检索结果-内蒙古大学图书馆

9th International Symposium on Computer and Information Processing Technology, ISCIPT 2024

作者： Li, Shaonan Xie, Yongqiang Zhang, Jie Zhou, Jian Wang, Yangyang Miao, Huijie Academy of Military Sciences Institute of Systems Engineering Beijing100141 China

ISBN: (纸本)9798350388404

Cloud-edge collaborative computation offloading aims to optimize resource utilization and enhance the quality of experience (QoE) for users. Despite this, existing studies in the field of computation offloading primarily focus on the task level and do not delve deeper into more granular aspects. Traditional computing offloading strategies primarily aim to select an optimal node for the task to be deployed. Their studies can not use the advantage of cloud-edge collaboration but also cause much waste of resources. In this manuscript, we introduce an algorithm for function computation offloading (FCO) that employs a Greedy and Adaptive heuristic (GAF), which tries to decompose the task into several functions under the Space-Integrated-Ground cloud-edge collaborative architecture, perform computation offloading on the functions, and optimize the delay. To benchmark the performance of GAF, We have established an integer linear programming (ILP) model that precisely captures the essence of the scenario. We have carried out comprehensive testing to substantiate the efficacy of the proposed algorithm. Our findings demonstrate that the performance of GAF is better than that of traditional task-level computational offloading algorithms in most cases. In a few cases, the performance is the same as that of traditional methods. The outcomes of this study offer insightful guidance for devising offloading strategies for function computation within the Space-Integrated-Ground cloud-edge framework. © 2024 IEEE.

关键词： computation offloading

来源：评论

学校读者我要写书评

暂无评论

Energy-Efficient computation offloading for UAV-Assisted MEC: A Two-Stage Optimization Scheme

引用

ACM TRANSACTIONS ON INTERNET TECHNOLOGY 2022年第1期22卷 1-23页

作者： Lin, Weiwei Huang, Tiansheng Li, Xin Shi, Fang Wang, Xiumin Hsu, Ching-Hsien South China Univ Technol Sch Comp Sci & Engn Guangzhou Peoples R China Asia Univ Dept Comp Sci & Informat Engn Taichung Taiwan China Med Univ China Med Univ Hosp Dept Med Res Taichung Taiwan

In addition to the stationary mobile edge computing (MEC) servers, a few MEC surrogates that possess a certain mobility and computation capacity, e.g., flying unmanned aerial vehicles (UAVs) and private vehicles, have risen as powerful counterparts for service provision. In this article, we design a two-stage online scheduling scheme, targeting computation offloading in a UAV-assisted MEC system. On our stage-one formulation, an online scheduling framework is proposed for dynamic adjustment of mobile users' CPU frequency and their transmission power, aiming at producing a socially beneficial solution to users. But the major impediment during our investigation lies in that users might not unconditionally follow the scheduling decision released by servers as a result of their individual rationality. In this regard, we formulate each step of online scheduling on stage one into a non-cooperative game with potential competition over the limited radio resource. As a solution, a centralized online scheduling algorithm, called ONCCO, is proposed, which significantly promotes social benefit on the basis of the users' individual rationality. On our stage-two formulation, we are working towards the optimization of UAV computation resource provision, aiming at minimizing the energy consumption of UAVs during such a process, and correspondingly, another algorithm, calledWS-UAV, is given as a solution. Finally, extensive experiments via numerical simulation are conducted for an evaluation purpose, by which we show that our proposed algorithms achieve satisfying performance enhancement in terms of energy conservation and sustainable service provision.

关键词： computation offloading mobile edge computing nash equilibrium non-cooperative game unmanned aerial vehicles

来源：评论

学校读者我要写书评

暂无评论

Modeling on Energy-Efficiency computation offloading Using Probabilistic Action Generating

引用

IEEE INTERNET OF THINGS JOURNAL 2022年第20期9卷 20681-20692页

作者： Wang, Cong Lu, Weicheng Peng, Sancheng Qu, Youyang Wang, Guojun Yu, Shui Northeastern Univ Qinhuangdao Sch Comp & Commun Engn Qinhuangdao 066004 Hebei Peoples R China Guangdong Univ Foreign Studies Lab Language Engn & Comp Guangzhou 510006 Peoples R China CSIRO Data61 Eveleigh NSW 2015 Australia Guangzhou Univ Sch Comp Sci & Cyber Engn Guangzhou 510006 Peoples R China Univ Technol Sydney Sch Comp Sci Ultimo NSW 2007 Australia

Wireless-powered mobile-edge computing (MEC) emerges as a crucial component in the Internet of Things (IoTs). It can cope with the fundamental performance limitations of low-power networks, such as wireless sensor networks or mobile networks. Although computation offloading and resource allocation in MEC have been studied with different optimization objectives, performance optimization in larger-scale systems still needs to be further improved. More importantly, energy efficiency is also a key issue as well as computation offloading and resource allocation for wireless-powered MEC. In this article, we investigate the joint optimization of computation rate and energy consumption under limited resources, and propose an online offloading model to search for the asymptotically optimal offloading and resource allocation strategy. First, the joint optimization problem is modeled as a mixed integer programming (MIP) problem. Second, a deep reinforcement learning (DRL)-based method, energy efficiency computation offloading using probabilistic action generating (ECOPG), is designed to generate the joint optimization policy for computation offloading and resource allocation. Finally, to avoid the curse of dimensionality in large network scales, an action exploration mechanism based on probability is introduced to accelerate the convergence rate by targeted sampling and dynamic experience replay. The experimental results demonstrate that the proposed methods significantly outperform other DRL-based methods in energy consumption, and gain better computation rate and execution efficiency at the same time. With the expansion of the network scale, the improvements become more apparent.

关键词： Task analysis Wireless communication Energy consumption Resource management computational efficiency Optimization Internet of Things computation offloading deep reinforcement learning (DRL) energy efficiency mobile-edge computing (MEC)

来源：评论

学校读者我要写书评

暂无评论

Intelligent computation offloading for MEC-Based Cooperative Vehicle Infrastructure System: A Deep Reinforcement Learning Approach

引用

IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2022年第7期71卷 7665-7679页

作者： Yang, Heng Wei, Zhiqing Feng, Zhiyong Chen, Xu Li, Yiheng Zhang, Ping Beijing Univ Posts & Telecommun Key Lab Universal Wireless Commun Minist Educ Beijing 100876 Peoples R China Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China

In the cooperative vehicle infrastructure system, the road side unit (RSU) equipped with a mobile edge computing (MEC) server and sensors could provide vehicle infrastructure cooperation services for vehicles, such as optimization and cooperative driving, enhanced visibility, and so on. In view of this, the MEC server needs to fuse the sensing information from sensors on the vehicles and RSU, respectively. In the case of bad channel conditions, uploading the raw sensing information from the vehicles results in high uplink transmission latency. To deal with it, the vehicles can process the information locally and just deliver the results to the RSU. However, due to the limited computing resources on the vehicles, the processing accuracy of the raw information on the vehicles is lower than that on the MEC server. Besides, processing locally leads to higher vehicle energy consumption. Thus, in this paper, we aim to jointly optimize execution latency, processing accuracy, and energy consumption of the MEC-based cooperative vehicle infrastructure system. Firstly, we design the terminal machine learning task model and the edge machine learning task model on the vehicle side and RSU side, respectively. Then, we formulate a long-term multi-objective optimization problem. Owing to the stochastic traffic and time-varying communication conditions, we reformulate it as a Markov decision process and propose a two-stage deep reinforcement learning-based offloading and resource allocation (TDORA) strategy to determine the task offloading and the transmit power of each vehicle. Simulation results demonstrate the efficacy of the proposed strategy.

关键词： Task analysis Sensors Servers Optimization computational modeling Resource management Energy consumption computation offloading cooperative vehicle infrastructure system deep reinforcement learning joint optimization mobile edge computing

来源：评论

学校读者我要写书评

暂无评论

SDN-Assisted Mobile Edge Computing for Collaborative computation offloading in Industrial Internet of Things

引用

IEEE INTERNET OF THINGS JOURNAL 2022年第23期9卷 24253-24263页

作者： Tang, Chaogang Zhu, Chunsheng Zhang, Ning Guizani, Mohsen Rodrigues, Joel J. P. C. China Univ Min & Technol Sch Comp Sci & Technol Xuzhou 221116 Jiangsu Peoples R China China Univ Min & Technol Mine Digitizat Engn Res Ctr Minist Educ Xuzhou 221116 Jiangsu Peoples R China Shenzhen Technol Univ Coll Big Data & Internet Shenzhen 518118 Peoples R China Univ Windsor Dept Elect & Comp Engn Windsor ON N9B 3P4 Canada Mohamed Bin Zayed Univ Artificial Intelligence Machine Learning Dept Abu Dhabi U Arab Emirates China Univ Petr East China Coll Comp Sci & Technol Qingdao 266555 Peoples R China Inst Telecomunicacoes P-3810193 Aveiro Portugal

Mobile edge computing (MEC) can provision augmented computational capacity in proximity so as to better support Industrial Internet of Things (IIoT). Tasks from the IIoT devices can be outsourced and executed at the accessible computational access point (CAP). This computing paradigm enables the computing resources much closer to the IIoT devices, and thus satisfy the stringent latency requirement of the IIoT tasks. However, existing works in MEC that focus on task offloading and resource allocation seldom consider the load balancing issue. Therefore, load balance aware task offloading strategies for IIoT devices in MEC are urgently needed. In this article, software-defined network (SDN) technology is adopted to address this issue, since the rule-based forwarding policy in SDN can help determine the most suitable offloading path and CAP for undertaking the computation. To this end, we formulate an optimization problem to minimize the response latency in the proposed SDN-assisted MEC architecture. A greedy algorithm is put forward to obtain the approximate optimal solution in polynomial time. Simulation has been carried out to evaluate the performance of the proposed approach. The simulation results reveal that our approach outstands other approaches in terms of the response latency.

关键词： computation offloading Industrial Internet of Things (IIoT) load balancing mobile edge computing (MEC) response latency optimization software-defined network (SDN)

来源：评论

学校读者我要写书评

暂无评论

Joint computation offloading and deployment optimization in multi-UAV-enabled MEC systems

引用

PEER-TO-PEER NETWORKING AND APPLICATIONS 2022年第1期15卷 194-205页

作者： Chen, Zheyi Zheng, Hongqiang Zhang, Jianshan Zheng, Xianghan Rong, Chunming Univ Exeter Dept Comp Sci Coll Engn Math & Phys Sci Exeter EX4 4QF Devon England Fuzhou Univ Coll Math & Comp Sci Fuzhou 350116 Peoples R China Univ Stavanger Dept Elect Engn & Comp Sci N-4036 Stavanger Norway

The combination of unmanned aerial vehicles (UAVs) and mobile edge computing (MEC) technology breaks through the limitations of traditional terrestrial communications. The effective line-of-sight channel provided by UAVs can greatly improve the communication quality between edge servers and mobile devices (MDs). To further enhance the Quality-of-Service (QoS) of MEC systems, a multi-UAV-enabled MEC system model is designed. In the proposed model, UAVs are regarded as edge servers to offer computing services for MDs, aiming to minimize the average task response time by jointly optimizing UAV deployment and computation offloading. Based on the problem definition, a two-layer joint optimization method (PSO-GA-G) is proposed. First, the outer layer utilizes a Particle Swarm Optimization algorithm combined with Genetic Algorithm operators (PSO-GA) to optimize UAV deployment. Next, the inner layer adopts a greedy algorithm to optimize computation offloading. The extensive simulation experiments verify the feasibility and effectiveness of the proposed PSO-GA-G. The results show that the PSO-GA-G can achieve a lower average task response time than the other three baselines.

关键词： Mobile edge computing Unmanned aerial vehicle deployment computation offloading Particle swarm optimization Genetic algorithm Greedy algorithm

来源：评论

学校读者我要写书评

暂无评论

A Multi-Task Oriented Framework for Mobile computation offloading

引用

IEEE TRANSACTIONS ON CLOUD COMPUTING 2022年第1期10卷 187-201页

作者： Lu, Junyu Li, Qiang Guo, Bing Li, Jie Shen, Yan Li, Gongliang Su, Hong Sichuan Univ Dept Comp Sci Chengdu 610065 Peoples R China Shanghai Liao Tong Univ Comp Sci & Engn Shanghai 200240 Peoples R China Chengdu Univ Informat Technol Sch Control Engn Chengdu 610041 Peoples R China Sichuan Univ Coll Comp Sci Chengdu 610017 Peoples R China

computation offloading has become popular in recent years as it is an effective way to reduce the energy consumption and enhance the performance of smartphones. To deal with the heterogeneous architectures between the smartphone and the server, and to simplify deployment of the server, we propose and implement a lightweight offloading framework which supports offloading of compute-intensive tasks and deploying the server efficiently. Based on this framework, generic and developer-customized offloading services could be provided for different third-party applications. Furthermore, we design a multi-task offloading tactic for the framework to deal with intensive offloading requests from various mobile devices. When receiving an offloading request, the master node in server-side determines whether this task should be offloaded or not and which VM should handle this task, so that the overall execution time and energy consumption are optimized. We implement this framework and evaluate it by comparing the execution time, energy consumption and CPU utilization rate among three execution modes with three applications. We also conduct experiments of the multi-task offloading tactic in simulation environment. Experimental results indicate that this framework effectively reduces energy consumption and boosts performance for compute-intensive tasks, and the multi-task offloading tactic is valid for intensive offloading requests.

关键词： computation offloading mobile cloud computing offloading service multi-task offloading Lyapunov-optimization

来源：评论

学校读者我要写书评

暂无评论

MCG: Mobility-Aware computation offloading in Edge Using Weighted Majority Game

引用

IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 2022年第6期9卷 4310-4321页

作者： Mukherjee, Anwesha Ghosh, Shreya De, Debashis Ghosh, Soumya K. Mahishadal Raj Coll Dept Comp Sci Purba Medinipur 721628 W Bengal India Penn State Univ Coll Informat Sci & Technol University Pk PA 16802 USA Maulana Abul Kalam Azad Univ Technol Dept Comp Sci & Engn Kolkata 700064 India Indian Inst Technol Kharagpur Dept Comp Sci & Engn Kharagpur 721302 W Bengal India

Edge computing plays a pivotal role in computation offloading at low latency. However, selecting the appropriate node to offload a computation is a challenge, especially when the user node is mobile. The problem can be stated as follows: (i) a mobile device has to offload a computation, and the user is moving, (ii) a set of edge/fog devices is available nearby the mobile device, then to select the suitable device to offload the computation. To address the challenge, this paper proposes an end-to-end mobility-aware computation offloading framework, MCG, which consists of: (i) a novel mobility prediction module that finds the user mobility pattern, (ii) selection of a set of edge/fog devices based on the predicted mobility and user's current location, (iii) selection of the high majority device from the set of edge/fog devices based on the resource availability and present load of the devices, and (iv) offloading the computation to the selected high majority device. The experimental results demonstrate that MCG outperforms existing mobility prediction modules in terms of accuracy, precision, and recall measures. The theoretical analysis and experimental results illustrate that MCG reduces the latency and power consumption of mobile device during offloading compared to existing offloading strategies.

关键词： Mobile handsets Power demand Performance evaluation Game theory Low power electronics computational modeling Task analysis computation offloading Game theory Latency-aware Low power Mobility analytics

来源：评论

学校读者我要写书评

暂无评论

Federated Learning Empowered computation offloading and Resource Management in 6G-V2X

引用

IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 2022年第5期9卷 3234-3243页

作者： Prathiba, Sahaya Beni Raja, Gunasekaran Anbalagan, Sudha Dev, Kapal Gurumoorthy, Sugeerthi Sankaran, Atshaya P. Anna Univ NGNLab Dept Comp Technol Chennai 600025 Tamil Nadu India Vellore Inst Technol Sch Comp Sci & Engn Chennai 632014 Tamil Nadu India Univ Johannesburg Dept Inst Intelligent Syst Auckland Pk ZA-2006 Gauteng South Africa

Humankind's urbanization and luxuriousneed increase the number of vehicles day by day. Alongside the advancement of technologies, autonomous vehicles have now come into a greater existence. Due to the resource constraints for obtaining a high processing rate in performing the intelligent tasks, Vehicular Edge Computing (VEC) has been introduced. However, a delay occurs in transferring the information due to the traits of automotive tasks. In this paper, a Federated Learning empowered computation offloading and Resource management (FLOR) is proposed, which offloads and manages the resources with low latency. The FLOR framework operates over heterogeneous networks that include Vehicle-to-Everything (V2X) communication, Dedicated Short Range Communication (DSRC), 5G millimeter-wave (5G-mmWave), and 6G Vehicle-to-Vehicle (6G-V2V) communication. FLOR employs the stochastic network calculus mechanism for computing the upper bound delay of the heterogeneous communication and obtains the probability of offloading mechanism. Upon the detected probability value, the FLOR offloads the computation task to the optimal network. Further, to avoid the increase in resource cost and utilize the available resources efficiently, an effective Radio Resource Management (e-RRM) based on Federated Q-Learning is proposed in FLOR, for allocating and using the available resources optimally. The simulation result shows that the proposed FLOR framework effectively offloads the computation between the available heterogeneous networks with optimal resource allocation by 20.69% than the existing solutions.

关键词： Vehicle-to-everything Task analysis Resource management Edge computing Delays 6G mobile communication Servers computation offloading Vehicular Edge Computing 6G-V2X Dedicated Short Range Communication 5G millimeter-Wave Federated Q-Learning Radio Resource Management

来源：评论

学校读者我要写书评

暂无评论

Authentication Security Level and Resource Optimization of computation offloading in Edge Computing Systems

引用

IEEE INTERNET OF THINGS JOURNAL 2022年第15期9卷 13010-13023页

作者： Xiao, Huizi Pei, Qingqi Song, Xifei Shi, Weisong Xidian Univ Sch Telecommun Engn State Key Lab ISN Xian 710071 Shaanxi Peoples R China Wayne State Univ Dept Comp Sci Detroit MI 48202 USA

Edge computing brings computation and storage resources to the edge of the mobile network to meet strict delay and high demanding applications. However, edge network environments are more vulnerable to malicious attacks. Reliable communication in networks usually relies heavily on verifying the authentication of content and identity. Nevertheless, enhancing the authentication security level means occupying more computation resources, time, and energy. There is a tradeoff between security level improvement and resource optimization in computation offloading. In this article, we take maximizing the authentication security level of the Merkle tree signature as segmental of the optimization objective and consider the different hash algorithms deployed on the edge servers to make the offloading decision. Specifically, to weight the time delay and authentication security level simultaneously, we formulate a minimum optimization problem to jointly optimize the offloading decision, packet transmitting rate, edge computation frequency, and data blocks number. Simulation results show that our proposed algorithms have well convergence and effectiveness and provide a tradeoff between time delay and authentication security level.

关键词： Security Authentication Servers Task analysis Optimization Mobile handsets Edge computing Authentication security level computation offloading edge computing Merkle tree (MT) signature resource optimization

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：