检索结果-内蒙古大学图书馆

A Code-Oriented Partitioning computation offloading strategy for Multiple Users and Multiple Mobile Edge Computing Servers

引用

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2020年第7期16卷 4800-4810页

作者： Ding, Yan Liu, Chubo Zhou, Xu Liu, Zhao Tang, Zhuo Hunan Univ Coll Informat Sci & Engn Changsha 410082 Hunan Peoples R China Natl Supercomp Ctr Changsha Changsha 410082 Hunan Peoples R China

In this article, we investigate code-oriented partitioning computation offloading strategy for multiple user equipments (UEs) and multiple mobile edge computing servers with limited resources (i.e., limited computing power and waiting task queues with finite capacity). This article aims to develop an offloading strategy to decide the execution location, CPU frequency, and transmission power for UE while minimizing the execution overhead (i.e., a weighted sum of energy consumption and computational time) of UE's applications, which is an NP-hard problem. To achieve the objective, first, we transform the problem into a convex optimization problem and find the optimal solution. Second, we propose a decentralized computation offloading strategy (DCOS) algorithm for UE, and define a dictionary data structure for recording the strategy of the UE to reduce the algorithm complexity. Finally, the effectiveness of DCOS, and the impact of various key parameters on the strategy and overhead are demonstrated by simulation experiments.

关键词： Code-oriented partitioning offloading (COPO) computation offloading strategy mobile edge computing (MEC) multiple servers with limited resources

来源：评论

学校读者我要写书评

暂无评论

MCVCO: Multi-MEC Cooperative Vehicular computation offloading

IEEE TRANSACTIONS ON INTELLIGENT VEHICLES

引用

IEEE TRANSACTIONS ON INTELLIGENT VEHICLES 2024年第1期9卷 813-826页

作者： Liu, Jianhang Xue, Kunlei Miao, Qinghai Li, Shibao Cui, Xuerong Wang, Danxin Li, Kewen China Univ Petr East China Coll Comp Sci & Technol Qingdao 266580 Peoples R China China Univ Petr East China Coll Oceanog & Space Informat Qingdao 266580 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China

Mobile edge computing (MEC) has been envisioned as a promising paradigm that provides processing resources for vehicular computation-intensive tasks to accommodate the strict latency requirement. However, there is still a need to further enhance system performance to overcome challenges such as poor efficiency of data transmission and limited system resources. To improve the quality of service, this article proposes a multi-MEC cooperative vehicular computation offloading (MCVCO) scheme. Firstly, we propose a heat-aware task offloading strategy to capture the time-varying multi-link relations between vehicle and MEC nodes. Secondly, we design a multi-MEC resource compensation method based on fountain code which cooperatively collects the task data and improves the efficiency of data reception in the edge layer. Finally, we develop a parallel transmission and execution based dynamic scheduling algorithm to make the most of available resources. Extensive simulation results and analyses demonstrate that MCVCO outperforms other baseline schemes in various experimental settings. MCVCO achieves a 32% increase in success rate, up to a 47% reduction in end-to-end latency, and a 24% improvement in uploading quality.

关键词： Task analysis Optimization Servers Delays Vehicle dynamics Heuristic algorithms Heating systems computation offloading strategy mobile edge computing(MEC) vehicular networks Quality of Service

来源：评论

学校读者我要写书评

暂无评论

IRS-assisted energy efficient communication for UAV mobile edge computing

引用

COMPUTER NETWORKS 2024年 246卷

作者： Zhang, Shuang Jin, Huilong Guo, Pingkang Hebei Normal Univ Shijiazhuang 050024 Peoples R China Hebei Prov Key Lab Informat Fus & Intelligent Cont Shijiazhuang 050024 Peoples R China

Unmanned aerial vehicles (UAVs) combined with mobile edge computing (MEC) servers assist ground terminals (GTs) for communication and computation in wireless networks. Intelligent reflecting surface(IRS) can effectively assist the UAV to improve the communication quality between UAV and GTs, at the same time, reducing processing delay in UAV for remote areas. In this paper, we propose a framework where an MEC server is mounted on the UAV and an IRS is used to enhance the communication channel in the uplink. The optimization problem of computation offloading strategy and 3D trajectory planning is formulated for minimizing the system service energy consumption. It is an NP -hard nonconvex optimization issue with extra difficulty that offloading tasks is highly dynamic. To tackle this challenging problem, we get a convex upper bound. On this basis, an energy consumption minimization algorithm (SAC-Enc) evolved from the deep reinforcement learning (DRL) soft actor critic (SAC) algorithm is proposed. Numerical evaluations confirm that the proposed approach is capable of rapid convergence and achieves superior performance in terms of energy consumption and computation delay compared to other benchmark algorithms.

关键词： Unmanned aerial vehicles (UAVs) Mobile edge computing (MEC) Intelligent reflecting surface (IRS) computation offloading strategy 3D trajectory planning

来源：评论

学校读者我要写书评

暂无评论

CA-PSO: A Combinatorial Auction and Improved Particle Swarm Optimization based computation offloading Approach for E-Healthcare

CA-PSO: A Combinatorial Auction and Improved Particle Swarm ...

引用

IEEE International Conference on Communications (ICC)

作者： Yuan, Xiaoming Tian, Hansen Zhang, Wenshuo Zhao, Hongyang Zhao, Zheyu Zhang, Ning Northeastern Univ Qinhuangdao Branch Campus Qinhuangdao 066004 Hebei Peoples R China Univ Windsor Dept Elect & Comp Engn Windsor ON N9B 3P4 Canada

ISBN: (数字)9781538683477

ISBN: (纸本)9781538683477

As one of the enabling technologies for E-Health, Internet of Medical Things (IoMT) interconnects various medical devices to collect and exchange healthcare information. To enable low-delay healthcare information processing, Mobile edge computing (MEC) can be incorporated in IoMT which can process various data in proximity of the medical devices. In this paper, we propose a Combinatorial Auction and Improved Particle Swarm Optimization based computation offloading Approach (CA-PSO) for e-healthcare to meet the Quality of Service (QoS) requirements of low delay and low energy consumption in healthcare monitoring. Firstly, we formulate a joint optimization problem to minimize the system cost consisting of delay and energy consumption, and transform this problem into a potential game. Secondly, we use combinatorial auction algorithm to analyze the offloading situation for different channels and servers, and combine channels and servers to simplify the original problem. Then we combine the offloading combination with improved Particle Swarm Optimization (PSO) to solve the optimal offloading strategy and server resource allocation. The simulation results show that compared with the comparison algorithm, the CA-PSO algorithm has achieved better performance in terms of average processing cost, delay, and energy consumption.

关键词： computation offloading strategy IoMT MEC Potential Game Combinatorial Auction Algorithm PSO

来源：评论

学校读者我要写书评

暂无评论

Performance Evaluation of Partial offloading under Various Scenarios in Mobile Edge Computing 24

Performance Evaluation of Partial Offloading under Various S...

引用

24th International Conference on Advanced Communication Technology (ICACT) - Artificial Intelligence Technologies toward Cybersecurity

作者： Oh, SeokBeom Lee, SooJeong Hong, Yong-Geun Kong, Gyuyeol Kahng, Hyun-Kook Korea Univ Program Converging Technol Syst & Standardizat Seoul South Korea Daejeon Univ Dept Artificial Intelligence & Convergence Daejeon South Korea Chongshin Univ Inst Hokmah Gen Educ Seoul South Korea Korea Univ Dept Elect & Informat Engn Seoul South Korea

ISBN: (纸本)9791188428090

The problem of offloading policy is addressed for mobile edge computing (MEC) in this paper. We proposed a deep learning-based partial offloading method to reduce user equipment's energy consumption and service delay. The proposed method consists of two deep neural networks (DNNs) to find the best partitioning of a single task and their offloading policy, respectively. Multiclass classification is used for the selection of partitioning and offloading policies. For partitioning selection, the DNN was learned through the ratio of task size instead of the actual task size to improve the classification accuracy. The performance of the proposed method was evaluated in three scenarios which are delay-critical model (DCM), energy-critical model (ECM), and delay and energy-critical model (DECM). The simulation results show that ECM has the worse classification performance for partitioning selection than DCM and DECM, while three scenarios have similar classification performance for offloading selection. Additionally, the proposed method has more than 77% and 89% classification performances for partitioning and offloading in various scenarios, respectively.

关键词： Mobile edge computing computation offloading strategy task partitioning deep learning

来源：评论

学校读者我要写书评

暂无评论

A Federated Learning and Deep Reinforcement Learning-Based Method with Two Types of Agents for computation Offload

引用

SENSORS 2023年第4期23卷 2243-2243页

作者： Liu, Song Yang, Shiyuan Zhang, Hanze Wu, Weiguo Xi An Jiao Tong Univ Sch Comp Sci & Technol Xian 710049 Peoples R China

With the rise of latency-sensitive and computationally intensive applications in mobile edge computing (MEC) environments, the computation offloading strategy has been widely studied to meet the low-latency demands of these applications. However, the uncertainty of various tasks and the time-varying conditions of wireless networks make it difficult for mobile devices to make efficient decisions. The existing methods also face the problems of long-delay decisions and user data privacy disclosures. In this paper, we present the FDRT, a federated learning and deep reinforcement learning-based method with two types of agents for computation offload, to minimize the system latency. FDRT uses a multi-agent collaborative computation offloading strategy, namely, DRT. DRT divides the offloading decision into whether to compute tasks locally and whether to offload tasks to MEC servers. The designed DDQN agent considers the task information, its own resources, and the network status conditions of mobile devices, and the designed D3QN agent considers these conditions of all MEC servers in the collaborative cloud-side end MEC system;both jointly learn the optimal decision. FDRT also applies federated learning to reduce communication overhead and optimize the model training of DRT by designing a new parameter aggregation method, while protecting user data privacy. The simulation results showed that DRT effectively reduced the average task execution delay by up to 50% compared with several baselines and state-of-the-art offloading strategies. FRDT also accelerates the convergence rate of multi-agent training and reduces the training time of DRT by 61.7%.

关键词： mobile edge computing computation offloading strategy multi-agent system deep reinforcement learning federated learning

来源：评论

学校读者我要写书评

暂无评论

Game-theoretic Analysis of computation offloading for Cloudlet-based Mobile Cloud Computing 15

Game-theoretic Analysis of Computation Offloading for Cloudl...

引用

18th ACM International Conference on Modelling, Analysis and Simulation of Wireless and Mobile Systems (MSWiM)

作者： Ma, Xiao Lin, Chuang Xiang, Xudong Chen, Congjie Tsinghua Univ Tsinghua Natl Lab Informat Sci & Technol Beijing Peoples R China Univ Sci & Technol Beijing Beijing Peoples R China

ISBN: (纸本)9781450337625

Mobile cloud computing (MC2) is emerging as a promising computing paradigm which helps alleviate the conflict between resource-constrained mobile devices and resource consuming mobile applications through computation offloading. In this paper, we analyze the computation offloading problem in cloudlet-based mobile cloud computing. Different from most of the previous works which are either from the perspective of a single user or under the setting of a single wireless access point (AP), we research the computation offloading strategy of multiple users via multiple wireless APs. With the widespread deployment of WLAN, offloading via multiple wireless APs will obtain extensive application. Taking energy consumption and delay (including computing and transmission delay) into account, we present a game-theoretic analysis of the computation offloading problem while mimicking the selfish nature of the individuals. In the case of homogeneous mobile users, conditions of Nash equilibrium are analyzed, and an algorithm that admits a Nash equilibrium is proposed. For heterogeneous users, we prove the existence of Nash equilibrium by introducing the definition of exact potential game and design a distributed computation offloading algorithm to help mobile users choose proper offloading strategies. Numerical extensive simulations have been conducted and results demonstrate that the proposed algorithm can achieve desired system performance.

关键词： computation offloading strategy Game theory Algorithm

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：