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A Deep Reinforcement Learning-Based Offloading Scheme for multi-access edge computing-Supported eXtended Reality Systems

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2023年第1期72卷 1254-1264页

作者： Trinh, Bao Muntean, Gabriel-Miro Dublin City Univ Insight SFI Res Ctr Data Analyt Sch Elect Engn Dublin 9 Ireland Dublin City Univ Sch Elect Engn Performance Engn Lab Dublin 9 Ireland

In recent years, eXtended Reality (XR) applications have been widely employed in various scenarios, e.g., health care, education, manufacturing, etc. Such application are now easily accessible via mobile phones, tablets, or wearable devices. However, such devices normally suffer from constraints in terms of battery capacity and processing power, limiting the range of applications supported or lowering Quality of Experience. One effective way to address these issues is to offload the computation tasks to the edge servers that are deployed at the network edges, e.g., base stations or WiFi access point, etc. This communication fashion, also named as multi-access edge computing (MEC), is proposed to overcome the limitations in terms of long latency due to long propagation distance of traditional cloud computing approach. XR devices, that are limited in computation resources and energy, can then benefit from offloading the computation intensive tasks to MEC servers. However, as XR applications are comprised of multiple tasks with variety of requirements in terms of latency and energy consumption, it is important to make decision whether one task should be offloaded to MEC server or not. This paper proposes a Deep Reinforcement Learning-based offloading scheme for XR devices (DRLXR). The proposed scheme is used to train and derive the close-to optimal offloading decision whereas optimizing a utility function optimization equation that considers both energy consumption and execution delay at XR devices. The simulation results show how our proposed scheme outperforms the other counterparts in terms of total execution latency and energy consumption.

关键词： X reality Cloud computing Servers Task analysis Mobile handsets Energy consumption Optimization Deep reinforcement learning energy efficiency eXtended reality multi-access edge computing offloading quality of service

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Deep Reinforcement Learning-Based Computation Offloading for 5G Vehicle-Aware multi-access edge computing Network

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China Communications 2021年第11期18卷 26-41页

作者： Ziying Wu Danfeng Yan State Key Laboratory of Network and Switching Technology Beijing University of Posts and TelecommunicationsBeijing 100876China

multi-access edge computing(MEC)is one of the key technologies of the future 5G *** deploying edge computing centers at the edge of wireless access network,the computation tasks can be offloaded to edge servers rather than the remote cloud server to meet the requirements of 5G low-latency and high-reliability application ***,with the development of IOV(Internet of Vehicles)technology,various delay-sensitive and compute-intensive in-vehicle applications continue to *** with traditional Internet business,these computation tasks have higher processing priority and lower delay *** this paper,we design a 5G-based vehicle-aware multi-access edge computing network(VAMECN)and propose a joint optimization problem of minimizing total system *** view of the problem,a deep reinforcement learningbased joint computation offloading and task migration optimization(JCOTM)algorithm is proposed,considering the influences of multiple factors such as concurrent multiple computation tasks,system computing resources distribution,and network communication ***,the mixed integer nonlinear programming problem is described as a Markov Decision *** show that our proposed algorithm can effectively reduce task processing delay and equipment energy consumption,optimize computing offloading and resource allocation schemes,and improve system resource utilization,compared with other computing offloading policies.

关键词： multi-access edge computing computation offloading 5G vehicle-aware deep reinforcement learning deep q-network

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MECInOT: a multi-access edge computing and industrial internet of things emulator for the modelling and study of cybersecurity threats

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JOURNAL OF SUPERcomputing 2023年第11期79卷 11895-11933页

作者： Ruiz-Villafranca, Sergio Carrillo-Mondejar, Javier Gomez, Juan Manuel Castelo Roldan-Gomez, Jose Univ Castilla La Mancha Campus Univ S-N Albacete 02006 Spain Univ Zaragoza Zaragoza Spain Univ Oviedo Dept Comp Sci Gijon Spain

In recent years, the Industrial Internet of Things (IIoT) has grown rapidly, a fact that has led to an increase in the number of cyberattacks that target this environment and the technologies that it brings together. Unfortunately, when it comes to using tools for stopping such attacks, it can be noticed that there are inherent weaknesses in this paradigm, such as limitations in computational capacity, memory and network bandwidth. Under these circumstances, the solutions used until now in conventional scenarios cannot be directly adopted by the IIoT, and so it is necessary to develop and design new ones that can effectively tackle this problem. Furthermore, these new solutions must be tested in order to verify their performance and viability, which requires testing architectures that are compatible with newly introduced IIoT topologies. With the aim of addressing these issues, this work proposes MECInOT, which is an architecture based on openLEON and capable of generating test scenarios for the IIoT environment. The performance of this architecture is validated by creating an intelligent threat detector based on tree-based algorithms, such as decision tree, random forest and other machine learning techniques. Which allows us to generate an intelligent and to demonstrate, we could generate an intelligent threat detector and demonstrate the suitability of our architecture for testing solutions in IIoT environments. In addition, by using MECInOT, we compare the performance of the different machine learning algorithms in an IIoT network. Firstly, we present the benefits of our proposal, and secondly, we describe the emulation of an IIoT environment while ensuring the repeatability of the experiments.

关键词： Industrial internet of things multi-access edge computing Machine learning Cybersecurity

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Online Optimal Service Selection, Resource Allocation and Task Offloading for multi-access edge computing: A Utility-Based Approach

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IEEE TRANSACTIONS ON MOBILE computing 2023年第7期22卷 4150-4167页

作者： Chu, Weibo Yu, Peijie Yu, Zhiwen Lui, John C. S. Lin, Yi Northwestern Polytech Univ Xian 710060 Shaanxi Peoples R China Chinese Univ Hong Kong Shatin Hong Kong Peoples R China

multi-access edge computing promises satisfactory user experience by offloading tasks to the MEC server deployed at the network edge. However, since the MEC server is often resource-limited as compared to the cloud infrastructure, how to efficiently utilize its resources for system performance optimization becomes a challenge. In this paper, we study this problem with the aim at maximizing user's QoE through jointly optimizing service selection, computation resource allocation and task offloading decision, which is less studied in existing literature. We formulate a mixed-integer nonlinear programming problem (MINLP) for the task and propose a utility-based approach together with a low-complexity resource-efficiency based heuristic to address the problem. We consider realistic settings, where centralized solutions may not apply and an optimal mechanism needs to adapt as system operates. A distributed algorithm based on the Lagrangian-dual based decomposition theory is proposed, and we prove all sub-problems derived can be efficiently solved. In line with the current VM technology, we develop a cost-aware online algorithm that explicitly incorporates the cost of service switches into service selection and resource allocation. We evaluate our mechanism through both synthetic and trace-driven simulations, and results indicate they are effective as compared to representative baseline algorithms.

关键词： Task analysis Servers Resource management Quality of experience System performance Computational modeling multi-access edge computing service selection computation resource allocation task offloading online algorithm

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Time Efficient Offloading Optimization in Automotive multi-access edge computing Networks Using Mean-Field Games

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2023年第5期72卷 6460-6473页

作者： Kang, Yuhan Wang, Haoxin Kim, BaekGyu Xie, Jiang Zhang, Xiao-Ping Han, Zhu Univ Houston Elect & Comp Engn Dept Houston TX 77004 USA Georgia State Univ Dept Comp Sci Atlanta GA 30303 USA Daegu Gyeongbuk Inst Sci & Technol Dept Elect Engn & Comp Sci Daegu 42988 South Korea Univ N Carolina Dept Elect & Comp Engn Charlotte NC 28223 USA Ryerson Univ Dept Elect Comp & Biomed Engn Toronto ON M5B 2K3 Canada Kyung Hee Univ Dept Comp Sci & Engn Seoul 446701 South Korea

Emerging connected vehicular services, such as intelligent driving and high-definition (HD) map, are gaining increasing interest with the fast development of multi-access edge computing (MEC). For most time-sensitive and computation-intensive vehicular services, the data offloading process significantly influences the capacity and performance of MEC, especially when the number of connected vehicles is enormous. In this work, we consider data offloading optimization for a large-scale automotive MEC network. The problem is challenging due to the large number of connected vehicles and the complicated interaction between vehicles and edge servers. To tackle the scalability problem, we reformulate the original offloading optimization problem into a Mean-Field-Game (MFG) problem by abstracting the interaction among the connected vehicles as a distribution over their state spaces of task sizes, known as the mean-field term. To solve the problem efficiently, we propose a G-prox Primal-Dual-Hybrid-Gradient (PDHG) algorithm that transforms the MFG problem into a saddle-point problem. Based on our developed MFG model and G-prox PDHG algorithm, we propose the first data offloading scheme whose computation time is independent of the number of connected vehicles in automotive MEC systems. Extensive evaluation results corroborate the superior performance of our proposed scheme compared with the state-of-the-art methods.

关键词： Task analysis Connected vehicles Servers Automotive engineering Optimization Sensors Computational modeling Mean-field game task offloading multi-access edge computing connected vehicles

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Joint task offloading and resource allocation in vehicle-assisted multi-access edge computing

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COMPUTER COMMUNICATIONS 2021年 177卷 77-85页

作者： Xue, Jianbin Hu, Qingchun An, Yaning Wang, Lu Lanzhou Univ Technol Sch Comp & Commun Lanzhou Peoples R China

multi-access edge computing (MEC) has significant advantages in improving resource efficiency of Internet of Things (IoT) and 5G networks, however its limited resources cannot meet the demand of data communication and computation capability during off-peak time. Incentivizing intelligent vehicles with idle computation resources as vehicle edge nodes (VENs) to provide computation offloading for nearby user equipments (UEs) is an appealing idea. Thus, we propose a vehicle-assisted MEC (VMEC) paradigm, where tasks can be offloaded to MEC server and VENs. In this paper, we first establish a differentiated pricing model based on different states of resources and a dynamic incentive model according to the demands of UEs. Then, we formulate a Stackelberg game between UEs and MEC service provider (MEC SP) to obtain the optimal offloading strategy and pricing scheme. A gradient-based resource allocation iteration algorithm (GRAIA) is designed for the Nash equilibrium solution. Finally, considering the matching between UEs and vehicles, we present a reverse auction-based task scheduling algorithm (RATSA) to choose VENs. The simulation results demonstrate that the proposed scheme can achieve significant performance improvement and is superior to the existing schemes in improving system utility.

关键词： multi-access edge computing Internet of vehicles Resource allocation Dynamic incentives Stackelberg game

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A multi-access edge computing enabled framework for the construction of a knowledge-sharing intelligent machine tool swarm in Industry 4.0

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JOURNAL OF MANUFACTURING SYSTEMS 2023年 66卷 56-70页

作者： Zhang, Chao Zhou, Guanghui Li, Jingjing Chang, Fengtian Ding, Kai Ma, Dongxu Xi An Jiao Tong Univ Sch Mech Engn Xian 710049 Peoples R China Xi An Jiao Tong Univ State Key Lab Mfg Syst Engn Xian 710054 Peoples R China Changan Univ Sch Construct Machinery Xian 710064 Peoples R China

Developing intelligent machine tools has been front and center for manufacturing enterprises to take a step towards intelligent manufacturing in Industry 4.0, which has attracted increasing attention from both academics and industry. Nevertheless, most current approaches focus on the construction of a single digital twin machine tool with limited intelligence due to the lack of data and knowledge accumulated by that machine tool for decision-making support. Consequently, this paper integrates digital twin with multi-access edge computing (MEC) and proposes a novel framework for the construction of a knowledge-sharing intelligent machine tool swarm that supports the secure knowledge sharing across the authorized machine tools in the swarm with ultra -low latency performance. Then, three key enabling methodologies of the framework are introduced from the perspective of digital twin machine tool swarm construction, knowledge-based cloud brain learning, and MEC-enhanced system deployment. Finally, a prototype system is implemented, where its application examples and evaluation experiments demonstrate the feasibility and effectiveness of the proposed approach.

关键词： multi-access edge computing Digital twin Intelligent machine tool Industry 4 0 Knowledge sharing

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Optimal Slicing of Virtualized Passive Optical Networks to Support Dense Deployment of Cloud-RAN and multi-access edge computing

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IEEE NETWORK 2022年第2期36卷 131-138页

作者： Das, Sandip Slyne, Frank Ruffini, Marco Trinity Coll Dublin CONNECT Ctr Dublin Ireland Trinity Coll Dublin CONNECT Dublin Ireland Trinity Coll Dublin Dublin Ireland

The commercialization of Cloud-RAN, and Open-RAN in particular, is a key factor to enable 5G cell densification by providing lower cost and more agile deployment of small cells. In addition, the adoption of multi-access edge computing (MEC) is important to support the ultra-low latency and high reliability required by mission-critical applications, which constitute a milestone of the 5G and beyond vision of a fully connected society. However, connecting antenna site, C-RAN processing, and MEC at low cost is challenging, as it requires high-capacity, low-latency connectivity delivered through a highly inter-connected topology. While passive optical networks (PONs) are being considered as a solution for providing low-cost connectivity to C-RAN, they only allow data transmission from the endpoints (e.g., hosting the radio unit, RU, at the antenna site) toward a central node (e.g., the central office, hosting computing equipment), and thus cannot support traffic from RUs toward MEC end nodes that could host the distributed unit (DU) and possibly central unit (CU) and network core. This has led to research into the evolution of PON architectures with the ability to provide direct communications between endpoints, thus supporting mesh traffic patterns required by MEC installations. In this context, virtualization plays a key role in enabling efficient resource allocation (i.e., optical transmission capacity) to endpoints, according to their communication patterns. In this article, we address the challenge of dynamic allocation of virtual PON slices over mesh-PON architectures to support C-RAN and MEC nodes. We make use of a mixed analytical-iterative model to compute optimal virtual PON slice allocation, with the objective of minimizing the use of MEC node resources, while meeting a target latency threshold (100 mu s in our scenario). Our method is particularly effective in reducing computation time, enabling virtual PON slice allocation in timescales compatible with real

关键词： multi-access edge computing Costs Microprocessors Computational modeling Computer architecture Real-time systems Passive optical networks Radio access networks Cloud computing

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Coopedge plus : Enabling Decentralized, Secure and Cooperative multi-access edge computing Based on Blockchain

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IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS 2023年第3期34卷 894-908页

作者： Yuan, Liang He, Qiang Tan, Siyu Li, Bo Yu, Jiangshan Chen, Feifei Yang, Yun Swinburne Univ Technol Dept Comp Technol Melbourne Vic 3122 Australia Huazhong Univ Sci & Technol Sch Comp Sci & Technol Natl Engn Res Ctr Big Data Technol & Syst Serv Comp Technol & Syst LabCluster & Grid Comp Wuhan 430074 Hubei Peoples R China Southeast Univ Sch Comp Sci & Engn Nanjing Peoples R China Monash Univ Fac Informat Technol Melbourne Vic 3800 Australia Deakin Univ Sch Informat Technol Geelong Vic 3220 Australia

multi-access edge computing (MEC) has emerged as a new distributed computing paradigm for its ability to offer low-latency services to users. Suffering from constrained computational resources because of their limited physical sizes, edge servers usually cannot handle all the incoming compute tasks on time when they operate independently. Thus, they need to cooperate by peer-offloading. Incentive and trust are the two major challenges towards to cooperative computing among edge servers operating in a distrusted environment. Another specific challenge in the MEC environment is to facilitate incentive and trust in a decentralized manner. This article proposes Coopedge+, a novel blockchain-based decentralized platform, to drive and support cooperative multi-access edge computing to tackle these challenges in a systematic manner. On Coopedge+, an edge server can publish a compute task for other edge servers to contend for. A winner is selected from candidate edge servers as the task executor based on their reputation to perform the compute task. After that, Coopedge+ employs a random leader election scheme to elect a task recorder without revealing its leadership until its consensus epoch. The task recorder will coordinate a consensus among edge servers to record the task executor's performance on blockchain. We implement Coopedge+ based on Hyperledger fabric and evaluate it experimentally against a baseline implementation and three state-of-the-art implementations in a simulated MEC environment. The results validate the usefulness of Coopedge+ and demonstrate its performance.

关键词： Blockchain cooperative multi-access edge computing distributed consensus multi-access edge computing peer offloading

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Cooperative Computation Offloading for multi-access edge computing in 6G Mobile Networks via Soft Actor Critic

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IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 2024年第6期11卷 5601-5614页

作者： Sun, Chuan Wu, Xiongwei Li, Xiuhua Fan, Qilin Wen, Junhao Leung, Victor C. M. Chongqing Univ Key Lab Dependable Serv Comp Cyber Phys Soc Minist Educ Chongqing Peoples R China Chongqing Univ Sch Big Data & Software Engn Chongqing 401331 Peoples R China Chinese Univ Hong Kong Dept Elect Engn Shatin Hong Kong Peoples R China Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen 518060 Peoples R China Univ British Columbia Dept Elect & Comp Engn Vancouver BC V6T 1Z4 Canada

Driven by numerous emerging mobile servicesand applications, multi-access edge computing (MEC) is regarded as a promising technique to alleviate core network congestion and reduce service latency for massive Internet of Things (IoT) over 6G mobile networks. However, the infrastructure of conventional MEC suffers from the lack of cloud server (CS) or cooperation of multiple edge servers (ESs), rendering it less capable of handling large-scale computation tasks in the ultra-dense smart environments. This paper investigates the issues of cooperative computation offloading for MEC in the 6G era. The proposed MEC system enables edge-cloud and edge-edge cooperation to address the limitations of single ES and the nonuniform distribution of computation task arrivals among multiple ESs. To support low-latency services, we model the cooperative computation offloading problem as a Markov decision process, and propose two intelligent computation offloading algorithms based on Soft Actor Critic (SAC), i.e., centralized SAC offloading and decentralized SAC offloading. Evaluation results show that the proposed algorithms outperform the existing computation offloading algorithms in terms of service latency.

关键词： Task analysis 6G mobile communication edge computing Cloud computing Computer architecture Servers Resource management multi-access edge computing 6G cooperative computation offloading service latency Soft Actor Critic.

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