期刊文献+
共找到1,441篇文章
< 1 2 73 >
每页显示 20 50 100
Task Offloading in Edge Computing Using GNNs and DQN
1
作者 Asier Garmendia-Orbegozo Jose David Nunez-Gonzalez Miguel Angel Anton 《Computer Modeling in Engineering & Sciences》 SCIE EI 2024年第6期2649-2671,共23页
In a network environment composed of different types of computing centers that can be divided into different layers(clod,edge layer,and others),the interconnection between them offers the possibility of peer-to-peer t... In a network environment composed of different types of computing centers that can be divided into different layers(clod,edge layer,and others),the interconnection between them offers the possibility of peer-to-peer task offloading.For many resource-constrained devices,the computation of many types of tasks is not feasible because they cannot support such computations as they do not have enough available memory and processing capacity.In this scenario,it is worth considering transferring these tasks to resource-rich platforms,such as Edge Data Centers or remote cloud servers.For different reasons,it is more exciting and appropriate to download various tasks to specific download destinations depending on the properties and state of the environment and the nature of the functions.At the same time,establishing an optimal offloading policy,which ensures that all tasks are executed within the required latency and avoids excessive workload on specific computing centers is not easy.This study presents two alternatives to solve the offloading decision paradigm by introducing two well-known algorithms,Graph Neural Networks(GNN)and Deep Q-Network(DQN).It applies the alternatives on a well-known Edge Computing simulator called PureEdgeSimand compares them with the two defaultmethods,Trade-Off and Round Robin.Experiments showed that variants offer a slight improvement in task success rate and workload distribution.In terms of energy efficiency,they provided similar results.Finally,the success rates of different computing centers are tested,and the lack of capacity of remote cloud servers to respond to applications in real-time is demonstrated.These novel ways of finding a download strategy in a local networking environment are unique as they emulate the state and structure of the environment innovatively,considering the quality of its connections and constant updates.The download score defined in this research is a crucial feature for determining the quality of a download path in the GNN training process and has not previously been proposed.Simultaneously,the suitability of Reinforcement Learning(RL)techniques is demonstrated due to the dynamism of the network environment,considering all the key factors that affect the decision to offload a given task,including the actual state of all devices. 展开更多
关键词 Edge computing edge offloading fog computing task offloading
下载PDF
A Task Offloading Strategy Based on Multi-Agent Deep Reinforcement Learning for Offshore Wind Farm Scenarios
2
作者 Zeshuang Song Xiao Wang +4 位作者 Qing Wu Yanting Tao Linghua Xu Yaohua Yin Jianguo Yan 《Computers, Materials & Continua》 SCIE EI 2024年第10期985-1008,共24页
This research is the first application of Unmanned Aerial Vehicles(UAVs)equipped with Multi-access Edge Computing(MEC)servers to offshore wind farms,providing a new task offloading solution to address the challenge of... This research is the first application of Unmanned Aerial Vehicles(UAVs)equipped with Multi-access Edge Computing(MEC)servers to offshore wind farms,providing a new task offloading solution to address the challenge of scarce edge servers in offshore wind farms.The proposed strategy is to offload the computational tasks in this scenario to other MEC servers and compute them proportionally,which effectively reduces the computational pressure on local MEC servers when wind turbine data are abnormal.Finally,the task offloading problem is modeled as a multi-intelligent deep reinforcement learning problem,and a task offloading model based on MultiAgent Deep Reinforcement Learning(MADRL)is established.The Adaptive Genetic Algorithm(AGA)is used to explore the action space of the Deep Deterministic Policy Gradient(DDPG),which effectively solves the problem of slow convergence of the DDPG algorithm in the high-dimensional action space.The simulation results show that the proposed algorithm,AGA-DDPG,saves approximately 61.8%,55%,21%,and 33%of the overall overhead compared to local MEC,random offloading,TD3,and DDPG,respectively.The proposed strategy is potentially important for improving real-time monitoring,big data analysis,and predictive maintenance of offshore wind farm operation and maintenance systems. 展开更多
关键词 Offshore wind MEC task offloading MADRL AGA-DDPG
下载PDF
Dynamic Offloading and Scheduling Strategy for Telematics Tasks Based on Latency Minimization
3
作者 Yu Zhou Yun Zhang +4 位作者 Guowei Li Hang Yang Wei Zhang Ting Lyu Yueqiang Xu 《Computers, Materials & Continua》 SCIE EI 2024年第8期1809-1829,共21页
In current research on task offloading and resource scheduling in vehicular networks,vehicles are commonly assumed to maintain constant speed or relatively stationary states,and the impact of speed variations on task ... In current research on task offloading and resource scheduling in vehicular networks,vehicles are commonly assumed to maintain constant speed or relatively stationary states,and the impact of speed variations on task offloading is often overlooked.It is frequently assumed that vehicles can be accurately modeled during actual motion processes.However,in vehicular dynamic environments,both the tasks generated by the vehicles and the vehicles’surroundings are constantly changing,making it difficult to achieve real-time modeling for actual dynamic vehicular network scenarios.Taking into account the actual dynamic vehicular scenarios,this paper considers the real-time non-uniform movement of vehicles and proposes a vehicular task dynamic offloading and scheduling algorithm for single-task multi-vehicle vehicular network scenarios,attempting to solve the dynamic decision-making problem in task offloading process.The optimization objective is to minimize the average task completion time,which is formulated as a multi-constrained non-linear programming problem.Due to the mobility of vehicles,a constraint model is applied in the decision-making process to dynamically determine whether the communication range is sufficient for task offloading and transmission.Finally,the proposed vehicular task dynamic offloading and scheduling algorithm based on muti-agent deep deterministic policy gradient(MADDPG)is applied to solve the optimal solution of the optimization problem.Simulation results show that the algorithm proposed in this paper is able to achieve lower latency task computation offloading.Meanwhile,the average task completion time of the proposed algorithm in this paper can be improved by 7.6%compared to the performance of the MADDPG scheme and 51.1%compared to the performance of deep deterministic policy gradient(DDPG). 展开更多
关键词 Component vehicular DYNAMIC task offloading resource scheduling
下载PDF
Task Offloading and Resource Allocation in NOMA-VEC:A Multi-Agent Deep Graph Reinforcement Learning Algorithm
4
作者 Hu Yonghui Jin Zuodong +1 位作者 Qi Peng Tao Dan 《China Communications》 SCIE CSCD 2024年第8期79-88,共10页
Vehicular edge computing(VEC)is emerging as a promising solution paradigm to meet the requirements of compute-intensive applications in internet of vehicle(IoV).Non-orthogonal multiple access(NOMA)has advantages in im... Vehicular edge computing(VEC)is emerging as a promising solution paradigm to meet the requirements of compute-intensive applications in internet of vehicle(IoV).Non-orthogonal multiple access(NOMA)has advantages in improving spectrum efficiency and dealing with bandwidth scarcity and cost.It is an encouraging progress combining VEC and NOMA.In this paper,we jointly optimize task offloading decision and resource allocation to maximize the service utility of the NOMA-VEC system.To solve the optimization problem,we propose a multiagent deep graph reinforcement learning algorithm.The algorithm extracts the topological features and relationship information between agents from the system state as observations,outputs task offloading decision and resource allocation simultaneously with local policy network,which is updated by a local learner.Simulation results demonstrate that the proposed method achieves a 1.52%∼5.80%improvement compared with the benchmark algorithms in system service utility. 展开更多
关键词 edge computing graph convolutional network reinforcement learning task offloading
下载PDF
Joint computation offloading and parallel scheduling to maximize delay-guarantee in cooperative MEC systems
5
作者 Mian Guo Mithun Mukherjee +3 位作者 Jaime Lloret Lei Li Quansheng Guan Fei Ji 《Digital Communications and Networks》 SCIE CSCD 2024年第3期693-705,共13页
The growing development of the Internet of Things(IoT)is accelerating the emergence and growth of new IoT services and applications,which will result in massive amounts of data being generated,transmitted and pro-cess... The growing development of the Internet of Things(IoT)is accelerating the emergence and growth of new IoT services and applications,which will result in massive amounts of data being generated,transmitted and pro-cessed in wireless communication networks.Mobile Edge Computing(MEC)is a desired paradigm to timely process the data from IoT for value maximization.In MEC,a number of computing-capable devices are deployed at the network edge near data sources to support edge computing,such that the long network transmission delay in cloud computing paradigm could be avoided.Since an edge device might not always have sufficient resources to process the massive amount of data,computation offloading is significantly important considering the coop-eration among edge devices.However,the dynamic traffic characteristics and heterogeneous computing capa-bilities of edge devices challenge the offloading.In addition,different scheduling schemes might provide different computation delays to the offloaded tasks.Thus,offloading in mobile nodes and scheduling in the MEC server are coupled to determine service delay.This paper seeks to guarantee low delay for computation intensive applica-tions by jointly optimizing the offloading and scheduling in such an MEC system.We propose a Delay-Greedy Computation Offloading(DGCO)algorithm to make offloading decisions for new tasks in distributed computing-enabled mobile devices.A Reinforcement Learning-based Parallel Scheduling(RLPS)algorithm is further designed to schedule offloaded tasks in the multi-core MEC server.With an offloading delay broadcast mechanism,the DGCO and RLPS cooperate to achieve the goal of delay-guarantee-ratio maximization.Finally,the simulation results show that our proposal can bound the end-to-end delay of various tasks.Even under slightly heavy task load,the delay-guarantee-ratio given by DGCO-RLPS can still approximate 95%,while that given by benchmarked algorithms is reduced to intolerable value.The simulation results are demonstrated the effective-ness of DGCO-RLPS for delay guarantee in MEC. 展开更多
关键词 Edge computing Computation offloading Parallel scheduling Mobile-edge cooperation Delay guarantee
下载PDF
IoT Task Offloading in Edge Computing Using Non-Cooperative Game Theory for Healthcare Systems
6
作者 Dinesh Mavaluru Chettupally Anil Carie +4 位作者 Ahmed I.Alutaibi Satish Anamalamudi Bayapa Reddy Narapureddy Murali Krishna Enduri Md Ezaz Ahmed 《Computer Modeling in Engineering & Sciences》 SCIE EI 2024年第5期1487-1503,共17页
In this paper,we present a comprehensive system model for Industrial Internet of Things(IIoT)networks empowered by Non-Orthogonal Multiple Access(NOMA)and Mobile Edge Computing(MEC)technologies.The network comprises e... In this paper,we present a comprehensive system model for Industrial Internet of Things(IIoT)networks empowered by Non-Orthogonal Multiple Access(NOMA)and Mobile Edge Computing(MEC)technologies.The network comprises essential components such as base stations,edge servers,and numerous IIoT devices characterized by limited energy and computing capacities.The central challenge addressed is the optimization of resource allocation and task distribution while adhering to stringent queueing delay constraints and minimizing overall energy consumption.The system operates in discrete time slots and employs a quasi-static approach,with a specific focus on the complexities of task partitioning and the management of constrained resources within the IIoT context.This study makes valuable contributions to the field by enhancing the understanding of resourceefficient management and task allocation,particularly relevant in real-time industrial applications.Experimental results indicate that our proposed algorithmsignificantly outperforms existing approaches,reducing queue backlog by 45.32% and 17.25% compared to SMRA and ACRA while achieving a 27.31% and 74.12% improvement in Qn O.Moreover,the algorithmeffectively balances complexity and network performance,as demonstratedwhen reducing the number of devices in each group(Ng)from 200 to 50,resulting in a 97.21% reduction in complexity with only a 7.35% increase in energy consumption.This research offers a practical solution for optimizing IIoT networks in real-time industrial settings. 展开更多
关键词 Internet of Things edge computing offloading NOMA
下载PDF
Policy Network-Based Dual-Agent Deep Reinforcement Learning for Multi-Resource Task Offloading in Multi-Access Edge Cloud Networks
7
作者 Feng Chuan Zhang Xu +2 位作者 Han Pengchao Ma Tianchun Gong Xiaoxue 《China Communications》 SCIE CSCD 2024年第4期53-73,共21页
The Multi-access Edge Cloud(MEC) networks extend cloud computing services and capabilities to the edge of the networks. By bringing computation and storage capabilities closer to end-users and connected devices, MEC n... The Multi-access Edge Cloud(MEC) networks extend cloud computing services and capabilities to the edge of the networks. By bringing computation and storage capabilities closer to end-users and connected devices, MEC networks can support a wide range of applications. MEC networks can also leverage various types of resources, including computation resources, network resources, radio resources,and location-based resources, to provide multidimensional resources for intelligent applications in 5/6G.However, tasks generated by users often consist of multiple subtasks that require different types of resources. It is a challenging problem to offload multiresource task requests to the edge cloud aiming at maximizing benefits due to the heterogeneity of resources provided by devices. To address this issue,we mathematically model the task requests with multiple subtasks. Then, the problem of task offloading of multi-resource task requests is proved to be NP-hard. Furthermore, we propose a novel Dual-Agent Deep Reinforcement Learning algorithm with Node First and Link features(NF_L_DA_DRL) based on the policy network, to optimize the benefits generated by offloading multi-resource task requests in MEC networks. Finally, simulation results show that the proposed algorithm can effectively improve the benefit of task offloading with higher resource utilization compared with baseline algorithms. 展开更多
关键词 benefit maximization deep reinforcement learning multi-access edge cloud task offloading
下载PDF
Online Learning-Based Offloading Decision and Resource Allocation in Mobile Edge Computing-Enabled Satellite-Terrestrial Networks
8
作者 Tong Minglei Li Song +1 位作者 Han Wanjiang Wang Xiaoxiang 《China Communications》 SCIE CSCD 2024年第3期230-246,共17页
Mobile edge computing(MEC)-enabled satellite-terrestrial networks(STNs)can provide Internet of Things(IoT)devices with global computing services.Sometimes,the network state information is uncertain or unknown.To deal ... Mobile edge computing(MEC)-enabled satellite-terrestrial networks(STNs)can provide Internet of Things(IoT)devices with global computing services.Sometimes,the network state information is uncertain or unknown.To deal with this situation,we investigate online learning-based offloading decision and resource allocation in MEC-enabled STNs in this paper.The problem of minimizing the average sum task completion delay of all IoT devices over all time periods is formulated.We decompose this optimization problem into a task offloading decision problem and a computing resource allocation problem.A joint optimization scheme of offloading decision and resource allocation is then proposed,which consists of a task offloading decision algorithm based on the devices cooperation aided upper confidence bound(UCB)algorithm and a computing resource allocation algorithm based on the Lagrange multiplier method.Simulation results validate that the proposed scheme performs better than other baseline schemes. 展开更多
关键词 computing resource allocation mobile edge computing satellite-terrestrial networks task offloading decision
下载PDF
Two-Stage IoT Computational Task Offloading Decision-Making in MEC with Request Holding and Dynamic Eviction
9
作者 Dayong Wang Kamalrulnizam Bin Abu Bakar Babangida Isyaku 《Computers, Materials & Continua》 SCIE EI 2024年第8期2065-2080,共16页
The rapid development of Internet of Things(IoT)technology has led to a significant increase in the computational task load of Terminal Devices(TDs).TDs reduce response latency and energy consumption with the support ... The rapid development of Internet of Things(IoT)technology has led to a significant increase in the computational task load of Terminal Devices(TDs).TDs reduce response latency and energy consumption with the support of task-offloading in Multi-access Edge Computing(MEC).However,existing task-offloading optimization methods typically assume that MEC’s computing resources are unlimited,and there is a lack of research on the optimization of task-offloading when MEC resources are exhausted.In addition,existing solutions only decide whether to accept the offloaded task request based on the single decision result of the current time slot,but lack support for multiple retry in subsequent time slots.It is resulting in TD missing potential offloading opportunities in the future.To fill this gap,we propose a Two-Stage Offloading Decision-making Framework(TSODF)with request holding and dynamic eviction.Long Short-Term Memory(LSTM)-based task-offloading request prediction and MEC resource release estimation are integrated to infer the probability of a request being accepted in the subsequent time slot.The framework learns optimized decision-making experiences continuously to increase the success rate of task offloading based on deep learning technology.Simulation results show that TSODF reduces total TD’s energy consumption and delay for task execution and improves task offloading rate and system resource utilization compared to the benchmark method. 展开更多
关键词 Decision making internet of things load prediction task offloading multi-access edge computing
下载PDF
Energy-Efficient Traffic Offloading for RSMA-Based Hybrid Satellite Terrestrial Networks with Deep Reinforcement Learning
10
作者 Qingmiao Zhang Lidong Zhu +1 位作者 Yanyan Chen Shan Jiang 《China Communications》 SCIE CSCD 2024年第2期49-58,共10页
As the demands of massive connections and vast coverage rapidly grow in the next wireless communication networks, rate splitting multiple access(RSMA) is considered to be the new promising access scheme since it can p... As the demands of massive connections and vast coverage rapidly grow in the next wireless communication networks, rate splitting multiple access(RSMA) is considered to be the new promising access scheme since it can provide higher efficiency with limited spectrum resources. In this paper, combining spectrum splitting with rate splitting, we propose to allocate resources with traffic offloading in hybrid satellite terrestrial networks. A novel deep reinforcement learning method is adopted to solve this challenging non-convex problem. However, the neverending learning process could prohibit its practical implementation. Therefore, we introduce the switch mechanism to avoid unnecessary learning. Additionally, the QoS constraint in the scheme can rule out unsuccessful transmission. The simulation results validates the energy efficiency performance and the convergence speed of the proposed algorithm. 展开更多
关键词 deep reinforcement learning energy efficiency hybrid satellite terrestrial networks rate splitting multiple access traffic offloading
下载PDF
Delay-optimal multi-satellite collaborative computation offloading supported by OISL in LEO satellite network
11
作者 ZHANG Tingting GUO Zijian +4 位作者 LI Bin FENG Yuan FU Qi HU Mingyu QU Yunbo 《Journal of Systems Engineering and Electronics》 SCIE CSCD 2024年第4期805-814,共10页
By deploying the ubiquitous and reliable coverage of low Earth orbit(LEO)satellite networks using optical inter satel-lite link(OISL),computation offloading services can be provided for any users without proximal serv... By deploying the ubiquitous and reliable coverage of low Earth orbit(LEO)satellite networks using optical inter satel-lite link(OISL),computation offloading services can be provided for any users without proximal servers,while the resource limita-tion of both computation and storage on satellites is the impor-tant factor affecting the maximum task completion time.In this paper,we study a delay-optimal multi-satellite collaborative computation offloading scheme that allows satellites to actively migrate tasks among themselves by employing the high-speed OISLs,such that tasks with long queuing delay will be served as quickly as possible by utilizing idle computation resources in the neighborhood.To satisfy the delay requirement of delay-sensi-tive task,we first propose a deadline-aware task scheduling scheme in which a priority model is constructed to sort the order of tasks being served based on its deadline,and then a delay-optimal collaborative offloading scheme is derived such that the tasks which cannot be completed locally can be migrated to other idle satellites.Simulation results demonstrate the effective-ness of our multi-satellite collaborative computation offloading strategy in reducing task complement time and improving resource utilization of the LEO satellite network. 展开更多
关键词 low Earth orbit(LEO)satellite network computation offloading task migration resource allocation
下载PDF
Outage Analysis of Optimal UAV Cooperation with IRS via Energy Harvesting Enhancement Assisted Computational Offloading
12
作者 Baofeng Ji Ying Wang +2 位作者 Weixing Wang Shahid Mumtaz Charalampos Tsimenidis 《Computer Modeling in Engineering & Sciences》 SCIE EI 2024年第2期1885-1905,共21页
The utilization of mobile edge computing(MEC)for unmanned aerial vehicle(UAV)communication presents a viable solution for achieving high reliability and low latency communication.This study explores the potential of e... The utilization of mobile edge computing(MEC)for unmanned aerial vehicle(UAV)communication presents a viable solution for achieving high reliability and low latency communication.This study explores the potential of employing intelligent reflective surfaces(IRS)andUAVs as relay nodes to efficiently offload user computing tasks to theMEC server system model.Specifically,the user node accesses the primary user spectrum,while adhering to the constraint of satisfying the primary user peak interference power.Furthermore,the UAV acquires energy without interrupting the primary user’s regular communication by employing two energy harvesting schemes,namely time switching(TS)and power splitting(PS).The selection of the optimal UAV is based on the maximization of the instantaneous signal-to-noise ratio.Subsequently,the analytical expression for the outage probability of the system in Rayleigh channels is derived and analyzed.The study investigates the impact of various system parameters,including the number of UAVs,peak interference power,TS,and PS factors,on the system’s outage performance through simulation.The proposed system is also compared to two conventional benchmark schemes:the optimal UAV link transmission and the IRS link transmission.The simulation results validate the theoretical derivation and demonstrate the superiority of the proposed scheme over the benchmark schemes. 展开更多
关键词 Unmanned aerial vehicle(UAV) intelligent reflective surface(IRS) energy harvesting computational offloading outage probability
下载PDF
UAV-assisted cooperative offloading energy efficiency system for mobile edge computing
13
作者 Xue-Yong Yu Wen-Jin Niu +1 位作者 Ye Zhu Hong-Bo Zhu 《Digital Communications and Networks》 SCIE CSCD 2024年第1期16-24,共9页
Reliable communication and intensive computing power cannot be provided effectively by temporary hot spots in disaster areas and complex terrain ground infrastructure.Mitigating this has greatly developed the applicat... Reliable communication and intensive computing power cannot be provided effectively by temporary hot spots in disaster areas and complex terrain ground infrastructure.Mitigating this has greatly developed the application and integration of UAV and Mobile Edge Computing(MEC)to the Internet of Things(loT).However,problems such as multi-user and huge data flow in large areas,which contradict the reality that a single UAV is constrained by limited computing power,still exist.Due to allowing UAV collaboration to accomplish complex tasks,cooperative task offloading between multiple UAVs must meet the interdependence of tasks and realize parallel processing,which reduces the computing power consumption and endurance pressure of terminals.Considering the computing requirements of the user terminal,delay constraint of a computing task,energy constraint,and safe distance of UAV,we constructed a UAV-Assisted cooperative offloading energy efficiency system for mobile edge computing to minimize user terminal energy consumption.However,the resulting optimization problem is originally nonconvex and thus,difficult to solve optimally.To tackle this problem,we developed an energy efficiency optimization algorithm using Block Coordinate Descent(BCD)that decomposes the problem into three convex subproblems.Furthermore,we jointly optimized the number of local computing tasks,number of computing offloaded tasks,trajectories of UAV,and offloading matching relationship between multi-UAVs and multiuser terminals.Simulation results show that the proposed approach is suitable for different channel conditions and significantly saves the user terminal energy consumption compared with other benchmark schemes. 展开更多
关键词 Computation offloading Internet of things(IoT) Mobile edge computing(MEC) Block coordinate descent(BCD)
下载PDF
Multi-Agent Deep Deterministic Policy Gradien-Based Task Offloading Resource Allocation Joint Offloading
14
作者 Xuan Zhang Xiaohui Hu 《Journal of Computer and Communications》 2024年第6期152-168,共17页
With the advancement of technology and the continuous innovation of applications, low-latency applications such as drones, online games and virtual reality are gradually becoming popular demands in modern society. How... With the advancement of technology and the continuous innovation of applications, low-latency applications such as drones, online games and virtual reality are gradually becoming popular demands in modern society. However, these applications pose a great challenge to the traditional centralized mobile cloud computing paradigm, and it is obvious that the traditional cloud computing model is already struggling to meet such demands. To address the shortcomings of cloud computing, mobile edge computing has emerged. Mobile edge computing provides users with computing and storage resources by offloading computing tasks to servers at the edge of the network. However, most existing work only considers single-objective performance optimization in terms of latency or energy consumption, but not balanced optimization in terms of latency and energy consumption. To reduce task latency and device energy consumption, the problem of joint optimization of computation offloading and resource allocation in multi-cell, multi-user, multi-server MEC environments is investigated. In this paper, a dynamic computation offloading algorithm based on Multi-Agent Deep Deterministic Policy Gradient (MADDPG) is proposed to obtain the optimal policy. The experimental results show that the algorithm proposed in this paper reduces the delay by 5 ms compared to PPO, 1.5 ms compared to DDPG and 10.7 ms compared to DQN, and reduces the energy consumption by 300 compared to PPO, 760 compared to DDPG and 380 compared to DQN. This fully proves that the algorithm proposed in this paper has excellent performance. 展开更多
关键词 Edge Computing Task offloading Deep Reinforcement Learning Resource Allocation MADDPG
下载PDF
A review of optimization methods for computation offloading in edge computing networks 被引量:6
15
作者 Kuanishbay Sadatdiynov Laizhong Cui +3 位作者 Lei Zhang Joshua Zhexue Huang Salman Salloum Mohammad Sultan Mahmud 《Digital Communications and Networks》 SCIE CSCD 2023年第2期450-461,共12页
Handling the massive amount of data generated by Smart Mobile Devices(SMDs)is a challenging computational problem.Edge Computing is an emerging computation paradigm that is employed to conquer this problem.It can brin... Handling the massive amount of data generated by Smart Mobile Devices(SMDs)is a challenging computational problem.Edge Computing is an emerging computation paradigm that is employed to conquer this problem.It can bring computation power closer to the end devices to reduce their computation latency and energy consumption.Therefore,this paradigm increases the computational ability of SMDs by collaboration with edge servers.This is achieved by computation offloading from the mobile devices to the edge nodes or servers.However,not all applications benefit from computation offloading,which is only suitable for certain types of tasks.Task properties,SMD capability,wireless channel state,and other factors must be counted when making computation offloading decisions.Hence,optimization methods are important tools in scheduling computation offloading tasks in Edge Computing networks.In this paper,we review six types of optimization methods-they are Lyapunov optimization,convex optimization,heuristic techniques,game theory,machine learning,and others.For each type,we focus on the objective functions,application areas,types of offloading methods,evaluation methods,as well as the time complexity of the proposed algorithms.We discuss a few research problems that are still open.Our purpose for this review is to provide a concise summary that can help new researchers get started with their computation offloading researches for Edge Computing networks. 展开更多
关键词 Edge computing Computation offloading Latency and energy consumption minimization
下载PDF
Joint offloading strategy based on quantum particle swarm optimization for MEC-enabled vehicular networks 被引量:3
16
作者 Wanneng Shu Yan Li 《Digital Communications and Networks》 SCIE CSCD 2023年第1期56-66,共11页
With the development of the mobile communication technology,a wide variety of envisioned intelligent transportation systems have emerged and put forward more stringent requirements for vehicular communications.Most of... With the development of the mobile communication technology,a wide variety of envisioned intelligent transportation systems have emerged and put forward more stringent requirements for vehicular communications.Most of computation-intensive and power-hungry applications result in a large amount of energy consumption and computation costs,which bring great challenges to the on-board system.It is necessary to exploit traffic offloading and scheduling in vehicular networks to ensure the Quality of Experience(QoE).In this paper,a joint offloading strategy based on quantum particle swarm optimization for the Mobile Edge Computing(MEC)enabled vehicular networks is presented.To minimize the delay cost and energy consumption,a task execution optimization model is formulated to assign the task to the available service nodes,which includes the service vehicles and the nearby Road Side Units(RSUs).For the task offloading process via Vehicle to Vehicle(V2V)communication,a vehicle selection algorithm is introduced to obtain an optimal offloading decision sequence.Next,an improved quantum particle swarm optimization algorithm for joint offloading is proposed to optimize the task delay and energy consumption.To maintain the diversity of the population,the crossover operator is introduced to exchange information among individuals.Besides,the crossover probability is defined to improve the search ability and convergence speed of the algorithm.Meanwhile,an adaptive shrinkage expansion factor is designed to improve the local search accuracy in the later iterations.Simulation results show that the proposed joint offloading strategy can effectively reduce the system overhead and the task completion delay under different system parameters. 展开更多
关键词 Computation offloading MEC-enabled vehicular networks Mobile edge computing Task scheduling
下载PDF
Task offloading mechanism based on federated reinforcement learning in mobile edge computing 被引量:2
17
作者 Jie Li Zhiping Yang +2 位作者 Xingwei Wang Yichao Xia Shijian Ni 《Digital Communications and Networks》 SCIE CSCD 2023年第2期492-504,共13页
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
关键词 Mobile edge computing Task offloading QoS Deep reinforcement learning Federated learning
下载PDF
Multi-Objective Optimization for NOMA-Based Mobile Edge Computing Offloading by Maximizing System Utility 被引量:1
18
作者 Hong Qin Haitao Du +2 位作者 Huahua Wang Li Su Yunfeng Peng 《China Communications》 SCIE CSCD 2023年第12期156-165,共10页
Mobile Edge Computing(MEC)is a technology for the fifth-generation(5G)wireless communications to enable User Equipment(UE)to offload tasks to servers deployed at the edge of network.However,taking both delay and energ... Mobile Edge Computing(MEC)is a technology for the fifth-generation(5G)wireless communications to enable User Equipment(UE)to offload tasks to servers deployed at the edge of network.However,taking both delay and energy consumption into consideration in the 5G MEC system is usually complex and contradictory.Non-orthogonal multiple access(NOMA)enable more UEs to offload their computing tasks to MEC servers using the same spectrum resources to enhance the spectrum efficiency for 5G,which makes the problem even more complex in the NOMA-MEC system.In this work,a system utility maximization model is present to NOMA-MEC system,and two optimization algorithms based on Newton method and greedy algorithm respectively are proposed to jointly optimize the computing resource allocation,SIC order,transmission time slot allocation,which can easily achieve a better trade-off between the delay and energy consumption.The simulation results prove that the proposed method is effective for NOMA-MEC systems. 展开更多
关键词 computation offloading mobile edge computing non-orthogonal multiple access resource allocation
下载PDF
Task Offloading and Resource Allocation for Edge-Enabled Mobile Learning 被引量:1
19
作者 Ziyan Yang Shaochun Zhong 《China Communications》 SCIE CSCD 2023年第4期326-339,共14页
Mobile learning has evolved into a new format of education based on communication and computer technology that is favored by an increasing number of learning users thanks to the development of wireless communication n... Mobile learning has evolved into a new format of education based on communication and computer technology that is favored by an increasing number of learning users thanks to the development of wireless communication networks,mobile edge computing,artificial intelligence,and mobile devices.However,due to the constrained data processing capacity of mobile devices,efficient and effective interactive mobile learning is a challenge.Therefore,for mobile learning,we propose a"Cloud,Edge and End"fusion system architecture.Through task offloading and resource allocation for edge-enabled mobile learning to reduce the time and energy consumption of user equipment.Then,we present the proposed solutions that uses the minimum cost maximum flow(MCMF)algorithm to deal with the offloading problem and the deep Q network(DQN)algorithm to deal with the resource allocation problem respectively.Finally,the performance evaluation shows that the proposed offloading and resource allocation scheme can improve system performance,save energy,and satisfy the needs of learning users. 展开更多
关键词 mobile learning mobile edge computing(MEC) system construction offloading resource allocation
下载PDF
Adaptive delay-energy balanced partial offloading strategy in Mobile Edge Computing networks 被引量:1
20
作者 Shumei Liu Yao Yu +3 位作者 Lei Guo Phee Lep Yeoh Branka Vucetic Yonghui Li 《Digital Communications and Networks》 SCIE CSCD 2023年第6期1310-1318,共9页
Mobile Edge Computing(MEC)-based computation offloading is a promising application paradigm for serving large numbers of users with various delay and energy requirements.In this paper,we propose a flexible MECbased re... Mobile Edge Computing(MEC)-based computation offloading is a promising application paradigm for serving large numbers of users with various delay and energy requirements.In this paper,we propose a flexible MECbased requirement-adaptive partial offloading model to accommodate each user's specific preference regarding delay and energy consumption.To address the dimensional differences between time and energy,we introduce two normalized parameters and then derive the computational overhead of processing tasks.Different from existing works,this paper considers practical variations in the user request patterns,and exploits a flexible partial offloading mode to minimize computation overheads subject to tolerable delay,task workload and power constraints.Since the resulting problem is non-convex,we decouple it into two convex subproblems and present an iterative algorithm to obtain a feasible offloading solution.Numerical experiments show that our proposed scheme achieves a significant improvement in computation overheads compared with existing schemes. 展开更多
关键词 Mobile edge computing(MEC) DELAY Energy consumption Dynamic balance Partial computation offloading
下载PDF
上一页 1 2 73 下一页 到第
使用帮助 返回顶部