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Joint Resource Allocation and User Association for Heterogeneous Services in multi-access edge computing Networks

IEEE access 2019年 7卷 12272-12282页

作者： Zhou, Jizhe Zhang, Xing Wang, Wenbo Beijing Univ Posts & Telecommun Sch Informat & Commun Engn Beijing 100876 Peoples R China

multi-access edge computing (MEC) has emerged as a promising technique for low-latency services, in light of its proximity to users and embedded cloud computing capability. In order to improve the network efficiency and fairness, it is crucial and challenging to jointly optimize resource management and user association mechanism with the consideration of heterogeneous services and network conditions. Moreover, the existing studies always neglect the heterogeneities of services on the requirements of both computation capability and storage capability. To solve this issue, we derive a strategy to improve the overall delay-aware performance of heterogeneous services with the MEC capability of computation and storage and the choices of the users' association. Accordingly, a coalition-game-based algorithm is proposed to form user coalitions for association scheme and resource sharing policy. In particular, we mathematically present that the proposed algorithm is capable of convergence and optimality. The simulation results also show that our algorithm gets a good performance efficiently. Furthermore, it reduces the weighted sum of delays of users by average 27.8% and 82.1%, while continuously improving delay-aware fairness, compared with those of the priority-based assignment scheme and the nearest assignment scheme, respectively.

关键词： multi-access edge computing resource allocation user association coalition game

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Joint Optimization of Request Assignment and computing Resource Allocation in multi-access edge computing

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IEEE TRANSACTIONS ON SERVICES computing 2023年第2期16卷 1254-1267页

作者： Liu, Haolin Long, Xiaoling Li, Zhetao Long, Saiqin Ran, Rong Wang, Hui-Ming Sch Comp Sci Key Lab Hunan Prov Internet Things & Informat Secu Xiangtan 411105 Peoples R China Xiangtan Univ Hunan Int Sci & Technol Cooperat Base Intelligent Xiangtan 411105 Peoples R China Ajou Univ Dept Elect & Comp Engn Suwon 16499 South Korea Xi An Jiao Tong Univ Sch Informat & Commun Engn Xian 710049 Shaanxi Peoples R China Minist Educ Key Lab Intelligent Networks Network Secur Xian 710049 Peoples R China

With the development of multi-access edge computing (MEC), the cloudlet at the edge of the network can provide nearby high-performance computing services, thus reducing the computational consumption of user equipments (UEs). To provide more real-time computing services to UEs, service providers face the challenge of optimizing the assignment of requests and the allocation of cloudlets' computing resources to achieve low latency while dealing with the large number of offloaded requests from UEs. Therefore, in this paper, we study the problem of minimizing the total latency to complete the requests in the MEC network by jointly optimizing request assignment and computing resource allocation. The problem is formulated as a mixed integer nonlinear programming (MINLP) problem which is NP-hard. To solve the problem, we decompose the problem into two subproblems which respectively optimize the request assignment and the computing resource allocation. We first deal with the computing resource allocation problem by utilizing the Lagrangian multiplier method, and the resulting solution is applied for the request assignment problem. Then a novel primal-dual based approximation algorithm is devised to address the request assignment problem. Finally, to verify the efficiency of the proposed algorithm, we provide an upper bound on the approximation ratio. The experiment results show that the proposed algorithm outperforms baseline algorithms in terms of total latency, loading balancing, and computational speed.

关键词： Resource management Approximation algorithms Task analysis Optimization Computational modeling Heuristic algorithms Cloud computing Approximation algorithm computing resource allocation joint optimization latency minimization multi-access edge computing request assignment

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An optimized heterogeneous multi-access edge computing framework based on transfer learning and artificial internet of things

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ALEXANDRIA ENGINEERING JOURNAL 2024年 109卷 176-190页

作者： Yu, Ping Zhao, Hongwei Geng, Xiaozhong Hu, Ming Yan, Hui Long, Duo Guo, Huangbin Zong, Baoshu Jilin Univ Coll Comp Sci & Technol Changchun 130015 Jilin Peoples R China Changchun Inst Technol Changchun 130012 Jilin Peoples R China Jilin Univ Architecture & Technol Changchun 130114 Jilin Peoples R China Suqian Univ Suqian 223800 Jiangsu Peoples R China

In most practical applications, the feature space of the training datasets and the target domain datasets are inconsistent, or the data distribution between them is inconsistent, which leads to the problem of data starvation and makes it difficult for terminal devices to obtain high accurate results. Aiming at the problems of limited terminal device resources, low accuracy of data processing results, and unsatisfactory processing speed, a Heterogeneous multi-access edge computing (MEC) Framework based on Transfer Learning (TL) is proposed, abbreviated as HMECF-TL. This framework adopts a cloud-edge-end three-layer architecture. It uses model transfer to optimize the Convolutional Neural Networks (CNN) model at each layer to achieve the goal of improving data processing speed and accuracy. Furthermore, a multi-agent Deep Reinforcement Learning Algorithm having Attention Mechanism (DRLAAM) is designed to further increase the timeliness performance of computation-intensive applications. The performance of HMECSF-TL framework is verified by simulation experiments, which not only reduces the delay by more than 24.66 %, but also improves the accuracy by more than 8.34 %. The framework not only increase the computing capacity to solve the shortage of terminal device resources, but also improve the quality of data processing to solve the problem of data starvation.

关键词： multi-access edge computing Task offloading Transfer learning Artificial intelligence Attention mechanism Deep reinforcement learning Image classification

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Distributed multi-Cloud multi-access edge computing by multi-Agent Reinforcement Learning

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2021年第4期20卷 2565-2578页

作者： Zhang, Yutong Di, Boya Zheng, Zijie Lin, Jinlong Song, Lingyang Peking Univ Dept Elect Beijing 100871 Peoples R China Imperial Coll London Dept Comp London SW7 2BU England Peking Univ Sch Software & Microelect Beijing 100871 Peoples R China

In this paper, we consider a three-layer distributed multi-access edge computing (MEC) network where multiple clouds, MEC servers, and edge devices (EDs) are deployed at the top layer, middle layer, and bottom layer, respectively. Each cloud center (CC) is associated with an independent service provider and publishes an application-driven computing task. To deliver the tasks, CCs rely on EDs to generate the raw data and offload part of the computing tasks to both EDs and MEC servers such that their computing and transmission resources can be fully utilized to reduce the system latency. However, in such a three-layer network, the distributed deployment of tasks leads to inevitable resource competition among CCs. To address this issue, we propose a distributed scheme based on multi-agent reinforcement learning, where each CC jointly determines the task offloading and resource allocation strategy based on its inference of other CCs' decisions. Simulation results indicate that a lower system latency is achieved via our proposed scheme compared with the existing schemes. In addition, the influence of the number of CCs, MEC servers, and EDs on latency performance is also discussed.

关键词： Task analysis Servers Wireless communication Resource management Data processing Cloud computing Reinforcement learning Distributed multi-access edge computing multi-cloud multi-agent reinforcement learning

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Resource management optimisation for federated learning-enabled multi-access edge computing in internet of vehicles

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INTERNATIONAL JOURNAL OF SENSOR NETWORKS 2023年第1期42卷 15-28页

作者： Zhou, Qilin Wang, Lili Wu, Shoulin Nanjing Univ Sci & Technol Sch Automat Nanjing 210094 Peoples R China

In internet of vehicles (IoVs), an edge server can process related computing tasks offloaded by vehicle-mounted terminals at a close distance based on cached application services. However, if the edge server caches all possible application services, the economic benefit of the server will be reduced owing to a large number of traffic service types. Additionally, vehicle-mounted terminals information may be leaked during data transmission. Therefore, we constructed an integrated framework of edge service and federated learning for IoVs. A resource management approach based on deep reinforcement learning (DRL) is also proposed for the joint optimisation of task offloading decisions, communication and computing resource allocation, as well as edge service caching placement in IoVs. The simulation results indicate that the DRL-based approach can overcome the high complexity of a network system and large space of policy selection and has good convergence performance as well as joint optimisation effect.

关键词： internet of vehicles IoVs multi-access edge computing MEC network resource management deep reinforcement learning DRL

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Cloud-Native Network Slicing Using Software Defined Networking Based multi-access edge computing: A Survey

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IEEE access 2021年 9卷 10903-10924页

作者： Shah, Syed Danial Ali Gregory, Mark A. Li, Shuo RMIT Univ Sch Engn Melbourne Vic 3000 Australia

Fifth-Generation (5G) mobile cellular networks provide a promising platform for new, innovative and diverse IoT applications, such as ultra-reliable and low latency communication, real-time and dynamic data processing, intensive computation, and massive device connectivity. End-to-End (E2E) network slicing candidates present a promising approach to resource allocation and distribution that permit operators to flexibly provide scalable virtualized and dedicated logical networks over common physical infrastructure. Though network slicing promises the provision of services on demand, many of its use cases, such as self-driving cars and Google's Stadia, would require the integration of a multi-access edge computing (MEC) platform in 5G networks. edge computing is envisioned as one of the key drivers for 5G and Sixth-Generation (6G) mobile cellular networks, but its role in network slicing remains to be fully explored. We investigate MEC and network slicing for the provision of 5G service focused use cases. Recently, changes to the cloud-native 5G core are a focus with MEC use cases providing network scalability, elasticity, flexibility, and automation. A cloud-native microservices architecture, along with its potential use cases for 5G network slicing, is envisioned. This paper also elaborates on the recent advances made in enabling E2E network slicing, its enabling technologies, solutions, and current standardization efforts. Finally, this paper identifies open research issues and challenges and provides possible solutions and recommendations.

关键词： 5G mobile communication Network slicing Cloud computing Computer architecture edge computing Software Servers Network slicing software defined networking multi-access edge computing cloud native ultra-reliable and low latency communication

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DQN-Based Proactive Trajectory Planning of UAVs in multi-access edge computing

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Computers, Materials & Continua 2023年第3期74卷 4685-4702页

作者： Adil Khan Jinling Zhang Shabeer Ahmad Saifullah Memon Babar Hayat Ahsan Rafiq School of Electronic Engineering Beijing University of Posts and TelecommunicationsBeijing100876China State Key Laboratory of Networking and Switching Technology Beijing University of Posts and TelecommunicationsBeijing100876China Department of Automation Chongqing University of Posts and TelecommunicationsChongqing400065China

The main aim of future mobile networks is to provide secure,reliable,intelligent,and seamless *** also enables mobile network operators to ensure their customer’s a better quality of service(QoS).Nowadays,Unmanned Aerial Vehicles(UAVs)are a significant part of the mobile network due to their continuously growing use in various *** better coverage,cost-effective,and seamless service connectivity and provisioning,UAVs have emerged as the best choice for telco *** can be used as flying base stations,edge servers,and relay nodes in mobile *** the other side,multi-access edgecomputing(MEC)technology also emerged in the 5G network to provide a better quality of experience(QoE)to users with different QoS ***,UAVs in a mobile network for coverage enhancement and better QoS face several challenges such as trajectory designing,path planning,optimization,QoS assurance,mobilitymanagement,*** efficient and proactive path planning and optimization in a highly dynamic environment containing buildings and obstacles are ***,an automated Artificial Intelligence(AI)enabled QoSaware solution is needed for trajectory planning and ***,this work introduces a well-designed AI and MEC-enabled architecture for a UAVs-assisted future *** has an efficient Deep Reinforcement Learning(DRL)algorithm for real-time and proactive trajectory planning and *** also fulfills QoS-aware service provisioning.A greedypolicy approach is used to maximize the long-term reward for serving more users *** results reveal the superiority of the proposed DRL mechanism for energy-efficient and QoS-aware trajectory planning over the existing models.

关键词： multi-access edge computing UAVs trajectory planning QoS assurance reinforcement learning deep Q network

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Secure and Efficient Coded multi-access edge computing With Generalized Graph Neural Networks

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IEEE TRANSACTIONS ON MOBILE computing 2023年第9期22卷 5504-5524页

作者： Asheralieva, Alia Niyato, Dusit Southern Univ Sci & Technol Res Inst Trustworthy Autonomous Syst Dept Comp Sci & Engn Shenzhen 518055 Guangdong Peoples R China Nanyang Technol Univ Sch Comp Sci & Engn Singapore 639798 Singapore

We formulate a novel framework to improve security and utility of the coded multi-access edge computing (MEC) network for Internet of Things (IoT) applications where multiple edge servers (ESs) jointly process raw IoT data to obtain the final network output. To correctly recover the final output even when some processing outputs produced by malicious or malfunctioning ESs are erroneous, the network utilizes coded distributed computing (CDC) that enhances security by adding computational redundancy to the data processed by ESs. Within the framework, we propose an advanced approach to address limitations of contemporary CDC-based systems related to their inability to guarantee security when the number of malicious ESs is large and reduced network utility due to redundant computations. In this approach, the processing loads are allocated to ESs based on deep learning (DL) algorithms to identify the unknown ESs' types (faithful or malicious) and minimize the load of malicious ESs, thereby optimizing security and utility. The proposed DL algorithms adopt the message passing neural network (NN) - a generalized graph NN with lower complexity and faster convergence than conventional NNs. We prove that our framework yields the optimal security and utility, and verify its superior performance compared with the state-of-the-art schemes.

关键词： Coded distributed computing computational offloading deep learning graph neural networks Internet of Things Lagrange inter-polation machine learning multi-access edge computing resource allocation security

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Resource Allocation and Computation Offloading for multi-access edge computing With Fronthaul and Backhaul Constraints

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2021年第8期70卷 8037-8049页

作者： Chen, Jun Chang, Zheng Guo, Xijuan Li, Renchuan Han, Zhu Hamalainen, Timo Yanshan Univ Coll Informat Sci & Engn Qinhuangdao 066004 Hebei Peoples R China Natl Def Univ Dept Joint Serv Beijing Peoples R China Univ Jyvaskyla Fac Informat Technol FIN-40014 Jyvaskyla Finland Univ Houston Dept Elect & Comp Engn Houston TX 77004 USA Kyung Hee Univ Dept Comp Sci & Engn Seoul 446701 South Korea

edge computing is able to provide proximity solutions for the future wireless network to accommodate different types of devices with various computing service demands. Meanwhile, in order to provide ubiquitous connectivities to massive devices over a relatively large area, densely deploying remote radio head (RRH) is considered as a cost-efficient solution. In this work, we consider a vertical and heterogeneous multi-access edge computing system. In the system, the RRHs are deployed for providing wireless access for the users and the edge node with computing capability can process the computation requests from the users. With the objective to minimize the total energy consumption for processing the computation task, a joint radio resource allocation and offloading decision optimization problem is presented under the explicit consideration of capacity constraints of fronthaul and backhaul links. Due to the non-convexity of the formulated problem, we divide the original problem into several sub-problems and address them accordingly to find the optimal solution. Extensive simulation studies are conducted and illustrated to evaluate the advantages of the proposed scheme.

关键词： Task analysis Resource management Delays Mobile handsets Energy consumption Optimization Computational modeling multi-access edge computing fronthaul backhaul link offloading resource allocation

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New Improved multi-Objective Gorilla Troops Algorithm for Dependent Tasks Offloading problem in multi-access edge computing

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JOURNAL OF GRID computing 2023年第2期21卷 1-24页

作者： Hosny, Khalid M. Awad, Ahmed I. Khashaba, Marwa M. Mohamed, Ehab R. Zagazig Univ Fac Comp & Informat Dept Informat Technol Zagazig 44519 Egypt

Computational offloading allows lightweight battery-operated devices such as IoT gadgets and mobile equipment to send computation tasks to nearby edge servers to be completed, which is a challenging problem in the multi-access edge computing (MEC) environment. Numerous conflicting objectives exist in this problem;for example, the execution time, energy consumption, and computation cost should all be optimized simultaneously. Furthermore, offloading an application that consists of dependent tasks is another important issue that cannot be neglected while addressing this problem. Recent methods are single objective, computationally expensive, or ignore task dependency. As a result, we propose an improved Gorilla Troops Algorithm (IGTA) to offload dependent tasks in the MEC environments with three objectives: 1-Minimizing the execution latency of the application, 2-energy consumption of the light devices, 3-the used cost of the MEC resources. Furthermore, it is supposed that each MEC supports many charge levels to provide more flexibility to the system. Additionally, we have extended the operation of the standard Gorilla Troops Algorithm (GTO) by adopting a customized crossover operation to improve its search strategy. A Max-To-Min (MTM) load-balancing strategy was also implemented in IGTA to improve the offloading operation. Relative to GTO, IGTA has reduced latency by 33%, energy consumption by 93%, and cost usage by 34.5%. We compared IGTA with other Optimizers in this problem, and the results showed the superiority of IGTA.

关键词： multi-access edge computing Computational offloading multi-objective gorilla troops Dynamic needs Task dependency

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