|
|
|
题名
|
作者
|
年代
|
出处
|
被引量
|
| 1 | Deep reinforcement learning-based joint task offloading and bandwidth allocation for multi-user mobile edge computing显示文摘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。 | Liang Huang Xu Feng Cheng Zhang Liping Qian Yuan Wu | 2019 | Digital Communications and Networks2019，5，1： | 25 |
| 2 | Energy-Efficient Computation Offloading and Resource Allocation in Fog Computing for Internet of Everything显示文摘With the dawning of the Internet of Everything(IoE) era, more and more novel applications are being deployed. However, resource constrained devices cannot fulfill the resource-requirements of these applications. This paper investigates the computation offloading problem of the coexistence and synergy between fog computing and cloud computing in IoE by jointly optimizing the offloading decisions, the allocation of computation resource and transmit power. Specifically, we propose an energy-efficient computation offloading and resource allocation(ECORA) scheme to minimize the system cost. The simulation results verify the proposed scheme can effectively decrease the system cost by up to 50% compared with the existing schemes, especially for the scenario that the computation resource of fog computing is relatively small or the number of devices increases. | Qiuping Li Junhui Zhao Yi Gong Qingmiao Zhang | 2019 | China Communications2019，16，3： | 19 |
| 3 | A Deep Learning Based Energy-Efficient Computational Offloading Method in Internet of Vehicles显示文摘With the emergence of advanced vehicular applications, the challenge of satisfying computational and communication demands of vehicles has become increasingly prominent. Fog computing is a potential solution to improve advanced vehicular services by enabling computational offloading at the edge of network. In this paper, we propose a fog-cloud computational offloading algorithm in Internet of Vehicles(IoV) to both minimize the power consumption of vehicles and that of the computational facilities. First, we establish the system model, and then formulate the offloading problem as an optimization problem, which is NP-hard. After that, we propose a heuristic algorithm to solve the offloading problem gradually. Specifically, we design a predictive combination transmission mode for vehicles, and establish a deep learning model for computational facilities to obtain the optimal workload allocation. Simulation results demonstrate the superiority of our algorithm in energy efficiency and network latency. | Xiaojie Wang Xiang Wei Lei Wang | 2019 | China Communications2019，16，3： | 15 |
| 4 | Efficient Task Completion for Parallel Offloading in Vehicular Fog Computing显示文摘In this paper,we investigate vehicular fog computing system and develop an effective parallel offloading scheme.The service time,that addresses task offloading delay,task decomposition and handover cost,is adopted as the metric of offloading performance.We propose an available resource-aware based parallel offloading scheme,which decides target fog nodes by RSU for computation offloading jointly considering effect of vehicles mobility and time-varying computation capability.Based on Hidden Markov model and Markov chain theories,proposed scheme effectively handles the imperfect system state information for fog nodes selection by jointly achieving mobility awareness and computation perception.Simulation results are presented to corroborate the theoretical analysis and validate the effectiveness of the proposed algorithm. | Jindou Xie Yunjian Jia Zhengchuan Chen Zhaojun Nan Liang Liang | 2019 | China Communications2019，16，11： | 5 |
| 5 | An Offloading Scheme Leveraging on Neighboring Node Resources for Edge Computing over Fiber-Wireless (FiWi) Access Networks显示文摘The computation resources at a single node in Edge Computing(EC)are commonly limited,which cannot execute large scale computation tasks.To face the challenge,an Offloading scheme leveraging on NEighboring node Resources(ONER)for EC over Fiber-Wireless(FiWi)access networks is proposed in this paper.In the ONER scheme,the FiWi network connects edge computing nodes with fiber and converges wireless and fiber connections seamlessly,so that it can support the offloading transmission with low delay and wide bandwidth.Based on the ONER scheme supported by FiWi networks,computation tasks can be offloaded to edge computing nodes in a wider range of area without increasing wireless hops(e.g.,just one wireless hop),which achieves low delay.Additionally,an efficient Computation Resource Scheduling(CRS)algorithm based on the ONER scheme is also proposed to make offloading decision.The results show that more offloading requests can be satisfied and the average completion time of computation tasks decreases significantly with the ONER scheme and the CRS algorithm.Therefore,the ONER scheme and the CRS algorithm can schedule computation resources at neighboring edge computing nodes for offloading to meet the challenge of large scale computation tasks. | Wei Chang Yihong Hu Guochu Shou Yaqiong Liu Zhigang Guo | 2019 | China Communications2019，16，11： | 3 |
| 6 | Software defined industrial network architecture for edge computing offloading显示文摘The integration of the Internet and the traditional manufacturing industry makes the industrial Internet of things(IIoT) as a popular research topic. However, traditional industrial networks continue to face challenges of resource management and limited raw data storage and computation capacity. A novel software defined industrial network(SDIN) architecture was proposed to address the existing drawbacks in IIoT such as resource utilization, data processing and storage, and system compatibility. The architecture is developed based on the software defined network(SDN) architecture, combining hierarchical cloud and fog computing and content-aware caching technologies. Based on the SDIN architecture, two types of edge computing strategies in industrial applications are discussed. Different scenarios and service requirements are considered. The simulation results confirm that the SDIN architecture is feasible and effective in the application of edge computing offloading. | Xu Fangmin Ye Huanyu Cui Shaohua Zhao Chenglin Yao Haipeng | 2019 | The Journal of China Universities of Posts and Telecommunications2019，26，1： | 2 |
| 7 | Joint time delay and energy optimization with intelligent overclocking in edge computing显示文摘With the rapid growth of user equipment(UE),the amount of data transmitted over networks has become enormous,exerting immense pressure on backbone networks and central cloud infrastructures.Simultaneously,corresponding applications requiring high energy consumption and low latency have multiplied the requirements for UE.Mobile edge computing(MEC)has been proposed to support the offloading of UE tasks to edge clouds for execution.The implementation of MEC requires fast data transmission between UE and edge servers,and the emerging 5G network appears to render this technology possible.In this paper,considering a large number of UE,a fixed MEC server,and an advanced intelligent network,we suggest an intelligent overclocking mechanism for the MEC server that operates for an intelligently calculated period to allow it to leverage more computing power without introducing additional hardware resources for a certain period of time.We jointly manage task offloading,server resource allocation,and overclocking to minimize the system-wide computation overhead and other risks.The proposed optimization problem is a mixedinteger nonlinear programming problem that is divided into three subproblems:offloading decision,resource allocation,and overclocking decision.We solve these subproblems using non-convex techniques and provide an iterative algorithm to obtain a heuristic solution for the original problem.Finally,simulation results show that the overclocked MEC server has lower system-wide computation overhead,faster task processing,and more offloaded UE as compared with the case without overclocking. | Kehao WANG Zhenhua XIONG Lin CHEN Pan ZHOU Hyundong SHIN | 2020 | Science China(Information Sciences)2020，63，4： | 2 |
| 8 | Enabling intelligence in fog computing to achieve energy and latency reduction显示文摘Fog computing is an emerging architecture intended for alleviating the network burdens at the cloud and the core network by moving resource-intensive functionalities such as computation, communication, storage, and analytics closer to the End Users (EUs). In order to address the issues of energy efficiency and latency requirements for the time-critical Internet-of-Things (IoT) applications, fog computing systems could apply intelligence features in their operations to take advantage of the readily available data and computing resources. In this paper, we propose an approach that involves device-driven and human-driven intelligence as key enablers to reduce energy consumption and latency in fog computing via two case studies. The first one makes use of the machine learning to detect user behaviors and perform adaptive low-latency Medium Access Control (MAC)-layer scheduling among sensor devices. In the second case study on task offloading, we design an algorithm for an intelligent EU device to select its offloading decision in the presence of multiple fog nodes nearby, at the same time, minimize its own energy and latency objectives. Our results show a huge but untapped potential of intelligence in tackling the challenges of fog computing。 | Quang Duy La Mao V. Ngo Thinh Quang Dinh Tony Q.S. Quek Hyundong Shin | 2019 | Digital Communications and Networks2019，5，1： | 2 |
| 9 | A mobile edge computing-based applications execution framework for Internet of Vehicles显示文摘Mobile edge computing(MEC)is a promising technology for the Internet of Vehicles,especially in terms of application offloading and resource allocation.Most existing offloading schemes are sub-optimal,since these offloading strategies consider an application as a whole.In comparison,in this paper we propose an application-centric framework and build a finer-grained offloading scheme based on application partitioning.In our framework,each application is modelled as a directed acyclic graph,where each node represents a subtask and each edge represents the data flow dependency between a pair of subtasks.Both vehicles and MEC server within the communication range can be used as candidate offloading nodes.Then,the offloading involves assigning these computing nodes to subtasks.In addition,the proposed offloading scheme deal with the delay constraint of each subtask.The experimental evaluation show that,compared to existing non-partitioning offloading schemes,this proposed one effectively improves the performance of the application in terms of execution time and throughput. | Libing WU Rui ZHANG Qingan LI Chao MA Xiaochuan SHI | 2022 | Frontiers of Computer Science2022，16，5： | 1 |
| 10 | On the System Performance of Mobile Edge Computing in an Uplink NOMA WSN With a Multiantenna Access Point Over Nakagami-m Fading显示文摘In this paper,we study the system performance of mobile edge computing(MEC)wireless sensor networks(WSNs)using a multiantenna access point(AP)and two sensor clusters based on uplink nonorthogonal multiple access(NOMA).Due to limited computation and energy resources,the cluster heads(CHs)offload their tasks to a multiantenna AP over Nakagami-m fading.We proposed a combination protocol for NOMA-MEC-WSNs in which the AP selects either selection combining(SC)or maximal ratio combining(MRC)and each cluster selects a CH to participate in the communication process by employing the sensor node(SN)selection.We derive the closed-form exact expressions of the successful computation probability(SCP)to evaluate the system performance with the latency and energy consumption constraints of the considered WSN.Numerical results are provided to gain insight into the system performance in terms of the SCP based on system parameters such as the number of AP antennas,number of SNs in each cluster,task length,working frequency,offloading ratio,and transmit power allocation.Furthermore,to determine the optimal resource parameters,i.e.,the offloading ratio,power allocation of the two CHs,and MEC AP resources,we proposed two algorithms to achieve the best system performance.Our approach reveals that the optimal parameters with different schemes significantly improve SCP compared to other similar studies.We use Monte Carlo simulations to confirm the validity of our analysis. | Van-Truong Truong Van Nhan Vo Dac-Binh Ha Chakchai So-In | 2022 | IEEE/CAA Journal of Automatica Sinica2022，9，4： | 1 |
| 11 | Assessment of Fatigue Strength of An Offshore Floating Production and Storage Unit显示文摘The procedure of assessment of structural fatigue strength of an offshore floating production and storage and offloadingunit(FPSO) in this paper. The emphasis is placed on the long-term prediction of wave induced loading, the refined finite el-ement model for hot spot stress calculation, the combination of stress components, and fatigue damage assessment based onS-N curve. | LIU Jiancheng(刘建成) GU Yongning(顾永宁) | 2002 | China Ocean Engineering2002，17，1： | 1 |
| 12 | An adaptive offloading framework for Android applications in mobile edge computing显示文摘Mobile edge computing(MEC) provides a fresh opportunity to significantly reduce the latency and battery energy consumption of mobile applications. It does so by enabling the offloading of parts of the applications on mobile edges, which are located in close proximity to the mobile devices. Owing to the geographical distribution of mobile edges and the mobility of mobile devices, the runtime environment of MEC is highly complex and dynamic. As a result, it is challenging for application developers to support computation offloading in MEC compared with the traditional approach in mobile cloud computing, where applications use only the cloud for offloading. On the one hand, developers have to make the offloading adaptive to the changing environment, where the offloading should dynamically occur among available computation nodes.On the other hand, developers have to effectively determine the offloading scheme each time the environment changes. To address these challenges, this paper proposes an adaptive framework that supports mobile applications with offloading capabilities in MEC. First, based on our previous study(DPartner), a new design pattern is proposed to enable an application to be dynamically offloaded among mobile devices, mobile edges,and the cloud. Second, an estimation model is designed to automatically determine the offloading scheme.In this model, different parts of the application may be executed on different computation nodes. Finally, an adaptive offloading framework is implemented to support the design pattern and the estimation model. We evaluate our framework on two real-world applications. The results demonstrate that our approach can aid in reducing the response time by 8%–50% and energy consumption by 9%–51% for computation-intensive applications. | Xing CHEN Shihong CHEN Yun MA Bichun LIU Ying ZHANG Gang HUANG | 2019 | Science China(Information Sciences)2019，62，8： | 1 |
| 13 | Intelligent Network Selection for Data Offloading in 5G Multi-Radio Heterogeneous Networks显示文摘In next generation networks, multiradio networks are emerging in order to deal with exponential data traffic increasing. Integrated Femto-WiFi(IFW) small cells have been introduced by 3GPP to offload data from cellular networks recently. These IFW cells are multi-mode capable(i.e., both licensed bands via cellular interface and unlicensed bands via WiFi interface). Therefore how to offload data effectively has become one of the most significant discussions in 5G Multi-Radio Heterogeneous Network. So far, most researches mainly focus on the generality of UEs, few attention has been paid to UEs' individual requirements. Considering UE's preference vary from individual to individual, in this paper, we present an UE preference-aware network selection scheme for mobile data offloading. It intelligently supports the distribution of heterogeneous classes of services, considers different types of UEs and delay-tolerant flows, and handles the mobility of UEs. The simulation results show the superiority of the proposed algorithm in user fairness, enhanced capacity and energy saving maximization. | WU Jin LIU Jing HUANG Zhangpeng DU Chen ZHAO Hui BAI Yu | 2015 | China Communications2015，12，S1： | 1 |
| 14 | IoT Task Offloading in Edge Computing Using Non-Cooperative Game Theory for Healthcare Systems显示文摘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. | Dinesh Mavaluru Chettupally Anil Carie Ahmed I.Alutaibi Satish Anamalamudi Bayapa Reddy Narapureddy Murali Krishna Enduri Md Ezaz Ahmed | 2024 | Computer Modeling in Engineering & Sciences2024，139，5： | 0 |
| 15 | Stochastic programming based multi-arm bandit offloading strategy for internet of things显示文摘In order to solve the high latency of traditional cloud computing and the processing capacity limitation of Internet of Things(IoT)users,Multi-access Edge Computing(MEC)migrates computing and storage capabilities from the remote data center to the edge of network,providing users with computation services quickly and directly.In this paper,we investigate the impact of the randomness caused by the movement of the IoT user on decision-making for offloading,where the connection between the IoT user and the MEC servers is uncertain.This uncertainty would be the main obstacle to assign the task accurately.Consequently,if the assigned task cannot match well with the real connection time,a migration(connection time is not enough to process)would be caused.In order to address the impact of this uncertainty,we formulate the offloading decision as an optimization problem considering the transmission,computation and migration.With the help of Stochastic Programming(SP),we use the posteriori recourse to compensate for inaccurate predictions.Meanwhile,in heterogeneous networks,considering multiple candidate MEC servers could be selected simultaneously due to overlapping,we also introduce the Multi-Arm Bandit(MAB)theory for MEC selection.The extensive simulations validate the improvement and effectiveness of the proposed SP-based Multi-arm bandit Method(SMM)for offloading in terms of reward,cost,energy consumption and delay.The results showthat SMMcan achieve about 20%improvement compared with the traditional offloading method that does not consider the randomness,and it also outperforms the existing SP/MAB based method for offloading. | Bin Cao Tingyong Wu Xiang Bai | 2023 | Digital Communications and Networks2023，9，5： | 0 |
| 16 | Research on adaptive dual task offloading decision algorithm for parking space recommendation service显示文摘In order to improve the efficiency of tasks processing and reduce the energy consumption of new energy vehicle(NEV), an adaptive dual task offloading decision-making scheme for Internet of vehicles is proposed based on information-assisted service of road side units(RSUs) and task offloading theory. Taking the roadside parking space recommendation service as the specific application Scenario, the task offloading model is built and a hierarchical self-organizing network model is constructed, which utilizes the computing power sharing among nodes, RSUs and mobile edge computing(MEC) servers. The task scheduling is performed through the adaptive task offloading decision algorithm, which helps to realize the available parking space recommendation service which is energy-saving and environmental-friendly. Compared with these traditional task offloading decisions, the proposed scheme takes less time and less energy in the whole process of tasks. Simulation results testified the effectiveness of the proposed scheme. | Peng Weiping Su Zhe Song Cheng Jia Zongpu | 2019 | The Journal of China Universities of Posts and Telecommunications2019，26，6： | 0 |
| 17 | Joint partial computation offloading and resource allocation in MEC-enable networks显示文摘The sudden surge of various applications poses great challenges to the computation capability of mobile devices.To address this issue,computation offloading to multi-access edge computing(MEC)was proposed as a promising paradigm.This paper studies partial computation offloading scenario by considering time delay and energy consumption,where the task can be splitted into several blocks and computed both in local devices and MEC,respectively.Since the formulated problem is a nonconvex problem,this paper proposes an ant colony-based algorithm to achieve the suboptimal solution.Specifically,the proposed method first establish a multi-user one-MEC scenario,in which user devices are able to offload some part of the task to MEC server.Then,it develops an ant colony-based algorithm to decide the offloading parts and allocation strategy of MEC resources to minimize system cost.Finally,simulation results show the effectiveness of the proposed algorithm in terms of system cost and demonstrate that it outperforms other existing methods. | Wu Hongxin Lin Zhijian Chen Pingping Chen Feng | 2023 | The Journal of China Universities of Posts and Telecommunications2023，30，1： | 0 |
| 18 | Computation Offloading and Scheduling in Edge-Fog Cloud Computing显示文摘Resource allocation and task scheduling in the Cloud environment faces many challenges,such as time delay,energy consumption,and security.Also,executing computation tasks of mobile applications on mobile devices(MDs)requires a lot of resources,so they can offload to the Cloud.But Cloud is far from MDs and has challenges as high delay and power consumption.Edge computing with processing near the Internet of Things(IoT)devices have been able to reduce the delay to some extent,but the problem is distancing itself from the Cloud.The fog computing(FC),with the placement of sensors and Cloud,increase the speed and reduce the energy consumption.Thus,FC is suitable for IoT applications.In this article,we review the resource allocation and task scheduling methods in Cloud,Edge and Fog environments,such as traditional,heuristic,and meta-heuristics.We also categorize the researches related to task offloading in Mobile Cloud Computing(MCC),Mobile Edge Computing(MEC),and Mobile Fog Computing(MFC).Our categorization criteria include the issue,proposed strategy,objectives,framework,and test environment. | Dadmehr Rahbari Mohsen Nickray | 2019 | Journal of Electronic & Information Systems2019，1，1： | 0 |
| 19 | MEACC: an energy-efficient framework for smart devices using cloud computing systems显示文摘Rapidly increasing capacities,decreasing costs,and improvements in computational power,storage,and communication technologies have led to the development of many applications that carry increasingly large amounts of traffic on the global networking infrastructure.Smart devices lead to emerging technologies and play a vital role in rapid evolution.Smart devices have become a primary 24/7 need in today’s information technology world and include a wide range of supporting processing-intensive applications.Extensive use of many applications on smart devices results in increasing complexity of mobile software applications and consumption of resources at a massive level,including smart device battery power,processor,and RAM,and hinders their normal operation.Appropriate resource utilization and energy efficiency are fundamental considerations for smart devices because limited resources are sporadic and make it more difficult for users to complete their tasks.In this study we propose the model of mobile energy augmentation using cloud computing(MEACC),a new framework to address the challenges of massive power consumption and inefficient resource utilization in smart devices.MEACC efficiently filters the applications to be executed on a smart device or offloaded to the cloud.Moreover,MEACC efficiently calculates the total execution cost on both the mobile and cloud sides including communication costs for any application to be offloaded.In addition,resources are monitored before making the decision to offload the application.MEACC is a promising model for load balancing and power consumption reduction in emerging mobile computing environments. | Khalid ALSUBHI Zuhaib IMTIAZ Ayesha RAANA MUsman ASHRAF Babur HAYAT | 2020 | Frontiers of Information Technology & Electronic Engineering2020，21，6： | 0 |
| 20 | Computation Offloading Algorithms in Mobile Edge Computing System: A Survey显示文摘With the rapid development of the internet of things (IoT), the number of devices that can connect to the network has exploded. More computation intensive task appear on mobile terminals, and mobile edge computing has emerged. Computation offloading technology is a key technology in mobile edge computing. This survey reviews the state of the art of computation offloading algorithms. It was classified into three categories: computation offloading algorithms in MEC system with single user, computation offloading algorithms in MEC system with multiple users, computation offloading algorithms in MEC system with enhanced MEC server. For each category of algorithms, the advantages and disadvantages were elaborated, some challenges and unsolved problems were pointed out, and the research prospects were forecasted. | Zhenyue Chen Siyao Cheng | 2019 | 国际计算机前沿大会会议论文集2019，，1： | 0 |
您所在的机构暂未开通此部分的功能，请联系管理员