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Multi-agent deep reinforcement learning based multi-task partial computation offloading in mobile edge computing☆

FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2025年 172卷

作者： Li, Han Meng, Shunmei Sun, Jin Cai, Zhicheng Li, Qianmu Zhang, Xuyun Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing 210094 Peoples R China Macquarie Univ Sch Comp Sydney 2109 Australia

Mobile edge computing (MEC) can enhance the computation performance of end-devices by providing computation offloading service at the network edge. However, given that both end-devices and edge servers have finite computation resources, inefficient offloading policies may lead to overload, thereby increasing the computation delays of tasks. In this paper, we investigate a multi-task partial computation offloading problem combined with a queue model. Based on achieving load-balancing across the MEC system, our objective is to minimize the long-standing average task-processing cost of the end-devices while ensuring the delay thresholds of tasks. For this purpose, a distributed offloading algorithm utilizing the multi-agent deep reinforcement learning (MADRL) method is proposed. Specifically, through interacting with the MEC environment and accumulating experience data, the device agents can collaborate to optimize their local offloading decisions over continuous time-slots, which includes adjusting the transmission power and determining the tasks' offloading ratios under the dynamic wireless channel conditions. Exhaustive experimental results demonstrate that in contrast with the baseline algorithms, the proposed offloading algorithm can not only better balance the computation loads between the end-devices and the MEC server, but also more effectively reduce the task-processing cost of the end-devices, as well as the percentage of timeout tasks.

关键词： Mobile edge computing partial computation offloading Load-balancing Multi-agent reinforcement learning

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partial computation offloading in NOMA-Assisted Mobile-Edge Computing Systems Using Deep Reinforcement Learning

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IEEE INTERNET OF THINGS JOURNAL 2021年第17期8卷 13196-13208页

作者： Dat, Van Tuong Truong, Thanh Phung Nguyen, The-Vi Noh, Wonjong Cho, Sungrae Chung Ang Univ Sch Comp Sci & Engn Seoul 06974 South Korea Hallym Univ Sch Software Chunchon 24252 South Korea

Mobile-edge computing (MEC) and nonorthogonal multiple access (NOMA) have been regarded as promising technologies for beyond fifth-generation (B5G) and sixth-generation (6G) networks. This study aims to reduce the computational overhead (weighted sum of consumed energy and latency) in a NOMA-assisted MEC network by jointly optimizing the computation offloading policy and channel resource allocation under dynamic network environments with time-varying channels. To this end, we propose a deep reinforcement learning algorithm named ACDQN that utilizes the advantages of both actor-critic and deep $Q$ -network methods and provides low complexity. The proposed algorithm considers partial computation offloading, where users can split computation tasks so that some are performed on the local terminal while some are offloaded to the MEC server. It also considers a hybrid multiple access scheme that combines the advantages of NOMA and orthogonal multiple access to serve diverse user requirements. Through extensive simulations, it is shown that the proposed algorithm stably converges to its optimal value, provides approximately 10%, 27%, and 69% lower computational overhead than the prevalent schemes, such as full offloading with NOMA, random offloading with NOMA, and fully local execution, and achieves near-optimal performance.

关键词： Deep reinforcement learning (DRL) mobile-edge computing (MEC) nonorthogonal multiple access (NOMA) partial computation offloading resource allocation

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Deep Reinforcement Learning Based Joint partial computation offloading and Resource Allocation in Mobility-Aware MEC System

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China Communications 2022年第8期19卷 85-99页

作者： Luyao Wang Guanglin Zhang College of Information Science and Technology Donghua UniversityShanghai 201620China Engineering Research Center of Digitized Textile and Apparel Technology Ministry of EducationShanghai 201620China

Mobile edge computing(MEC)emerges as a paradigm to free mobile devices(MDs)from increasingly dense computing workloads in 6G *** quality of computing experience can be greatly improved by offloading computing tasks from MDs to MEC *** energy harvested by energy harvesting equipments(EHQs)is considered as a promising power supply for users to process and offload *** this paper,we apply the uniform mobility model of MDs to derive a more realistic wireless channel model in a multi-user MEC system with batteries as EHQs to harvest and storage *** investigate an optimization problem of the weighted sum of delay cost and energy cost of MDs in the MEC *** propose an effective joint partial computation offloading and resource allocation(CORA)algorithm which is based on deep reinforcement learning(DRL)to obtain the optimal scheduling without prior knowledge of task arrival,renewable energy arrival as well as channel *** simulation results verify the efficiency of the proposed algorithm,which undoubtedly minimizes the cost of MDs compared with other benchmarks.

关键词： mobile edge computing energy harvesting device-mobility partial computation offloading resource allocation deep reinforcement learning

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Lyapunov-Based partial computation offloading for Multiple Mobile Devices Enabled by Harvested Energy in MEC

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IEEE INTERNET OF THINGS JOURNAL 2022年第11期9卷 9025-9035页

作者： Guo, Min Wang, Wei Huang, Xing Chen, Yanru Zhang, Lei Chen, Liangyin Sichuan Univ Coll Comp Sci Chengdu 610065 Peoples R China Sichuan Univ Coll Software Engn Chengdu 610065 Peoples R China Sichuan Univ Inst Ind Internet Res Chengdu 610065 Peoples R China Northwest Minzu Univ Sch Math & Comp Sci Lanzhou 730050 Peoples R China

Mobile-edge computing (MEC) has been garnering considerable level of interests by processing computation tasks nearby mobile devices (MDs). With limited computation and communication resources and strict task deadline, balancing the energy consumption and time delay of computational tasks will be highly focused. MDs deployed energy harvesting (EH) modules can always provide service to continuous task requests, and finer-grained offloading schemes of the MEC system will significantly affect the time delay of computation tasks. However, when combined them together, the energy causal constraint and the coupling between offloading ratios and resources allocation will cause new challenges for the computation offloading problem. To address these issues, we investigate the partial computation offloading schemes for multiple MDs enabled by harvested energy in MEC. Specifically, we build models for two computing modes and EH process. Subsequently, we formulate a nonconvex optimization problem by minimizing the energy consumption of all the MDs while satisfying the constraint of time delay. Furthermore, we propose and design a novel algorithm based on the Lyapunov optimization to achieve optimal solution, that is, Lyapunov-optimization-based partial computation offloading for multiuser (LOMUCO). Then, we take the long-term average energy consumption and the discarding ratio of computation tasks as the quantitative metrics and conduct extended simulation experiments to confirm the performance of LOMUCO. Finally, compared to several baseline or state-of-the-art algorithms, including local computing all (LCA), offloading computing all (OCA), randomly partial computation offloading (RPCO), and Lyapunov-optimization-based dynamic computation offloading (LODCO), we can demonstrate the superiority of LOMUCO.

关键词： Task analysis Servers computational modeling Optimization Energy consumption Delay effects Resource management Data-partition applications energy harvesting (EH) Lyapunov optimization mobile-edge computing (MEC) partial computation offloading

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Cost Optimization of partial computation offloading and Pricing in Vehicular Networks

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JOURNAL OF SIGNAL PROCESSING SYSTEMS FOR SIGNAL IMAGE AND VIDEO TECHNOLOGY 2020年第12期92卷 1421-1435页

作者： Li, Lanhui Lv, Tiejun Huang, Pingmu Mathiopoulos, P. Takis Beijing Univ Posts & Telecommun BUPT Sch Informat & Commun Engn Beijing Peoples R China Natl & Kapodistrian Univ Athens Dept Informat & Telecommun Athens 15784 Greece

For vehicles with limited computation resources offloading their computational tasks to a mobile edge computing (MEC) server has been studied in the past as an effective means for improving their computational capabilities. However, most of these studies do not consider, in a meaningful way, the economic aspects related to both the computation offloading of the vehicles and the MEC service providers. In order to fill this gap, in this paper, a new cost based optimization methodology which jointly considers the cost of partial offloading vs. the pricing of the MEC server is proposed and its performance is analyzed. In particular, we first formally establish the cost model for vehicles and then, by setting a service price, the revenue model for MEC server. Secondly, optimal vehicle offloading strategies are identified and through a cost minimization partial computation offloading algorithm vehicles can configure, in an optimal way, the local CPU frequency and task partition based on the service price. Thirdly, by considering its computation resource limitations, the resource allocation and pricing mechanism for the MEC server is presented. It is shown that, through the development of an appropriate pricing algorithm, the MEC server can obtain the service price which maximizes its revenue while at the same time satisfying the server's resource constraints. Numerical results have verified that the proposed scheme is indeed more cost effective as compared to local execution with dynamic voltage scaling (DVS) technique, full computation offloading and other partial computation offloading schemes. Furthermore, various performance evaluation results obtained by means of computer simulations have shown that the proposed pricing scheme achieves higher revenue as compared to other previously known fixed and random pricing schemes.

关键词： Vehicular networks partial computation offloading MEC server Pricing scheme

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A Cooperative partial computation offloading Scheme for Mobile Edge Computing Enabled Internet of Things

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IEEE INTERNET OF THINGS JOURNAL 2019年第3期6卷 4804-4814页

作者： Ning, Zhaolong Dong, Peiran Kong, Xiangjie Xia, Feng Dalian Univ Technol Key Lab Ubiquitous Network & Serv Software Liaoni Sch Software Dalian 116620 Peoples R China Nanjing Univ State Key Lab Novel Software Technol Nanjing 210008 Jiangsu Peoples R China

With the evolutionary development of latency sensitive applications, delay restriction is becoming an obstacle to run sophisticated applications on mobile devices. partial computation offloading is promising to enable these applications to execute on mobile user equipments with low latency. However, most of the existing researches focus on either cloud computing or mobile edge computing (MEC) to offload tasks. In this paper, we comprehensively consider both of them and it is an early effort to study the cooperation of cloud computing and MEC in Internet of Things. We start from the single user computation offloading problem, where the MEC resources are not constrained. It can be solved by the branch and bound algorithm. Later on, the multiuser computation offloading problem is formulated as a mixed integer linear programming problem by considering resource competition among mobile users, which is NP-hard. Due to the computation complexity of the formulated problem, we design an iterative heuristic MEC resource allocation algorithm to make the offloading decision dynamically. Simulation results demonstrate that our algorithm outperforms the existing schemes in terms of execution latency and offloading efficiency.

关键词： Internet of Things (IoT) mobile edge computing (MEC) partial computation offloading resource allocation

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The partial computation offloading strategy based on game theory for multi-user in mobile edge computing environment

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COMPUTER NETWORKS 2020年 178卷 107334-107334页

作者： Zhou, Shuchen Jadoon, Waqas COMSATS Univ Islamabad Dept Comp Sci Abbottabad Campus Islamabad 22060 Pakistan Huanghuai Univ Inst Int Educ Zhumadian 463000 Peoples R China

Mobile edge computing provides powerful computing for mobile device users to broaden their computation capability. However, due to the limited bandwidth of the wireless channel, both the energy consumption for mobile device's computation offloading and the service latency maybe increase. In this paper, we focus on the partial computation offloading problem for multi-user in mobile edge computing environment with multi-wireless channel. The computation overhead model is built based on game theory. Then, the existence of Nash equilibrium is proven. Furthermore, the partial computation offloading algorithm with low time complexity is given to achieve the Nash equilibrium. In addition, another partial computation task offloading mechanism for mobile device users, who has enough energy and only pay attention to the computation time overhead, is given to reduce the computation overhead. Finally, extensively numerical experiments are conducted. The experimental results show that our proposed algorithm can achieve better performance than the compared algorithms in the mobile edge computing environment.

关键词： Mobile edge computing partial computation offloading Game theory NOMA

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Mobile-Edge Computing: partial computation offloading Using Dynamic Voltage Scaling

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IEEE TRANSACTIONS ON COMMUNICATIONS 2016年第10期64卷 4268-4282页

作者： Wang, Yanting Sheng, Min Wang, Xijun Wang, Liang Li, Jiandong Xidian Univ State Key Lab Integrated Serv Networks Inst Informat Sci Xian 710071 Peoples R China Shannxi Normal Univ Sch Comp Sci Xian 710119 Peoples R China

The incorporation of dynamic voltage scaling technology into computation offloading offers more flexibilities for mobile edge computing. In this paper, we investigate partial computation offloading by jointly optimizing the computational speed of smart mobile device (SMD), transmit power of SMD, and offloading ratio with two system design objectives: energy consumption of SMD minimization (ECM) and latency of application execution minimization (LM). Considering the case that the SMD is served by a single cloud server, we formulate both the ECM problem and the LM problem as nonconvex problems. To tackle the ECM problem, we recast it as a convex one with the variable substitution technique and obtain its optimal solution. To address the nonconvex and nonsmooth LM problem, we propose a locally optimal algorithm with the univariate search technique. Furthermore, we extend the scenario to a multiple cloud servers system, where the SMD could offload its computation to a set of cloud servers. In this scenario, we obtain the optimal computation distribution among cloud servers in closed form for the ECM and LM problems. Finally, extensive simulations demonstrate that our proposed algorithms can significantly reduce the energy consumption and shorten the latency with respect to the existi ofngfloading schemes.

关键词： partial computation offloading dynamic voltage scaling mobile-edge computing collaboration between communication and computation resources

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Joint partial offloading and Resource Allocation for Parked Vehicle-Assisted Multi-Access Edge Computing

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IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTING 2024年第3期12卷 918-923页

作者： Pham, Xuan-Qui Huynh-The, Thien Kim, Dong-Seong Kumoh Natl Inst Technol ICT Convergence Res Ctr Gumi 39177 South Korea Ho Chi Minh City Univ Technol & Educ Dept Comp & Commun Engn Ho Chi Minh City 700000 Vietnam

In recent years, parked vehicle-assisted multi-access edge computing (PVMEC) has emerged to expand the computational power of MEC networks by utilizing the opportunistic resources of parked vehicles (PVs) for computation offloading. In this article, we study a joint optimization problem of partial offloading and resource allocation in a PVMEC paradigm that enables each mobile device (MD) to offload its task partially to either the MEC server or nearby PVs. The problem is first formulated as a mixed-integer nonlinear programming problem with the aim of maximizing the total offloading utility of all MDs in terms of the benefit of reducing latency through offloading and the overall cost of using computing and networking resources. We then propose a partial offloading scheme, which employs a differentiation method to derive the optimal offloading ratio and resource allocation while optimizing the task assignment using a metaheuristic solution based on the whale optimization algorithm. Finally, evaluation results justify the superior system utility of our proposal compared with existing baselines.

关键词： parked vehicle partial computation offloading resource allocation Multi-access edge computing parked vehicle Multi-access edge computing partial computation offloading resource allocation

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Adaptive delay-energy balanced partial offloading strategy in Mobile Edge Computing networks

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Digital Communications and Networks 2023年第6期9卷 1310-1318页

作者： Shumei Liu Yao Yu Lei Guo Phee Lep Yeoh Branka Vucetic Yonghui Li School of Computer Science and Engineering Northeastern UniversityShenyang110819China Key Laboratory of Intelligent Computing in Medical Image Ministry of EducationNortheastern UniversityShenyang110819China School of Communication and Information Engineering Chongqing University of Posts and TelecommunicationsChongqing400065China School of Electrical and Information Engineering University of SydneySydneyNSW2006Australia

Mobile Edge Computing(MEC)-based computation offloading is a promising application paradigm for serving large numbers of users with various delay and energy *** this paper,we propose a flexible MECbased requirement-adaptive partial offloading model to accommodate each user's specific preference regarding delay and energy *** address the dimensional differences between time and energy,we introduce two normalized parameters and then derive the computational overhead of processing *** from existing works,this paper considers practical variations in the user request patterns,and exploits a flexible partial offloading mode to minimize computation overheads subject to tolerable delay,task workload and power *** the resulting problem is non-convex,we decouple it into two convex subproblems and present an iterative algorithm to obtain a feasible offloading *** experiments show that our proposed scheme achieves a significant improvement in computation overheads compared with existing schemes.

关键词： Mobile edge computing(MEC) Delay Energy consumption Dynamic balance partial computation offloading

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