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cooperative computation offloading in FiWi Enhanced 4G HetNets Using Self-Organizing MEC

IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2020年第7期19卷 4480-4493页

作者： Ebrahimzadeh, Amin Maier, Martin INRS Opt Zeitgeist Lab Montreal PQ H5A 1K6 Canada Concordia Univ Telecommun Serv Engn Res Lab Montreal PQ H36 1M8 Canada

Multi-access edge computing (MEC) is an emerging paradigm to meet the rapidly growing computation demands of mobile applications. This paper investigates the performance gains of cooperative computation offloading for MEC enabled FiWi enhanced HetNets with capacity-limited backhaul links. After presenting the envisioned two-tier MEC architecture for a FiWi based networking infrastructure, we propose a simple but efficient offloading strategy, which relies on the flexible trilateral cooperation between end-device, edge servers, and the remote cloud. We then present an analytical framework to estimate the average response time and energy consumption of mobile users for various offloading scenarios with different wireless access modes (i.e., WiFi and 4G LTE-A). The presented analysis flexibly allows for incorporating both offloaded and conventional human-to-human (H2H) traffic of mobile users as well as fixed (wired) subscribers. Finally, we present our self-organization based mechanism, which enables mobile users to make suitable energy-delay trade-offs by jointly minimizing the average task execution time and energy consumption, using only their local information. The obtained results demonstrate the feasibility of the proposed cooperative self-organizing offloading strategy and its superior performance over schemes with MEC- or cloud-only offloading strategies.

关键词： Servers Wireless communication Task analysis Mobile handsets Computer architecture Cloud computing Energy consumption cooperative computation offloading FiWi enhanced 4G HetNets multi-access edge computing

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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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cooperative computation offloading combined with data compression in mobile edge computing system

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JOURNAL OF SUPERCOMPUTING 2023年第12期79卷 13490-13518页

作者： Li, Hongjian Li, Dongjun Zhang, Xue Sun, Hu Chongqing Univ Posts & Telecommun Dept Comp Sci & Technol Chongqing 400065 Peoples R China

cooperative computation offloading (CCO) is a technique to improve computation offloading performance in edge networks through collaboration between edge nodes. CCO can achieve better resource utilization, balance the computational load and further reduce delay to improve the service experience of user equipment (UE). In this paper, we investigate the problem of collaborative computing task offloading scheme and computing resource allocation in mobile edge computing and propose a data compression cooperation computing offloading (DCCO) scheme. To reduce the amount of data transmitted on the UE offloading link, we introduce a Data Compression into CCO and present the computational offloading strategy, collaborative offloading and computational resource allocation problems with the goal of minimizing the weighted sum of delay and energy consumption of the UE under the constraints of UE delay and energy consumption. And an improved genetic algorithm is proposed to solve the problem which is a non-convex mixed-integer problem with binary and continuous variables. The offloading strategy and computational resource allocation correspond to the genes in the genetic algorithm chromosome. The simulation results show that the DCCO scheme can reduce the offloading cost up to 11% compared with the existing schemes. It effectively improves the computation offloading performance of edge network computing.

关键词： Mobile edge computing cooperative computation offloading Genetic algorithm Data compression

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cooperative computation offloading and resource allocation for delay minimization in mobile edge computing*

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JOURNAL OF SYSTEMS ARCHITECTURE 2021年 118卷

作者： Kuang, Zhufang Ma, Zhihao Li, Zhe Deng, Xiaoheng Cent South Univ Forestry & Technol Sch Comp & Informat Engn Changsha 410004 Peoples R China Cent South Univ Sch Comp Sci & Engn Changsha 410010 Peoples R China

Mobile edge computing (MEC) is a promising paradigm, which brings computation resources in proximity to mobile devices and allows the tasks of mobile devices to be offloaded to MEC servers with low latency. The joint problem of cooperative computation task offloading and resource allocation is a challenging issue. The joint problem of cooperative computation task offloading scheme and resource assignment in MEC is investigated in this paper, where the vertical cooperation among mobile devices, mobile edge server nodes and mobile cloud server nodes is considered, and the horizontal computation cooperation between edge nodes is considered as well. A computation offloading decision, cooperative selection, power allocation and CPU cycle frequency assignment problem is formulated. The objective is to minimize the latency while guaranteeing the constraint of transmission power, energy consumption and CPU cycle frequency. The formulated latency optimization problem is a nonconvex mixed-integer problem in general, which has binary variables and continuous variables. In order to solve the formulated problem. A joint iterative algorithm based on the Lagrangian dual decomposition, ShengJin Formula method, and monotonic optimization method is proposed. The CPU cycle frequence allocation is handled by the ShengJin Formula method due to the cubic equation of one variable about the CPU frequence allocation. The transmission power assignment is handled by the monotonic optimization method. In the algorithm convergence with different number of tasks, the proposed algorithm can quickly and effectively reach the convergence state and getting the minimum task execution delay. Numerical results demonstrate that the proposed algorithm outperforms the Full MEC, Full Local and Full Cloud three schemes in terms of execution latency.

关键词： Mobile edge computing cooperative computation offloading Delay minimization Resource allocation

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NOMA-Enabled cooperative computation offloading for Blockchain-Empowered Internet of Things: A Learning Approach

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IEEE INTERNET OF THINGS JOURNAL 2021年第4期8卷 2364-2378页

作者： Li, Zhenni Xu, Minrui Nie, Jiangtian Kang, Jiawen Chen, Wuhui Xie, Shengli Guangdong Univ Technol Sch Automat Guangzhou 510006 Peoples R China Guangdong Univ Technol Guangdong Key Lab IoT Informat Technol Guangzhou 510006 Peoples R China Sun Yat Sen Univ Sch Data & Comp Sci Guangzhou 510006 Peoples R China Nanyang Technol Univ Energy Res Inst NTU Singapore Singapore Guangdong Univ Technol Guangdong HongKong Macao Joint Lab Smart Discret Guangzhou 510006 Peoples R China Guangdong Univ Technol Ctr Intelligent Batch Mfg Based IoT Technol 111 Guangzhou 510006 Peoples R China

Blockchain technologies allow the Internet of Things (IoT) to build trust among various interest parties. For the resource-limited IoT devices, offloading computation-intensive tasks (blockchain verification and mining tasks, and data process tasks) to edge servers for execution is considered as a promising solution in mobile-edge computing. However, conventional methods (such as linear programming or game theory) for the computation offloading problem cannot achieve long-term performance while the existing deep reinforcement learning (DRL)-based algorithms suffer from slow convergence, lack of robustness, and unstable performance. In this article, we propose a multiagent DRL framework to achieve long-term performance for cooperative computation offloading, in which a scatter network is adopted to improve its stability and league learning is introduced for agents to explore the environment collaboratively for fast convergence and robustness. First, we study the nonorthogonal multiple access-enabled cooperative computation offloading problem and formulate the joint problem as a Markov decision process by considering both the blockchain mining tasks and data processing tasks. Second, to avoid useless exploration and unstable performance, we initially train an intelligent agent represented by scatter networks using conventional expert strategies. Third, in order to enhance the performance, we subsequently establish a hierarchical league where agents collaborate with others to explore the environment. Finally, our experimental results demonstrate that our algorithm could perform better in terms of reducing energy cost and delay cost, and shortening almost 60% of the training time compared with the state-of-the-art approaches.

关键词： Internet of Things Intelligent agents Heuristic algorithms Task analysis Convergence Training Blockchain cooperative computation offloading deep reinforcement learning (DRL) edge computing

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cooperative Multiagent Deep Reinforcement Learning for computation offloading in Digital Twin Cyber-Physical-Social System

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IEEE TRANSACTIONS ON computationAL SOCIAL SYSTEMS 2025年

作者： Peng, Ziyu Xu, Yang Kang, Runyu Liu, Yaqin Zhang, Yaoxue Hunan Univ Coll Cyber Sci & Technol Changsha 410082 Peoples R China Tsinghua Univ Dept Comp Sci & Technol BNRist Beijing 100084 Peoples R China

The widespread deployment of edge computing infrastructures has greatly advanced cyber-physical-social systems (CPSSs) by enabling computation offloading for various applications. However, the heterogeneous nature of edge servers (ESs) and the single-destination offloading strategy may limit the overall network performance. In this case, tasks are exclusively handled by the administrative ES, resulting in only marginal improvements in service quality, such as energy consumption and task delay. To fully exploit the advantages of edge computing, this article investigates the cooperation among ESs within CPSS and proposes a cooperative algorithm, named MATORA, to exploit a further performance gain by calculating the task offloading and resource allocation decisions. First, we formulate the joint optimization problem as a time-varying mixed integer nonlinear programming problem;then, we utilize the multiagent deep reinforcement learning (MADRL) to learn the action policy. Second, the MATORA novelly builds reputation model for each ES to obtain insightful knowledge based on service quality, enhancing the training efficiency of MADRL. Thirdly, we simulate a digital twin-based virtual system to monitor the physical network, in which a risk-free, centralized training environment is provided for the multiagent algorithm. Finally, the simulation results and analysis directly validate the effectiveness of our algorithm.

关键词： cooperative computation offloading digital twin (DT) multiagent deep reinforcement learning (MADRL) reputation model

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Joint Communication and computation Cooperation in Wireless-Powered Mobile-Edge Computing Networks With NOMA

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IEEE INTERNET OF THINGS JOURNAL 2023年第11期10卷 9849-9862页

作者： Zeng, Sheng Huang, Xiaohong Li, Dandan Beijing Univ Posts & Telecommun Sch Comp Sci Natl Pilot Software Engn Sch Beijing 100876 Peoples R China

Incorporating wireless power transfer (WPT) into mobile-edge computing (MEC) is an effective way to enhance the self-sustainability of the MEC network. However, it is susceptible to the effect of double-far-near. Meanwhile, inspired by the fact that nonorthogonal multiple access (NOMA) has shown its great potential in improving the spectral efficiency of the network over orthogonal multiple access, this article investigates a cooperative computation offloading scheme for a basic three-node wireless-powered MEC scenario with NOMA, where users overcome the effect of double-far-near through joint communication and computation cooperation. Specifically, we consider a "harvest-then-offload" protocol composed of three stages. In the first stage, users harvest wireless energy signals from the access point (AP). In the second and third stages, based on NOMA, users offload their partial computing tasks on the same time-frequency resource block by using the harvested energy, in which the near user can cooperatively compute and forward the computing task of the far user. We formulate an optimization problem to minimize the transmission energy consumption of the AP by jointly optimizing the transmission power, the offloading coefficient, and the duration of each stage. Due to the nonconvexity of the formulated problem, we first transform it into an equivalent one by using the variable substitution method. Then, we propose an efficient iterative algorithm based on the successive convex approximation (SCA) and Lagrangian duality method to solve this transformed problem. Simulation results show that the proposed algorithm has good convergence and the proposed scheme can achieve the best performance compared with several baseline schemes, which highlights the superiority of the proposed scheme.

关键词： Task analysis Wireless communication NOMA Energy consumption Internet of Things Servers Uplink cooperative computation offloading mobile-edge computing (MEC) nonorthogonal multiple access (NOMA) wireless power transfer (WPT)

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Energy-Efficient joint Resource Allocation and computation offloading in NOMA-enabled Vehicular Fog Computing

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MOBILE NETWORKS & APPLICATIONS 2024年第5期29卷 1564-1576页

作者： Lin, Zhijian Lin, Yonghang Yang, Jianjie Zhang, Qingsong Fuzhou Univ Coll Phys & Informat Engn Fuzhou 350108 Peoples R China Fuzhou Univ Sch Adv Mfg Quanzhou 362251 Peoples R China

The rapid growth in vehicle data fosters the development of vehicular computational offloading. By exploiting the superiority of non-orthogonal multiple access (NOMA) in the offloading process, NOMA-enabled vehicular computation offloading enables collaborative transmissions of multiple fog nodes and is promising for distributed wireless communications environments. Nonetheless, to the best of our knowledge, the problem of task allocation and resource allocation in NOMA-enabled multipoint collaborative offloading in Vehicular Fog Computing (VFC) still remains open. With this consideration, the NOMA-enabled vehicular fog computing problem is investigated in this work, as well as an energy-efficient NOMA-enabled vehicular computing offloading scheme is proposed. The scheme combines task vehicles, main fog access points (F-APs), idle vehicles, and auxiliary F-APs to collaborate on tasks. However, the corresponding optimization problem turns out to be a non-convex issue. To this end, through in-depth mathematical analysis, the monotonicity of NOMA power allocation is discussed to find the optimal power allocation coefficient. Furthermore, an interior-point method based on successive convex approximation (SCA) is proposed to obtain the sub-optimal solution of task division and bandwidth allocation. Simulations are conducted to validate our analysis and corroborate the effectiveness of the proposed scheme.

关键词： NOMA VFC Energy consumption cooperative computation offloading

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computation offloading in NOMA-enabled Vehicular Fog Computing Networks

Computation Offloading in NOMA-enabled Vehicular Fog Computi...

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IEEE International Conference on Communications (IEEE ICC)

作者： Lin, Zhijian Lin, Yonghang Zhang, Qingsong Chen, Pingping Fuzhou Univ Coll Phys & Informat Engn Fuzhou Peoples R China

ISBN: (纸本)9781538674628

With the advent of the Internet of vehicles (IoV), the explosive growth of data from vehicular sensors places a heavy computing burden on green-enabled intelligent transportation systems. In this study, a paradigm of vehicular fog computing (VFC) is introduced, which is able to offload computation tasks to the nearby fog nodes. In addition, considering the advantage of non-orthogonal multiple access (NOMA), a NOMA-enabled VFC is proposed, in which a task vehicle, a main fog access point (F-AP), an idle vehicle, and other cooperative F-APs are involved to process the task. By jointly optimizing NOMA power allocation, task allocation ratio, bandwidth allocation and time slot allocation, the offloading data maximization problem is investigated. After simplifying the problem through mathematical analysis, the offloading data maximization problem is solved by the proposed interior-point method based on successive convex approximation (SCA). Simulation results show that the proposed offloading scheme performs better than other existing schemes in terms of offloading data.

关键词： NOMA VFC cooperative computation offloading

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