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7th International Conference on Communication and Information Processing (ICCIP)

作者： Wu Yinliang Zhi Hui Yang Lixia Anhui Univ Sch Elect Engn & Informat Hefei Peoples R China

ISBN: (纸本)9781450385190

IN IOT network, existing computing task offloading methods between terminals are focus on the scenario that computing resources and computing tasks remain unchanged. However, in the actual network, computing tasks and computing resources change dynamically with time, and these existing methods cannot be adjusted with the change of network state. So, in this paper, we propose a dynamic computing task offloading method based on multi round multi node auction algorithm (MRMNA) for IOT intelligent terminal. The auction algorithm is used to realize multi round offloading and the resource is scheduled by base station to realize dynamic allocation. Simulation results show that proposed method can improve the performance of time delay and energy consumption when compared with other methods.

关键词： Internet of things computing task offloading multi round multi node auction

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A dynamic computing task offloading method for IOT intelligent terminals 21

A dynamic computing task offloading method for IOT intellige...

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Proceedings of the 7th International Conference on Communication and Information Processing

作者： Yingliang Wu Hui Zhi Lixia Yang School of Electronic Information Engineering Anhui University China

ISBN: (纸本)9781450385190

关键词： computing task offloading Internet of things multi round multi node auction

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Training Latency Minimization for Model-Splitting Allowed Federated Edge Learning

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IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 2025年第3期12卷 2081-2092页

作者： Wen, Yao Zhang, Guopeng Wang, Kezhi Yang, Kun China Univ Min & Technol Sch Comp Sci & Technol Xuzhou 221116 Peoples R China Brunel Univ London Dept Comp Sci Uxbridge UB8 3PH Middx England Nanjing Univ State Key Lab Novel Software Technol Nanjing 210008 Peoples R China Nanjing Univ Sch Intelligent Software & Engn Suzhou Campus Suzhou 215163 Peoples R China

To alleviate the shortage of computing power faced by clients in training deep neural networks (DNNs) using federated learning (FL), we leverage the edge computing and split learning to propose a model-splitting allowed FL (SFL) framework, with the aim to minimize the training latency without loss of test accuracy. Under the synchronized global update setting, the latency to complete a round of global training is determined by the maximum latency for the clients to complete a local training session. Therefore, the training latency minimization problem (TLMP) is modelled as a minimizing-maximum problem. To solve this mixed integer nonlinear programming problem, we first propose a regression method to fit the quantitative-relationship between the cut-layer and other parameters of an AI-model, and thus, transform the TLMP into a continuous problem. Considering that the two subproblems involved in the TLMP, namely, the cut-layer selection problem for the clients and the computing resource allocation problem for the parameter-server are relative independence, an alternate-optimization-based algorithm with polynomial time complexity is developed to obtain a high-quality solution to the TLMP. Extensive experiments are performed on a popular DNN-model EfficientNetV2 using dataset MNIST, and the results verify the validity and improved performance of the proposed SFL framework.

关键词： Computational modeling Training Artificial intelligence Servers Resource management Edge computing Data models Load modeling Federated learning Minimization split learning edge computing computing task offloading resource allocation

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Achieving Cooperative Mobile-Edge computing Using Helper Scheduling

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IEEE TRANSACTIONS ON COMMUNICATIONS 2023年第6期71卷 3419-3436页

作者： Yang, Long Jiang, Hai Shi, Jia Xue, Xuan Ren, Peng Feng, Yunpeng Chen, Jian Xidian Univ State Key Lab Integrated Serv Networks Xian 710071 Peoples R China Univ Alberta Dept Elect & Comp Engn Edmonton AB T6G 1H9 Canada

This paper investigates computing task offloading from an Internet-of-Thing (IoT) device with limited transmit power to a mobile-edge computing (MEC) server located beyond the communication range of the IoT device. We propose an opportunistic cooperative offloading (OCO) strategy that recruits the IoT device's nearby spatially random idle-state users as helpers and opportunistically schedules one of them to partially execute the latency-critical task, and forwards the rest portion of the task to the MEC server. For the OCO strategy, we investigate the helper scheduling under three cases of system information availability, i.e., the global, partial, and distance information cases, and develop an offloading-outage optimal scheduling scheme for each case. For each scheduling scheme, an approximate expression is derived for the offloading-outage probability, with which the achieved diversity order is also theoretically evaluated. Simulation results verify our performance analysis for the OCO strategy using helper scheduling and show its achieved offloading-outage/energy consumption reduction over multi-helper cooperative offloading that fully uses all helpers for cooperation. In addition, the advantages of the proposed helper scheduling schemes over existing scheduling schemes are also demonstrated by simulations.

关键词： Mobile-edge computing (MEC) computing task offloading helper scheduling cooperative communications diversity order

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Matching-Theory-Based Multi-User Cooperative computing Framework

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IEEE COMMUNICATIONS LETTERS 2022年第2期26卷 414-418页

作者： Zhou, Ya Zhang, Guopeng Wang, Kezhi Yang, Kun China Univ Min & Technol Minist Educ Engn Res Ctr Mine Digitalizat Xuzhou 221116 Jiangsu Peoples R China China Univ Min & Technol Sch Comp Sci & Technol Xuzhou 221116 Jiangsu Peoples R China Northumbria Univ Dept Comp & Informat Sci Newcastle Upon Tyne NE1 8ST Tyne & Wear England Nanjing Univ Informat Sci & Technol Sch Elect & Informat Engn Nanjing 210044 Peoples R China Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu 610054 Peoples R China

In this letter, we propose a matching theory based multi-user cooperative computing (MUCC) scheme to minimize the overall energy consumption of a group of user equipments (UEs), where the UEs can be classified into the following roles: resource demander (RD), resource provider (RP), and standalone UE (SU). We first determine the role of each UE by leveraging the roommate matching method. Then, we propose the college admission based algorithm to divide the UEs into multiple cooperation groups, each consisting of one RP and multiple RDs. Next, we propose the rotation swap operation to further improve the performance without deteriorating the system stability. Finally, we present an effective task offloading algorithm to minimize the energy consumption of all the cooperation groups. The simulation results verify the effectiveness of the proposed scheme.

关键词： task analysis Energy consumption NOMA Solid modeling Partitioning algorithms Information science Computational modeling Multi-user cooperative computing matching theory computing task offloading

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A novel hierarchical distributed vehicular edge computing framework for supporting intelligent driving

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AD HOC NETWORKS 2024年 153卷

作者： Yang, Kun Sun, Peng Yang, Dingkang Lin, Jieyu Boukerche, Azzedine Song, Liang Fudan Univ Shanghai Peoples R China Duke Kunshan Univ Suzhou Peoples R China Univ Toronto Toronto ON Canada Univ Ottawa Ottawa ON Canada Gulf Univ Sci & Technol Ctr Appl Math & Bioinformat CAMB Hawally Kuwait

Recently, various infrastructure-assisted or onboard driving assistant applications have been proposed as a component of intelligent transportation systems (ITS) to improve the transportation system's efficiency and release public concern about road safety. However, such AI-assisted intelligent applications are mainly data-driven and put great demands on the computing power of the ITS systems. Therefore, in the highly dynamic Internet-of-Vehicles environment in ITS, how to effectively coordinate the limited computing power of the various components of the system and realize reliable support for such resource-consuming applications through efficient resource allocation methods is the focus of our research. Accordingly, a novel joint computing and communication resource scheduling method is proposed to fulfill those ITS applications' inherent heterogeneous quality of service (QoS) requirements. By fully exploiting the computing resources provided by the onboard computing device, the edge computing device located in the vehicle's proximity and remote data center, we designed a hierarchical three-layer Vehicular Edge computing (VEC) framework. Briefly, an onboard joint computation offloading and transmission scheduling policy is designed to assign corresponding offloading decisions to the locally generated computing tasks by considering the vehicle's computing resources and real-time network link status. Additionally, a new distributed resource allocation policy is developed for the edge devices, in which we derive a server selection policy and allocate communication time based on our proposed metric. To evaluate the performance and validate the effectiveness of our proposed method, we conduct extensive simulation tests and ablation experiments, respectively. The results show that our approach can achieve stable performance in various experimental settings. Also, compared to the state-of-the-art algorithms, our joint resource allocation policy significantly reduces the

关键词： Vehicular edge computing Distributed computing Scheduling computing task offloading Mobility-aware

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Joint RAN Slicing and Computation offloading for Autonomous Vehicular Networks: A Learning-Assisted Hierarchical Approach

IEEE OPEN JOURNAL OF VEHICULAR TECHNOLOGY

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IEEE OPEN JOURNAL OF VEHICULAR TECHNOLOGY 2021年 2卷 272-288页

作者： Ye, Qiang Shi, Weisen Qu, Kaige He, Hongli Zhuang, Weihua Shen, Xuemin Minnesota State Univ Dept Elect & Comp Engn & Technol Mankato MN 56001 USA Univ Waterloo Dept Elect & Comp Engn Waterloo ON N2L 3G1 Canada Zhejiang Univ Coll Informat Sci & Elect Engn Hangzhou 310027 Peoples R China

In this paper, a two-timescale radio access network (RAN) slicing and computing task offloading problem is investigated for a cloud-enabled autonomous vehicular network (C-AVN). We aim at jointly maximizing the communication and computing resource utilization with diverse quality-of-service (QoS) guarantee for autonomous driving tasks. Specifically, to capture the small-timescale network dynamics, a computing task scheduling problem is formulated as a stochastic optimization program, for maximizing the long-term network-wide computation load balancing with minimum task offloading variations. Due to the large problem size and unavailable network state transition probabilities, we employ cooperative multi-agent deep Q-learning (MA-DQL) with fingerprint to solve the problem by learning the set of stationary task offloading policies with stabilized convergence. Given the task offloading decisions, we further study a RAN slicing problem in a large timescale, which is formulated as a convex optimization program. We focus on optimizing the radio resource slicing ratios among base stations, to maximize the aggregate network utility with statistical QoS provisioning for autonomous driving tasks. Based on the impact of radio resource slicing on computation load balancing, we propose a two-timescale hierarchical optimization framework to maximize both communication and computing resource utilization. Extensive simulation results are provided to demonstrate the effectiveness of the proposed framework in comparison with state-of-the-art schemes.

关键词： Autonomous vehicular networks computing task offloading RAN slicing resource sharing task scheduling cooperative multi-agent deep Q-learning learning-assisted hierarchical approach diverse QoS provisioning

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Adaptive Compute offloading Algorithm for Metasystem Based on Deep Reinforcement Learning

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INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE 2022年第13期36卷 2252019-2252019页

作者： Wang, Chunxin Wang, Wensheng Li, Wenjing Liu, Zhu Zhu, Jinhong Zhang, Nan Tianjin Univ Tianjin 300072 Peoples R China State Grid Informat & Telecommun Co Ltd Beijing 100031 Peoples R China

There has been a lot of research on edge-computing task offloading in deep reinforcement learning (DRL). Deep reinforcement learning is one of the important algorithms in the current AI field, but there is still room for improvement in the time cost and adaptive correction ability of the algorithm. This paper studies the application of DRL algorithms in edge-computing task offloading, and its key innovation is to propose an MADRLCO algorithm, which is based on the design idea of the Actor-Critic framework, uses the DNN model to act as an Actor, and can more accurately locate the initial decision through iterative training, and use the LSTM model to optimize the Critic, which can be more accurate. The optimal decision can be located in a short period of time. The main work of this paper is divided into three parts: (1) The AC algorithm of the Actor-Critic framework in DRL is proposed to be applied to edge-computing task offloading. (2) To address the weak generalization ability of the basic version of the Actor-Critic algorithm in multi-objective optimization, the sequential quantitative correction and adaptive correction parameter K method are used to optimize the Critic frame, thereby improving the generalization ability of the model in multi-objective decision-making and improving the rationality of decision-making results. (3) Aiming at the problem of large time cost in the critical framework of the model, a search algorithm for resource allocation-related parameters based on the time-series prediction method is proposed (time-series forecasting is a research branch of pattern recognition), which reduces the time overhead of the algorithm and improves the adaptive correction capability of the model. The algorithm in this paper can adapt to not only the time-varying network channel state, but also the time-varying number of device connections. Finally, it is proved by experiments that compared with the DRL calculation offloading algorithm based on DNN plus binary

关键词： Deep reinforcement learning edge computing computing task offloading time-series forecasting pattern recognition artificial intelligence

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