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

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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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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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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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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Large-Scale Computation Offloading Using a multi-Agent Reinforcement Learning in Heterogeneous multi-access edge computing

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IEEE TRANSACTIONS ON MOBILE computing 2023年第6期22卷 3425-3443页

作者： Gao, Zhen Yang, Lei Dai, Yu Northeastern Univ Sch Comp Sci & Engn Shenyang 110819 Liaoning Peoples R China Northeastern Univ Sch Coll Software Shenyang 110819 Liaoning Peoples R China

Recently, existing computation offloading methods have provided extremely low service latency for mobile users (MUs) in multi-access edge computing (MEC). However, this remains a challenge in large-scale mixed cooperative-competitive MUs heterogeneous MEC environments. Moreover, existing methods focus more on all offloaded tasks handled by static resource allocation MEC servers (ESs) within a time interval, ignoring on-demand requirements of heterogeneous tasks, resulting in many tasks being dropped or wasting resources, especially for latency-sensitive tasks. To address these issues, we present a decentralized computation offloading solution based on the Attention-weighted Recurrent multi-Agent Actor-Critic (ARMAAC). First, we design a recurrent actor-critic framework to assist MU agents in remembering historical resource allocation information of ESs to better understand the future state of ESs, especially in dynamic resource allocation. Second, an attention mechanism is introduced to compress the joint observation space dimension of all MUs agent to adapt to large-scale MUs. Finally, the actor-critic framework with double centralized critics and Dueling network is redesigned considering the instability and convergence difficulties caused by the sensitive relationship between the actor and critic networks. The experiments show that ARMAAC improves task completion rates and reduces average system cost by 11.01%$\sim$& SIM;14.03% and 10.45%$\sim$& SIM;15.56% compared with baselines.

关键词： Large-scale computation offloading multi-access edge computing multi-agent reinforcement learning (MARL) recurrent multi-agent actor-critic attention mechanism

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Platform Profit Maximization in D2D Collaboration Based multi-access edge computing

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2023年第7期22卷 4282-4295页

作者： Huang, Xiaoyao Ji, Guoliang Zhang, Baoxian Li, Cheng Univ Chinese Acad Sci Res Ctr Ubiquitous Sensor Networks Beijing 100049 Peoples R China Mem Univ Fac Engn & Appl Sci St John NF A1B 3X5 Canada

multi-access edge computing (MEC) has been an important and promising paradigm for offering computing services to mobile users with computation-intensive and latency-critical tasks. In this paper, we study a D2D collaboration based MEC system, where the service platform purchases resources from resource-rich collaborative D2D devices when the task arrival rate exceeds the platform's capability for providing satisfactory QoS. The design objective is to maximize the platform profit while maximally satisfying the delay requirements of tasks. We define delay based utility functions for different participants and accordingly formulate the platform profit maximization problem as a Mixed Integer Non-Linear Programming (MINLP) problem. For the online case where future task arrivals are unknown in advance, we propose a reverse auction based task assignment and urgency-value based transmission scheduling algorithm (RAGM). We present the detailed algorithm design and deduce its computation complexity. We prove that RAGM satisfies individual rationality of all participants. We conduct extensive simulations and the results show the high performance of RAGM as compared with benchmark algorithms.

关键词： Task analysis Device-to-device communication Collaboration Delays Quality of service Servers Costs multi-access edge computing D2D assisted network resource management reverse auction profit maximization

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Accurate and Reliable Service Recommendation Based on Bilateral Perception in multi-access edge computing

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IEEE TRANSACTIONS ON SERVICES computing 2023年第2期16卷 886-899页

作者： Liu, Zhizhong Sheng, Quan Z. Zhang, Zhenxing Xu, Xiaofei Chu, Dianhui Yu, Jian Wang, Shuang Yantai Univ Sch Comp & Control Engn Yantai 264005 Peoples R China Macquarie Univ Sch Comp Sydney NSW 2109 Australia Henan Polytech Univ Coll Comp Sci Technol Jiaozuo 454000 Peoples R China Harbin Inst Technol Coll Comp Sci & Technol Weihai 264209 Peoples R China Auckland Univ Technol Dept Comp Sci Auckland 1142 New Zealand Southeast Univ Sch Comp Sci & Engn Nanjing 211189 Peoples R China

multi-access edge computing (MEC) is an emerging computing paradigm that brings services from the centralized cloud to nearby network edge to improve users' Quality of Experience (QoE). As massive services with dynamic Quality of Service (QoS) are available in MEC, it becomes challenging for users to find reliable services that satisfy their needs. Therefore, service recommendation technology is urgently needed in MEC. Although existing service recommendation methods work well on recommending popular services that users might be interested in, they fail to recommend services with reliable QoS in the MEC environment. To tackle this issue, an accurate and reliable service recommendation (ARSR) approach based on bilateral perception is proposed, which aims to proactively recommend reliable services by perceiving both users' service demands and multi-QoS of candidate services. ARSR consists of three main steps. First, a user's service demand is estimated by a context-aware service demand prediction method based on an improved online deep learning model. Then, multiple QoS attributes of candidate services are forecasted by a multidimensional contexts-aware QoS prediction method based on an improved multi-task deep neural network. Finally, the optimal service is recommended to the user based on the predicted QoS. Extensive experiments have been carried out to verify the proposed approach and to prove its performance superiority.

关键词： multi-access edge computing service recommendation bilateral perception service demand prediction QoS prediction

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6G-Empowered Offloading for Realtime Applications in multi-access edge computing

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IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 2023年第3期10卷 1311-1325页

作者： Huang, Hui Ye, Qiang Zhou, Yitong Dalhousie Univ Fac Comp Sci Halifax NS B3H 4R2 Canada

multi-access edge computing (MEC) is a promising solution to the resource shortage problem on mobile devices. With MEC, a fraction of the computational tasks on mobile devices could be offloaded to edge servers. Over the past years, a series of machine learning based offloading methods for MEC have been proposed to reduce the completion time of computational tasks. However, most of the existing methods do not work well for realtime applications, which involve tasks with rigorous deadline constraints. In addition, offloading data-intensive tasks via the latest wireless networks, such as LTE and 5G, could lead to unsatisfactory transmission delays. Furthermore, with the state-of-the-art learning-based methods, both the training and inference operation of the learning algorithm are carried out on mobile devices, undesirably leaving less computation resources for computational tasks on mobile devices. In this paper, we propose a 6G-empowered learning-based offloading scheme, MELO, which can be used to make appropriate offloading decisions for realtime tasks. Specifically, the task offloading problem is first formulated as a Markov Decision Process. Thereafter, the problem is solved with a Reinforcement Learning (RL) algorithm, TD3. In addition, 6G is adopted as the communication infrastructure to sufficiently support the data transfer between mobile devices and edge servers. Furthermore, to leave more resources on mobile devices, we devise a novel learning architecture, EALA. With EALA, the training and inference operation of a learning algorithm are decoupled. The training operation is carried out on edge servers while the inference operation is performed on mobile devices. Our experimental results indicate that MELO outperforms the existing offloading methods in terms of task completion time.

关键词： Task analysis Mobile handsets Servers 6G mobile communication Computational modeling Training Delays 6G multi-access edge computing Realtime Applications Offloading edge-assisted Learning

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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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Task Offloading in multi-access edge computing Enabled UAV-Aided Emergency Response Operations

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IEEE access 2023年 11卷 23167-23188页

作者： Akter, Shathee Kim, Dae-Young Yoon, Seokhoon Univ Ulsan Dept Elect Elect & Comp Engn Ulsan 44610 South Korea Soonchunhyang Univ Dept Comp Software Engn Asan 31538 South Korea

In emergency response operations, using uncrewed aerial vehicles (UAVs) has recently become a promising solution due to their flexibility and easy deployment. However, tasks performed by the UAVs, e.g., object detection and human pose recognition, usually require a high computation capacity and energy supply. Furthermore, offloading tasks to the edge server-equipped base stations may not always be possible because of a lack of infrastructure or distance. Therefore, UAV-aided edge servers can be deployed near UAV scouts to provide computing services. However, a UAV can not perform all types of tasks since it has limitations on memory, available software, central processing unit (CPU), and graphics processing unit (GPU) capacity. Therefore, this study focuses on task offloading (TO), power, and computation resource allocation (PRA) problems in a multi-layer MEC-enabled UAV network while taking into account CPU and GPU requirements of tasks, the capacity of the devices (i.e., computational resources, power, and energy), and limitations on the type of tasks a UAV can perform. The problem is formulated as a non-convex mixed-integer nonlinear problem to minimize the weighted sum of the maximum energy consumption ratio in the network and total task execution latency ratio, and then decomposed and converted into an integer and a convex problem. A messy genetic algorithm (mGA)-based TO and PRA strategy (mGA-TPR) is proposed to solve the problem, where two PRA strategies are based on the Karush-Kuhn-Tucker conditions used to solve the PRA problem. Simulation results verify that the proposed scheme can outperform the baseline methods.

关键词： multi-access edge computing task offloading resource allocation messy genetic algorithm

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