the objectives,satisfying the requirements of different users.
文摘Mobile Edge Computing(MEC) is an emerging technology in 5G era which enables the provision of the cloud and IT services within the close proximity of mobile subscribers.It allows the availability of the cloud servers inside or adjacent to the base station.The endto-end latency perceived by the mobile user is therefore reduced with the MEC platform.The context-aware services are able to be served by the application developers by leveraging the real time radio access network information from MEC.The MEC additionally enables the compute intensive applications execution in the resource constraint devices with the collaborative computing involving the cloud servers.This paper presents the architectural description of the MEC platform as well as the key functionalities enabling the above features.The relevant state-of-the-art research efforts are then surveyed.The paper finally discusses and identifies the open research challenges of MEC.
基金the National Key R&D Program of China 2018YFB1800804the Nature Science Foundation of China (No. 61871254,No. 61861136003,No. 91638204)Hitachi Ltd.
文摘By Mobile Edge Computing(MEC), computation-intensive tasks are offloaded from mobile devices to cloud servers, and thus the energy consumption of mobile devices can be notably reduced. In this paper, we study task offloading in multi-user MEC systems with heterogeneous clouds, including edge clouds and remote clouds. Tasks are forwarded from mobile devices to edge clouds via wireless channels, and they can be further forwarded to remote clouds via the Internet. Our objective is to minimize the total energy consumption of multiple mobile devices, subject to bounded-delay requirements of tasks. Based on dynamic programming, we propose an algorithm that minimizes the energy consumption, by jointly allocating bandwidth and computational resources to mobile devices. The algorithm is of pseudo-polynomial complexity. To further reduce the complexity, we propose an approximation algorithm with energy discretization, and its total energy consumption is proved to be within a bounded gap from the optimum. Simulation results show that, nearly 82.7% energy of mobile devices can be saved by task offloading compared with mobile device execution.
基金supported by NSFC(No. 61571055)fund of SKL of MMW (No. K201815)Important National Science & Technology Specific Projects(2017ZX03001028)
文摘Mobile edge computing （MEC） is a novel technique that can reduce mobiles＇ com- putational burden by tasks offioading, which emerges as a promising paradigm to provide computing capabilities in close proximity to mobile users. In this paper, we will study the scenario where multiple mobiles upload tasks to a MEC server in a sing cell, and allocating the limited server resources and wireless chan- nels between mobiles becomes a challenge. We formulate the optimization problem for the energy saved on mobiles with the tasks being dividable, and utilize a greedy choice to solve the problem. A Select Maximum Saved Energy First （SMSEF） algorithm is proposed to realize the solving process. We examined the saved energy at different number of nodes and channels, and the results show that the proposed scheme can effectively help mobiles to save energy in the MEC system.
基金the National Natural Science Foundation of China under Grants No.61572440 and No.61502428the Zhejiang Provincial Natural Science Foundation of China under Grants No.LR16F010003 and No.LY19F020033.
文摘The rapid growth of mobile internet services has yielded a variety of computation-intensive applications such as virtual/augmented reality. Mobile Edge Computing (MEC), which enables mobile terminals to offload computation tasks to servers located at the edge of the cellular networks, has been considered as an efficient approach to relieve the heavy computational burdens and realize an efficient computation offloading. Driven by the consequent requirement for proper resource allocations for computation offloading via MEC, in this paper, we propose a Deep-Q Network (DQN) based task offloading and resource allocation algorithm for the MEC. Specifically, we consider a MEC system in which every mobile terminal has multiple tasks offloaded to the edge server and design a joint task offloading decision and bandwidth allocation optimization to minimize the overall offloading cost in terms of energy cost, computation cost, and delay cost. Although the proposed optimization problem is a mixed integer nonlinear programming in nature, we exploit an emerging DQN technique to solve it. Extensive numerical results show that our proposed DQN-based approach can achieve the near-optimal performance。
基金supported in part by National Natural Science Foundation of China (Grant No. 62101277)in part by the Natural Science Foundation of Jiangsu Province (Grant No. BK20200822)+1 种基金in part by the Natural Science Foundation of Jiangsu Higher Education Institutions of China (Grant No. 20KJB510036)in part by the Guangxi Key Laboratory of Multimedia Communications and Network Technology (Grant No. KLF-2020-03)。
文摘This article establishes a three-tier mobile edge computing(MEC) network, which takes into account the cooperation between unmanned aerial vehicles(UAVs). In this MEC network, we aim to minimize the processing delay of tasks by jointly optimizing the deployment of UAVs and offloading decisions,while meeting the computing capacity constraint of UAVs. However, the resulting optimization problem is nonconvex, which cannot be solved by general optimization tools in an effective and efficient way. To this end, we propose a two-layer optimization algorithm to tackle the non-convexity of the problem by capitalizing on alternating optimization. In the upper level algorithm, we rely on differential evolution(DE) learning algorithm to solve the deployment of the UAVs. In the lower level algorithm, we exploit distributed deep neural network(DDNN) to generate offloading decisions. Numerical results demonstrate that the two-layer optimization algorithm can effectively obtain the near-optimal deployment of UAVs and offloading strategy with low complexity.
基金supported in part by the U.S. National Science Foundation under Grant CNS-2007995in part by the National Natural Science Foundation of China under Grant 92067201,62171231in part by Jiangsu Provincial Key Research and Development Program under Grant BE2020084-1。
文摘The unmanned aerial vehicle(UAV)-enabled mobile edge computing(MEC) architecture is expected to be a powerful technique to facilitate 5 G and beyond ubiquitous wireless connectivity and diverse vertical applications and services, anytime and anywhere. Wireless power transfer(WPT) is another promising technology to prolong the operation time of low-power wireless devices in the era of Internet of Things(IoT). However, the integration of WPT and UAV-enabled MEC systems is far from being well studied, especially in dynamic environments. In order to tackle this issue, this paper aims to investigate the stochastic computation offloading and trajectory scheduling for the UAV-enabled wireless powered MEC system. A UAV offers both RF wireless power transmission and computation services for IoT devices. Considering the stochastic task arrivals and random channel conditions, a long-term average energyefficiency(EE) minimization problem is formulated.Due to non-convexity and the time domain coupling of the variables in the formulated problem, a lowcomplexity online computation offloading and trajectory scheduling algorithm(OCOTSA) is proposed by exploiting Lyapunov optimization. Simulation results verify that there exists a balance between EE and the service delay, and demonstrate that the system EE performance obtained by the proposed scheme outperforms other benchmark schemes.
基金supported by the National High Technology Research and Development Program(863) of China(No.2015AA01A701)
文摘Through enabling the IT and cloud computation capacities at Radio Access Network(RAN),Mobile Edge Computing(MEC) makes it possible to deploy and provide services locally.Therefore,MEC becomes the potential technology to satisfy the requirements of 5G network to a certain extent,due to its functions of services localization,local breakout,caching,computation offloading,network context information exposure,etc.Especially,MEC can decrease the end-to-end latency dramatically through service localization and caching,which is key requirement of 5G low latency scenario.However,the performance of MEC still needs to be evaluated and verified for future deployment.Thus,the concept of MEC is introduced into5 G architecture and analyzed for different 5G scenarios in this paper.Secondly,the evaluation of MEC performance is conducted and analyzed in detail,especially for network end-to-end latency.In addition,some challenges of the MEC are also discussed for future deployment.
基金the National Natural Science Foundation of China(61971066,61941114)the Beijing Natural Science Foundation(No.L182038)National Youth Top-notch Talent Support Program.
文摘For the mobile edge computing network consisting of multiple base stations and resourceconstrained user devices,network cost in terms of energy and delay will incur during task offloading from the user to the edge server.With the limitations imposed on transmission capacity,computing resource,and connection capacity,the per-slot online learning algorithm is first proposed to minimize the time-averaged network cost.In particular,by leveraging the theories of stochastic gradient descent and minimum cost maximum flow,the user association is jointly optimized with resource scheduling in each time slot.The theoretical analysis proves that the proposed approach can achieve asymptotic optimality without any prior knowledge of the network environment.Moreover,to alleviate the high network overhead incurred during user handover and task migration,a two-timescale optimization approach is proposed to avoid frequent changes in user association.With user association executed on a large timescale and the resource scheduling decided on the single time slot,the asymptotic optimality is preserved.Simulation results verify the effectiveness of the proposed online learning algorithms.
基金This work was supported by the National Natural Science Foundation of China(61571059 and 61871058).
文摘To reduce the transmission latency and mitigate the backhaul burden of the centralized cloud-based network services,the mobile edge computing(MEC)has been drawing increased attention from both industry and academia recently.This paper focuses on mobile users’computation offloading problem in wireless cellular networks with mobile edge computing for the purpose of optimizing the computation offloading decision making policy.Since wireless network states and computing requests have stochastic properties and the environment’s dynamics are unknown,we use the modelfree reinforcement learning(RL)framework to formulate and tackle the computation offloading problem.Each mobile user learns through interactions with the environment and the estimate of its performance in the form of value function,then it chooses the overhead-aware optimal computation offloading action(local computing or edge computing)based on its state.The state spaces are high-dimensional in our work and value function is unrealistic to estimate.Consequently,we use deep reinforcement learning algorithm,which combines RL method Q-learning with the deep neural network(DNN)to approximate the value functions for complicated control applications,and the optimal policy will be obtained when the value function reaches convergence.Simulation results showed that the effectiveness of the proposed method in comparison with baseline methods in terms of total overheads of all mobile users.
基金This work is partly supported by the US NSF under grants CNS 1650831,and HRD 1828811the U.S.Department of Homeland Security under grant DHS 2017-ST-062-000003.
文摘The demand for digital media services is increasing as the number of wireless subscriptions is growing exponentially.In order to meet this growing need,mobile wireless networks have been advanced at a tremendous pace over recent days.However,the centralized architecture of existing mobile networks,with limited capacity and range of bandwidth of the radio access network and low bandwidth back-haul network,can not handle the exponentially increasing mobile traffic.Recently,we have seen the growth of new mechanisms of data caching and delivery methods through intermediate caching servers.In this paper,we present a survey on recent advances in mobile edge computing and content caching,including caching insertion and expulsion policies,the behavior of the caching system,and caching optimization based on wireless networks.Some of the important open challenges in mobile edge computing with content caching are identified and discussed.We have also compared edge,fog and cloud computing in terms of delay.Readers of this paper will get a thorough understanding of recent advances in mobile edge computing and content caching in mobile wireless networks.
基金supported in part by the Natural Science Foundation of Beijing Municipality under Grant 4204099,Grant 19L2022,Grant L182032,Grant L182039 and Grant KZ201911232046the Science and Technology Project of Beijing Municipal Education Commission under Grant KM202011232002 and Grant KM202011232003。
文摘In this manuscript, a cooperative non-orthogonal multiple access based intelligent mobile edge computing(NOMA-MEC) communication system is constructed in detail. The nearby user is viewed as a decoding and forwarding relay, which can assist a distant user in offloading tasks to the intelligent MEC server. Then, the closed-form expressions of offloading outage probability for a pair of users are derived in detail to evaluate the performance of the cooperative NOMA-MEC system. Furthermore, the approximate expressions of offloading outage probability are provided in the high signal-to-noise ratio region. Based on the asymptotic analyses, the diversity order of distant user and nearby user is n+m+1 and n+1, respectively. The system throughput and energy efficiency of cooperative NOMA-MEC are analyzed in delay-limited transmission mode. Numerical results show that 1) Cooperative NOMA-MEC is better than orthogonal multiple access(OMA) in terms of offload performance;2) The offload performance of cooperative NOMA-MEC system improves as the number of transmission task decreases;and 3) Cooperative NOMA-MEC performs better than OMA in energy efficiency.
基金supported by the National Natural Science Foundation of China(62032013,62072094Liaoning Province Science and Technology Fund Project(2020MS086)+1 种基金Shenyang Science and Technology Plan Project(20206424)the Fundamental Research Funds for the Central Universities(N2116014,N180101028)CERNET Innovation Project(NGII20190504).
文摘With the arrival of 5G,latency-sensitive applications are becoming increasingly diverse.Mobile Edge Computing(MEC)technology has the characteristics of high bandwidth,low latency and low energy consumption,and has attracted much attention among researchers.To improve the Quality of Service(QoS),this study focuses on computation offloading in MEC.We consider the QoS from the perspective of computational cost,dimensional disaster,user privacy and catastrophic forgetting of new users.The QoS model is established based on the delay and energy consumption and is based on DDQN and a Federated Learning(FL)adaptive task offloading algorithm in MEC.The proposed algorithm combines the QoS model and deep reinforcement learning algorithm to obtain an optimal offloading policy according to the local link and node state information in the channel coherence time to address the problem of time-varying transmission channels and reduce the computing energy consumption and task processing delay.To solve the problems of privacy and catastrophic forgetting,we use FL to make distributed use of multiple users’data to obtain the decision model,protect data privacy and improve the model universality.In the process of FL iteration,the communication delay of individual devices is too large,which affects the overall delay cost.Therefore,we adopt a communication delay optimization algorithm based on the unary outlier detection mechanism to reduce the communication delay of FL.The simulation results indicate that compared with existing schemes,the proposed method significantly reduces the computation cost on a device and improves the QoS when handling complex tasks.
基金supported in part by the National Natural Science Foundation of China under Grant 62171465,62072303,62272223,U22A2031。
文摘By pushing computation,cache,and network control to the edge,mobile edge computing(MEC)is expected to play a leading role in fifth generation(5G)and future sixth generation(6G).Nevertheless,facing ubiquitous fast-growing computational demands,it is impossible for a single MEC paradigm to effectively support high-quality intelligent services at end user equipments(UEs).To address this issue,we propose an air-ground collaborative MEC(AGCMEC)architecture in this article.The proposed AGCMEC integrates all potentially available MEC servers within air and ground in the envisioned 6G,by a variety of collaborative ways to provide computation services at their best for UEs.Firstly,we introduce the AGC-MEC architecture and elaborate three typical use cases.Then,we discuss four main challenges in the AGC-MEC as well as their potential solutions.Next,we conduct a case study of collaborative service placement for AGC-MEC to validate the effectiveness of the proposed collaborative service placement strategy.Finally,we highlight several potential research directions of the AGC-MEC.
基金supported by the Chinese Scholarship Council(CSC)under MOFCOM(No.2017MOC010907)any opinions,findings,and conclusions are those of the authors and do not necessarily reflect the views of the above agency.
文摘Mobile Edge Computing (MEC) has been considered a promising solution that can address capacity and performance challenges in legacy systems such as Mobile Cloud Computing (MCC). In particular, such challenges include intolerable delay, congestion in the core network, insufficient Quality of Experience (QoE), high cost of resource utility, such as energy and bandwidth. The aforementioned challenges originate from limited resources in mobile devices, the multi-hop connection between end-users and the cloud, high pressure from computation-intensive and delay-critical applications. Considering the limited resource setting at the MEC, improving the efficiency of task offloading in terms of both energy and delay in MEC applications is an important and urgent problem to be solved. In this paper, the key objective is to propose a task offloading scheme that minimizes the overall energy consumption along with satisfying capacity and delay requirements. Thus, we propose a MEC-assisted energy-efficient task offloading scheme that leverages the cooperative MEC framework. To achieve energy efficiency, we propose a novel hybrid approach established based on Particle Swarm Optimization (PSO) and Grey Wolf Optimizer (GWO) to solve the optimization problem. The proposed approach considers efficient resource allocation such as sub-carriers, power, and bandwidth for offloading to guarantee minimum energy consumption. The simulation results demonstrate that the proposed strategy is computational-efficient compared to benchmark methods. Moreover, it improves energy utilization, energy gain, response delay, and offloading utility.
基金the National Natural Science Foundation of China(Nos.61772130 and 62072096)the Fundamental Research Funds for the Central Universities(No.2232020A-12)+1 种基金the International S&T Cooperation Program of Shanghai Science and Technology Commission(No.20220713000)the Young Top-Notch Talent Program in Shanghai。
文摘The development of intelligent connected vehicles(ICVs)has tremendously inspired the emergence of a new computing paradigm called mobile edge computing(MEC),which meets the demands of delay-sensitive on-vehicle applications.Most existing studies focusing on the issue of task offloading in ICVs assume that the MEC server can directly complete computation tasks without considering the necessity of service caching.However,this is unrealistic in practice because a large number of tasks require the use of corresponding third-party libraries and databases,that is,service caching.Therefore,we investigate the delay optimization in an MEC-enabled ICVs system with multiple mobile vehicles,resource-limited base stations(BSs),and one cloud server.We aim to determine the optimal service caching and task offloading decisions to minimize the overall system delay using mixed-integer nonlinear programming.To address this problem,we first convert it into a quadratically constrained quadratic program and then propose an efficient semidefinite relaxation-based joint service caching and task offloading(JSCTO)algorithm to obtain the service caching and task offloading decisions.In the simulations,we validate the efficiency of our proposed method by setting different numbers of vehicles and the storage capacity of BSs.The results show that our proposed JSCTO algorithm can significantly decrease the total delay of all offloaded tasks compared with the cloud processing only scheme.
基金supported in part by the Joint Scientific Research Project Funding Scheme between Macao Science and Technology Development Fund and the Ministry of Science and Technology of the People's Republic of China under Grant 0066/2019/AMJin part by the Intergovernmental International Cooperation in Science and Technology Innovation Program under Grants 2019YFE0111600+3 种基金in part by the Macao Science and Technology Development Fund under Grants 0060/2019/A1 and 0162/2019/A3in part by National Natural Science Foundation of China under Grant 62072490in part by Research Grant of University of Macao under Grants MYRG2018-00237-FST and SRG2019-00168-IOTSCin part by FDCT SKL-IOTSC(UM)-2021-2023.
文摘Mobile Edge Computing(MEC)has been envisioned as an efficient solution to provide computation-intensive yet latency-sensitive services for wireless devices.In this paper,we investigate the optimal dynamic spectrum allocation-assisted multiuser computation offloading in MEC for overall latency minimization.Specifically,we first focus on a static multiuser computation offloading scenario and jointly optimize users'offloading decisions,transmission durations,and Edge Servers'(ESs)resource allocations.Owing to the nonconvexity of our joint optimization problem,we identify its layered structure and decompose it into two problems:a subproblem and a top problem.For the subproblem,we propose a bisection search-based algorithm to efficiently find the optimal users'offloading decisions and ESs’resource allocations under a given transmission duration.Second,we use a linear search-based algorithm for solving the top problem to obtain the optimal transmission duration based on the result of the subproblem.Further,after solving the static scenario,we consider a dynamic scenario of multiuser computation offloading with time-varying channels and workload.To efficiently address this dynamic scenario,we propose a deep reinforcement learning-based online algorithm to determine the near-optimal transmission duration in a real-time manner.Numerical results are provided to validate our proposed algorithms for minimizing the overall latency in both static and dynamic offloading scenarios.We also demonstrate the advantages of our proposed algorithms compared to the conventional multiuser computation offloading schemes.
基金This work was partly supported by the Project of Cultivation for young top-motch Talents of Beijing Municipal Institutions(No BPHR202203225)the Young Elite Scientists Sponsorship Program by BAST(BYESS2023031)the National key research and development program(No 2022YFF0604502).
文摘Limited by battery and computing re-sources,the computing-intensive tasks generated by Internet of Things(IoT)devices cannot be processed all by themselves.Mobile edge computing(MEC)is a suitable solution for this problem,and the gener-ated tasks can be offloaded from IoT devices to MEC.In this paper,we study the problem of dynamic task offloading for digital twin-empowered MEC.Digital twin techniques are applied to provide information of environment and share the training data of agent de-ployed on IoT devices.We formulate the task offload-ing problem with the goal of maximizing the energy efficiency and the workload balance among the ESs.Then,we reformulate the problem as an MDP problem and design DRL-based energy efficient task offloading(DEETO)algorithm to solve it.Comparative experi-ments are carried out which show the superiority of our DEETO algorithm in improving energy efficiency and balancing the workload.
