检索结果-内蒙古大学图书馆

computation offloading for Smart Devices in Fog-Cloud Queuing System

IETE JOURNAL OF RESEARCH 2023年第3期69卷 1509-1521页

作者： Sufyan, Farhan Banerjee, Amit South Asian Univ Dept Comp Sci New Delhi 110021 India

Advancements in sensor and hardware technology have surged the growth of smart devices (SDs), including smartphones, and wearable devices. The data generated by the built-in sensors are utilized by different applications such as health-care, smart-city, and connected-vehicles. However, due to the computation and energy limitations of the SDs, they often need to offload the computation-intensive tasks for processing to the remote server. The cloud-based offloading can meet various applications' demands, but due to high network latency, it is inefficient for real-time applications. Fog computing provides an alternative for the same, as it aggregates the fog nodes' resources at the edge of the network to meet the computational requirements of the real-time applications. In this paper, we consider a Fog-Cloud architecture consisting of multiple SDs, fog nodes, and the cloud. We use appropriate queuing models to simulate the traffic delay at different network elements and formulate a non-linear multi-objective optimization problem to minimize the energy consumption, execution delay, and cost of remote execution. Finally, the Stochastic Gradient descent (SGD) algorithm based solution approach is proposed that jointly optimizes offloading probability and transmission power to find the optimal trade-off between the offloading objectives. Simulation results show the effectiveness and the efficiency of the proposed system validated by the results.

关键词： computation offloading Fog-Cloud computing queuing theory smart devices (SDs)

来源：评论

学校读者我要写书评

暂无评论

computation offloading and Resource Allocation in Unmanned Aerial Vehicle Networks

引用

IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2023年第4期72卷 4981-4995页

作者： Liu, Binghong Liu, Chenxi Peng, Mugen Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China

Deploying unmanned aerial vehicles (UAVs) as mobile edge computing (MEC) servers has been attracting significant attention, since the UAVs' inherent maneuverability and mobility can further reduce the distance between the users and computational functionalities. In the UAV-assisted MEC systems, the caching and offloading decision optimization, subject to the computation and energy constraints of the UAVs, is one of the key design issues. Against this backdrop, in this paper, we propose a novel cloud-edge framework to facilitate MEC in the UAV networks. Specifically, in our framework, the edge UAVs (EUAVs), together with the cloud, provide caching and computing services for the terrestrial users. In order to minimize the weighted sum cost of latency and energy consumption, we jointly optimize the caching and offloading decisions, the EUAV deployment, the radio and computation resource allocation, while simultaneously satisfying the UAVs' cache and computation capacity constraints, as well as the users' latency and energy consumption constraints. To solve this NP-hard problem, we propose a sequential convex programming (SCP) and sequential quadratic programming (SQP) based deep Q-learning (SS-DQN) algorithm. The proposed algorithm allows the system to adaptively adjust caching and offloading decisions with the EUAV deployment scheme and the resource allocation scheme obtained by SCP and SQP algorithms, respectively. Simulation results verify the superiority of our proposed algorithm compared to two benchmark schemes, and demonstrate how the combination of DRL and convex optimization can improve the system performance significantly.

关键词： Task analysis Cloud computing Resource management Autonomous aerial vehicles Servers Energy consumption Costs Unmanned aerial vehicles (UAVs) wireless caching computation offloading

来源：评论

学校读者我要写书评

暂无评论

computation offloading for Energy and Delay Trade-Offs With Traffic Flow Prediction in Edge Computing-Enabled IoV

引用

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2023年第12期24卷 15613-15623页

作者： Xu, Xiaolong Yang, Chenyi Bilal, Muhammad Li, Weimin Wang, Huihui Nanjing Univ Informat Sci & Technol Jiangsu Collaborat Innovat Ctr Atmospher Environm Nanjing 210044 Peoples R China Nanjing Univ Informat Sci & Technol Sch Software Nanjing 210044 Peoples R China Hankuk Univ Foreign Studies Dept Comp & Elect Syst Engn Yongin 17035 Gyeonggi Do South Korea Shanghai Univ Sch Comp Engn & Sci Shanghai 200444 Peoples R China St Bonaventure Univ Cybersecur Program St Bonaventure NY 14778 USA

An unprecedented prosperity in artificial intelligence promotes the development of Internet of Vehicles (IoV). Assisted by edge computing, vehicles enable to offload data to edge servers in close proximity to users for processing, thus making up for the shortage of local computing resources. However, due to the uneven space-time distribution of traffic flow, edge servers of a certain road segment may be overwhelmed by the surge of service requests. Furthermore, IoV system will incur significant additional energy consumption and time delay because of the absence of a proper computation offloading scheme between edge servers. To cope with above challenges, a computing offloading method for energy and delay trade-offs with traffic flow prediction in edge computing-enabled IoV is proposed. We first design the graph weighted convolution network (GWCN) that can fully excavate the connectivity and distance relation information between road segments to conduct traffic flow prediction. The short-term prediction results are utilized as the basis for adjusting the resource allocation of edge resources in different regions. Then, a computation offloading method driven by deep deterministic policy gradient (DDPG) is leveraged to obtain an optimal computation offloading scheme for edge servers. Finally, extensive comparative experiments demonstrate the low prediction error of GWCN and superior performance of DDPG-driven method in reducing total time delay and energy consumption.

关键词： computation offloading deep reinforcement learning traffic flow prediction graph neural network edge computing

来源：评论

学校读者我要写书评

暂无评论

computation offloading for ground robotic systems communicating over WiFi - an empirical exploration on performance and energy trade-offs

引用

EMPIRICAL SOFTWARE ENGINEERING 2023年第6期28卷 1-61页

作者： Dordevic, Milica Albonico, Michel Lewis, Grace A. Malavolta, Ivano Lago, Patricia Vrije Univ Amsterdam Amsterdam Netherlands Fed Univ Technol Parana UTFPR Francisco Beltrao Brazil Carnegie Mellon Software Engn Inst Pittsburgh PA USA

ContextRobotic systems are known to perform computation-intensive tasks with limited computational resources and battery life. Such systems might benefit from offloading heavy workloads to the Cloud;however, in some cases, this implies high network traffic that degrades performance and energy *** this study, we aim at evaluating the impact of different computation offloading strategies on performance and energy consumption in the context of autonomous *** conduct two controlled experiments involving a robotic mission based on the Turtlebot3 robot and ROS 1. The mission consists of three tasks that are recurrent in robotics and good candidates for computation offloading in research, namely, SLAM mapping, navigation stack, and object recognition. Each of the tasks is either executed on board or offloaded in a full-factorial experiment design. The obtained measures are then statistically *** results show that offloading the object recognition task causes a more significant decrease in resource utilization and energy consumption than both SLAM mapping and navigation. However, object recognition affects the volume of network traffic significantly to the extent that it can easily cause network *** the context of our experiments (i.e., those involving small-scale ground ROS-based mobile robots operating under WiFi networks), offloading object recognition is beneficial in terms of performance and energy consumption. Nevertheless, large network bandwidth needs to be available for object recognition offloading. While the image resolution and frame rate have a significant impact on not only the network traffic but also energy consumption and performance, these parameters need to be carefully set so that the results of this task can be always received in time, which is particularly crucial in real-time systems.

关键词： Robotic system computation offloading Software engineering Empirical evaluation Energy consumption

来源：评论

学校读者我要写书评

暂无评论

computation offloading for Tasks With Bound Constraints in Multiaccess Edge Computing

引用

IEEE INTERNET OF THINGS JOURNAL 2023年第17期10卷 15526-15536页

作者： Li, Kexin Wang, Xingwei He, Qiang Ni, Qiang Yang, Mingzhou Dustdar, Schahram Northeastern Univ Coll Comp Sci & Engn Shenyang 110819 Peoples R China Northeastern Univ Key Lab Synthet Automat Proc Ind Shenyang 110819 Peoples R China Northeastern Univ Coll Med & Biol Informat Engn Shenyang 110169 Peoples R China Univ Lancaster Sch Comp & Commun Lancaster LA1 4WA England Shenyang Univ Technol Sch Informat Sci & Engn Shenyang 110178 Peoples R China TU Wien Distributed Syst Grp A-1040 Vienna Austria

Multiaccess edge computing (MEC) provides task offloading services to facilitate the integration of idle resources with the network and bring cloud services closer to the end user. By selecting suitable servers and properly managing resources, task offloading can reduce task completion latency while maintaining the Quality of Service (QoS). Prior research, however, has primarily focused on tasks with strict time constraints, ignoring the possibility that tasks with soft constraints may exceed the bound limits and failing to analyze this complex task constraint issue. Furthermore, considering additional constraint features makes convergent optimization algorithms challenging when dealing with such complex and high-dimensional situations. In this article, we propose a new computational offloading decision framework by minimizing the long-term payment of computational tasks with mixed bound constraints. In addition, redundant experiences are gotten rid of before the training of the algorithm. The most advantageous transitions in the experience pool are used for training in order to improve the learning efficiency and convergence speed of the algorithm as well as increase the accuracy of offloading decisions. The findings of our experiments indicate that the method we have presented is capable of achieving fast convergence rates while also reducing sample redundancy.

关键词： Bound constraints computation offloading deep reinforcement learning (DRL) Markov decision process multiaccess edge computing (MEC)

来源：评论

学校读者我要写书评

暂无评论

computation offloading and Resource Allocation in NOMA-MEC: A Deep Reinforcement Learning Approach

引用

IEEE INTERNET OF THINGS JOURNAL 2023年第17期10卷 15464-15476页

作者： Shang, Ce Sun, Yan Luo, Hong Guizani, Mohsen Beijing Univ Posts & Telecommun Dept Comp Sci Beijing 100876 Peoples R China Mohamed Bin Zayed Univ Artificial Intelligence Machine Learning Dept Abu Dhabi U Arab Emirates

Multiaccess edge computing has emerged as a powerful paradigm for increasing the computation performance of mobile devices (MDs). Applying nonorthogonal multiple access (NOMA) to MEC can further improve the spectrum efficiency and reduce offloading delays caused by the upload congestion. In this article, we examine the joint computation offloading and resource allocation problem in the NOMA-MEC system, which benefits from the combination of NOMA and MEC. Our optimization objective is to minimize the computational overhead (the weighted sum of the execution delay and the energy consumption) in dynamic environments with time-varying wireless fading channels. The optimization problem is formulated as a mixed-integer programming (MIP), which involves jointly optimizing the task offloading decisions, channel assignment, and transmit power allocation. To solve such an optimization problem, we formalize the task offloading and the resource allocation as a Markov decision process (MDP). Then, we propose a deep reinforcement learning (DRL)-based approach, which combines multiple deep neural networks (DNNs) to directly approximate different statistical models for continuous and discrete control. The simulation results demonstrate that the proposed approach can rapidly converge and efficiently decrease the total computational overhead compared to other baseline approaches in different scenarios.

关键词： computation offloading deep reinforcement learning (DRL) mobile-edge computing (MEC) nonorthogonal multiple access (NOMA) resource allocation

来源：评论

学校读者我要写书评

暂无评论

computation offloading and Resource Allocation Based on DT-MEC-Assisted Federated Learning Framework

引用

IEEE TRANSACTIONS ON COGNITIVE COMMUNICATIONS AND NETWORKING 2023年第6期9卷 1707-1720页

作者： He, Yejun Yang, Mengna He, Zhou Guizani, Mohsen Shenzhen Univ Guangdong Engn Res Ctr Base Stn Antennas & Propag Shenzhen Key Lab Antennas & Propagat Coll Elect & Informat EngnState Key Lab Radio Fr Shenzhen 518060 Peoples R China Univ Maryland Dept Mech Engn College Pk MD 20742 USA Mohamed Bin Zayed Univ Artificial Intelligence Machine Learning Dept Abu Dhabi U Arab Emirates

Traditional centralized machine learning uses a large amount of data for model training, which may face some privacy and security problems. On the other hand, federated learning (FL), which focuses on privacy protection, also faces challenges such as core network congestion and limited mobile device (MD) resources. The computation offloading technology of mobile edge computing (MEC) can effectively alleviate these challenges, but it ignores the effect of user mobility and the unpredictable MEC environment. In this paper, we first propose an architecture that combines digital twin (DT) and MEC technologies with the FL framework, where the DT network can virtually imitate the statue of physical entities (PEs) and network topology to be used for real-time data analysis and network resource optimization. The computation offloading technology of MEC is used to alleviate resource constraints of MDs and the core network congestion. We further leverage the FL to construct DT models based on PEs' running data. Then, we jointly optimize the problem of computation offloading and resource allocation to reduce the straggler effect in FL based on the framework. Since the solution of the objective function is a stochastic programming problem, we model a Markov decision process (MDP), and use the deep deterministic policy gradient (DDPG) algorithm to solve this objective function. The simulation results prove the feasibility of the proposed scheme, and the scheme can significantly reduce the total cost by about 50% and improve the communication performance compared with baseline schemes.

关键词： Servers computational modeling Resource management Task analysis Training Optimization Internet of Things computation offloading resource allocation federated learning deep deterministic policy gradient (DDPG)

来源：评论

学校读者我要写书评

暂无评论

computation offloading Method Using Stochastic Games for Software-Defined-Network-Based Multiagent Mobile Edge Computing

引用

IEEE INTERNET OF THINGS JOURNAL 2023年第20期10卷 17620-17634页

作者： Wu, Guowen Wang, Hui Zhang, Hong Zhao, Yuhan Yu, Shui Shen, Shigen Donghua Univ Sch Comp Sci & Technol Shanghai 201620 Peoples R China Univ Technol Sydney Sch Comp Sci Ultimo NSW 2007 Australia Huzhou Univ Sch Informat Engn Huzhou 313000 Peoples R China

In the scenario of Industry 4.0, mobile smart devices (SDs) on production lines have to process massive amounts of data. These computing tasks sometimes far exceed the computing capability of SDs and require lots of energy and time to process. How to effectively reduce energy consumption and latency is necessary to be solved. To this end, we first propose a software-defined network (SDN)-based mobile edge computing (MEC) system. In the MEC system, SDs can offload computation tasks to edge servers to decrease the processing latency and avoid the waste of energy. At the same time, taking advantage of SDN's programmability, scalability, and isolation of the control plane and the data plane, an SDN controller can manage edge devices within the MEC system. Second, based on a stochastic game, we study the computation offloading and resource allocation problems in the MEC system and establish a stochastic game-based computation offloading model. Furthermore, we prove that the multiuser stochastic game in this system can achieve Nash Equilibrium. We further consider each SD as an independent agent and design a stochastic game-based resource allocation algorithm with prioritized experience replays (SGRA-PERs) to minimize energy consumption and processing latency with Multiagent Reinforcement Learning. Experiment results demonstrate that the proposed SGRA-PER is superior to MADDPG, Q -Mix, and MAPPO algorithms, which can significantly reduce the processing delay and energy consumption with dynamic resource allocation. Moreover, SGRA-PER can still keep a higher performance under the increase of SDs, which can be applied in a large-scale MEC system.

关键词： computation offloading mobile edge computing (MEC) multiagent reinforcement learning (MARL) resource allocation stochastic game

来源：评论

学校读者我要写书评

暂无评论

computation offloading for Rechargeable Users in Space-Air-Ground Networks

引用

IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2023年第3期72卷 3805-3818页

作者： Gong, Yongkang Yao, Haipeng Wu, Di Yuan, Wanmai Dong, Tao Yu, F. Richard BUPT Sch Informat & Commun Engn Beijing 100876 Peoples R China BUPT Sch Elect Engn Beijing 100876 Peoples R China China Acad Elect & Informat Technol Beijing 100041 Peoples R China Beijing Inst Satellite Informat Engn State Key Lab Space Ground Integrated Informat Tec Beijing 100095 Peoples R China Carleton Univ Sch Informat Technol Ottawa ON K1S 5B6 Canada

Relying on space-air-ground (SAG)-integrated artificial intelligence of everything (AIoE) networks, massive computation-intensive and latency-sensitive tasks can be efficiently either executed locally by ground AIoE users, or offloaded to SAG servers, such as remote base stations, aerial high altitude platform (HAP) and low earth orbit satellites. However, joint optimization of communication and computation resources becomes a great challenge considering dynamic network environment, large-scale coverage and battery energy backup constraint. Hence, in this paper, we propose a SAG-integrated heterogenous computation offloading architecture for the deep integration of communication and computation resources in order to maximize the sum-rate of all AIoE users. Moreover, we propose a multi-agent proximal policy optimization algorithm with the aid of Lyapunov-based profile to solve the task scheduling and HAP selection. And a convex optimization based communication and computation resource allocation scheme processes the CPU-cycle frequency and transmission power. The battery energy backup is tackled via the linear programming policy. Experimental results demonstrate that our proposed method outperforms existing state-of-the-art baselines in terms of convergence speed, average sum-rate and battery backup level of AIoE users.

关键词： Task analysis Batteries Satellites Resource management Optimization computational modeling Vehicle dynamics Battery energy backup computation offloading Lyapunov optimization multi-agent proximal policy optimization (MAPPO) space-air-ground (SAG) networks

来源：评论

学校读者我要写书评

暂无评论

computation offloading and Resource Allocation in C-RAN Supporting Wireless Charging

引用

IEEE INTERNET OF THINGS JOURNAL 2023年第10期10卷 8853-8864页

作者： Li, Ruijing Tang, Lan Bai, Yechao Lou, Mengting Zhang, Xinggan Nanjing Univ Sch Elect Sci & Engn Nanjing 210093 Peoples R China China Mobile Res Inst Future Res Lab Beijing 100053 Peoples R China

In this article, we consider a cloud-radio access network (C-RAN) with multiple enhanced remote radio heads (RRHs), which support for wireless charging, computation offloading and information transmission. The employment of enhanced RRHs requires a redesign for efficient resource allocation. Our goal is to minimize the weighted sum of energy transfer time and computation time by jointly optimizing energy transmission time, energy beamforming vectors, offloading factors, information transmission time, and combining vectors, while satisfying the communication performance requirements of terminal nodes. The problem is formulated as a mixed-integer nonlinear programming, which can be solved by alternating optimization (AO) utilizing semidefinite relaxation (SDR) and nonlinear transformation. To reduce the online computation delay of AO algorithm, we propose an optimization method based on the deep learning framework, which can determine an optimal solution with a trained neural network in real-time application. To improve the performance of the learning algorithm, we use two methods to set labels for training deep neural network to obtain the optimal offloading decision: 1) preoptimization and 2) self-evolution. Simulation results show that with the trained neural network, the algorithms based on deep learning can achieve a similar performance to the AO algorithm with less computation time.

关键词： Wireless communication Wireless sensor networks Task analysis Resource management Sensors Information processing Array signal processing Alternating optimization (AO) computation offloading deep neural network resource allocation semidefinite relaxation (SDR)

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：