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Joint partial computation offloading and resource allocation in MEC-enable networks

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The Journal of China Universities of Posts and Telecommunications 2023年第1期30卷 80-86页

作者： Wu Hongxin Lin Zhijian Chen Pingping Chen Feng College of Physics and Information Engineering Fuzhou UniversityFuzhou 350108China

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.

关键词： mobile edge computing resource allocation partial computation offloading ant colony-based algorithm

Energy-Efficient Computation offloading and Resource Allocation in Fog Computing for Internet of Everything

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China Communications 2019年第3期16卷 32-41页

作者： Qiuping Li Junhui Zhao Yi Gong Qingmiao Zhang School of Electronic and Information Engineering Beijing Jiaotong UniversityBeijing 100044China School of Information Engineering East China Jiaotong UniversityNanchang 330013China Shenzhen Engineering Laboratory of Intelligent Information Processing for IoT Southern University of Science and TechnologyShenzhen 518055China

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.

关键词： fog computing cloud computing resource allocation computation offloading IoE

A Deep Learning Based Energy-Efficient Computational offloading Method in Internet of Vehicles

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China Communications 2019年第3期16卷 81-91页

作者： Xiaojie Wang Xiang Wei Lei Wang School of Software Dalian University of Technology School of Computer Science and Engineering Beihang University

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.

关键词： computational offloading fog computing deep learning internet of vehicles

An offloading Scheme Leveraging on Neighboring Node Resources for Edge Computing over Fiber-Wireless (FiWi) Access Networks

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China Communications 2019年第11期16卷 107-119页

作者： Wei Chang Yihong Hu Guochu Shou Yaqiong Liu Zhigang Guo Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing University of Posts and Telecommunications

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.

关键词： edge computing offloading Fiber-wireless access networks delay

Software defined industrial network architecture for edge computing offloading

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The Journal of China Universities of Posts and Telecommunications 2019年第1期26卷 49-58页

作者： Xu Fangmin Ye Huanyu Cui Shaohua Zhao Chenglin Yao Haipeng School of Telecommunication Engineering Beijing University of Posts and Telecommunications China Petroleum Technology and Development Corporation

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.

关键词： software defined industrial network(SDIN) industrial Internet of things(IIoT) edge computing computing offloading time delay

Research on adaptive dual task offloading decision algorithm for parking space recommendation service

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The Journal of China Universities of Posts and Telecommunications 2019年第6期26卷 30-42页

作者： Peng Weiping Su Zhe Song Cheng Jia Zongpu School of Computer Science and Technology Henan Polytechnic UniversityJiaozuo 454000China

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.

关键词： parking space recommendation service Internet of vehicles RSU adaptive dual task offloading

Efficient Task Completion for Parallel offloading in Vehicular Fog Computing

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China Communications 2019年第11期16卷 42-55页

作者： Jindou Xie Yunjian Jia Zhengchuan Chen Zhaojun Nan Liang Liang Chongqing Key Laboratory of Space Information Network and Intelligent Information Fusion School of Microelectronics and Communication Engineering Chongqing University

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.

关键词： parallel offloading vehicular fog computing task offloading HMM

IoT Task offloading in Edge Computing Using Non-Cooperative Game Theory for Healthcare Systems

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Computer Modeling in Engineering & Sciences 2024年第5期139卷 1487-1503页

作者： Dinesh Mavaluru Chettupally Anil Carie Ahmed I.Alutaibi Satish Anamalamudi Bayapa Reddy Narapureddy Murali Krishna Enduri Md Ezaz Ahmed School of Computing and Informatics Saudi Electronic UniversityRiyadhSaudi Arabia Department of Computer Engineering SRM University APGunturIndia College of Computer and Information Sciences Majmaah UniversityAl Majma’ahSaudi Arabia Department of Public Health College of Applied Medical SciencesKing Khalid UniversityAbhaSaudi Arabia

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.

关键词： Internet of Things edge computing offloading NOMA

A review on offloading in fog-based Internet of Things:Architecture,machine learning approaches,and open issues

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高置信计算(英文)

高置信计算(英文) 2023年第2期3卷 83-92页

作者： Kalimullah Lone Shabir Ahmad Sofi National Institute of Technology SrinagarJammu and Kashmir 190006India

There is an exponential increase in the number of smart devices,generating helpful information and posing a serious challenge while processing this huge data.The processing is either done at fog level or cloud level depending on the size and nature of the task.offloading data to fog or cloud adds latency,which is less in fog and more in the cloud.The methods of processing data and tasks at fog level or cloud are mostly machine learning based.In this paper,we will discuss all three levels in terms of architecture,starting from the internet of things to fog and fog to cloud.Specifically,we will describe machine learning-based offloading from the internet of things to fog and fog to cloud.Finally,we will come up with current research directions,issues,and challenges in the IoT–fog–cloud environment.

关键词： Fog computing Internet of Things Machine learning Architectures offloading Data