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Multi-agent reinforcement learning based computation offloading and resource allocation for LEO Satellite edge computing networks

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COMPUTER COMMUNICATIONS 2024年 222卷 268-276页

作者： Li, Hai Yu, Jinyang Cao, Lili Zhang, Qin Song, Zhengyu Hou, Shujuan Beijing Inst Technol Sch Informat & Elect Beijing 100081 Peoples R China Shanghai Aerosp Elect Technol Inst Shanghai 201109 Peoples R China Beijing Jiaotong Univ Sch Elect & Informat Engn Beijing 100044 Peoples R China

Due to the limitations caused by geographical conditions and economic requirements, it is difficult to provide computing services by terrestrial networks for mobile terminals in remote areas. To address this issue, mobile edge computing (MEC) servers can be deployed in the low earth orbit (LEO) satellites to act as a complement and accommodate the unserved terminals. However, offloading computing tasks to servers in satellites may increase the energy consumption of ground terminals. Considering the limited battery capacity of ground terminals, how to perform the computation offloading and resource allocation are key challenges in the LEO satellite edge computing networks. Therefore, in this paper, we investigate the energy minimization problem for LEO satellite edge computing networks, where a multi-agent deep reinforcement learning algorithm with global rewards is proposed to optimize the transmit power, CPU frequency, bit allocation, offloading decision and bandwidth allocation via a decentralized method. Simulation results show that our proposed algorithm can converge faster. Most importantly, compared with the random algorithm, the proximal policy optimization (PPO) algorithm, and the deep deterministic policy gradient (DDPG) algorithm, the ground terminals' energy consumption can be effectively reduced by our proposed algorithm.

关键词： Multi-agent deep reinforcement learning LEO satellite computation offloading Resource allocation Mobile edge computing

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A Low-Latency Edge computation offloading Scheme for Trust Evaluation in Finance-Level Artificial Intelligence of Things

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IEEE INTERNET OF THINGS JOURNAL 2024年第1期11卷 114-124页

作者： Zhu, Xiaogang Ma, Feicheng Ding, Feng Guo, Zhiwei Yang, Junchao Yu, Keping Wuhan Univ Sch Informat Management Wuhan 430072 Peoples R China Inst Ind Technol Internet Things Yingtan Jiangxi Peoples R China Nanchang Univ Sch Software Nanchang 330031 Peoples R China Chongqing Technol & Business Univ Sch Artificial Intelligence Chongqing 400067 Peoples R China Hosei Univ Grad Sch Sci & Engn Tokyo 1848584 Japan

The finance-level Artificial Intelligence of Things (AIoT) is going to become a novel media in the 6G-driven digital society. Inside the financial AIoT environment, large-scale crowd credit assessment with the guarantee of low latency has been a general demand. Facing limited computational resources, there is still a lack of effective computation offloading methods for this purpose to ensure low latency. In order to deal with such an issue, this article introduces edge computing mode and proposes a low-latency edge computation offloading scheme for trust evaluation in financial AIoT. With different elements involved in the assessment process being denoted via mathematical description, a multiobjective optimization problem with constraints is formulated. Then, the aforementioned optimization problem is solved by a specific search algorithm, so that optimal task offloading schemes can be found. To assess the performance of the proposal, some simulation experiments are conducted to verify the proposed task offloading method. And it can be reflected from numerical results that latency can be well reduced compared with baseline methods.

关键词： Artificial Intelligence of Things (AIoT) computation offloading edge computing trust evaluation

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Deep-Reinforcement-Learning-Based computation offloading for Servicing Dynamic Demand in Multi-UAV-Assisted IoT Network

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IEEE INTERNET OF THINGS JOURNAL 2024年第10期11卷 17249-17263页

作者： Lin, Na Bai, Lu Hawbani, Ammar Guan, Yunchong Mao, Chaojin Liu, Zhi Zhao, Liang Shenyang Aerosp Univ Sch Comp Sci Shenyang 110136 Peoples R China Univ Electrocommun Dept Comp & Network Engn Tokyo 1828585 Japan

In wireless networks, meeting the performance requirements of all tasks solely with Internet of Things (IoT) devices is challenging due to their limited computational power and battery capacity. Given their flexibility and mobility, the application of unmanned aerial vehicles (UAVs) in the context of mobile edge computing (MEC) has garnered significant interest within the sector. However, UAVs also face constraints in terms of resources like storage and computational power. Therefore, it is vital to develop effective UAV assistance solutions to provide long-term demands of in-network services. The dynamic scheduling and computation offloading of UAVs is the subject of this article. Specifically, we propose a deep deterministic policy gradient algorithm based on a greedy (DDPGG) strategy to jointly optimize dynamic scheduling, device association, and task allocation of UAVs, with the goal of minimizing the weighted sum of total system energy consumption and time delay. The problem is formulated as a nonlinear programming problem involving mixed integers. The simulation results demonstrate that the DDPGG algorithm we have proposed exhibits a higher level of performance in comparison to its competitors.

关键词： Internet of Things Task analysis Dynamic scheduling Autonomous aerial vehicles Resource management Heuristic algorithms Servers computation offloading deep reinforcement learning (DRL) dynamic scheduling mobile edge computing (MEC) unmanned aerial vehicle (UAV)

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QDRL: Queue-Aware Online DRL for computation offloading in Industrial Internet of Things

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IEEE INTERNET OF THINGS JOURNAL 2024年第5期11卷 7772-7786页

作者： Xu, Aikun Hu, Zhigang Zhang, Xinyu Xiao, Hui Zheng, Hao Chen, Bolei Zheng, Meiguang Zhong, Ping Kang, Yilin Li, Keqin Cent South Univ Sch Comp Sci & Engn Changsha 410073 Peoples R China South Cent Univ Nationalities Sch Comp Sci Wuhan 430074 Peoples R China SUNY Coll New Paltz Dept Comp Sci New Paltz NY 12561 USA

Recently, the Industrial Internet of Things (IIoT) has shown great application value in environmental monitoring. However, it suffers from serious bottlenecks in energy and computing capability. To address them, researchers have made lots of effort. Nevertheless, they neglect either the edge-end collaboration or the impact of task queue backlog, resulting in low system revenue. To this end, we design a queue-aware computation offloading method based on DRL (QDRL). Specifically, we represent the long-term system operation as a multistage stochastic mixed-integer optimization problem (M-SMIP), which is further converted into a deterministic problem using Lyapunov optimization. Given that the resource allocation and computation offloading in this deterministic problem are strongly coupled and difficult to solve, we decompose this problem into two subproblems. Subsequently, a reinforcement learning scheme with actor-critic architecture is designed to solve these subproblems. The Actor module is designed based on a deep learning model and quantization strategy for generating computation offloading actions. The mathematical reasoning and learning-based methods are integrated as the Critic module for achieving resource allocation. Extensive simulation results show that the performance of QDRL surpasses four baselines and approaches the approximate optimal algorithm in terms of average task queue length, normalized real computation rate, and computation time.

关键词： computation offloading deep learning edge computing Industrial Internet of Things (IIoT) reinforcement learning (RL)

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A Cooperative computation offloading Strategy With On-Demand Deployment of Multi-UAVs in UAV-Aided Mobile Edge Computing

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IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT 2024年第2期21卷 2095-2110页

作者： Li, Chunlin Gan, Yongzheng Zhang, Yong Luo, Youlong Wuhan Univ Technol Sch Comp & Artificial intelligence Wuhan 430063 Peoples R China Natl Univ Def Technol Sci & Technol Parallel & Distributed Proc Lab Changsha 410073 Hunan Peoples R China

In this paper, we plan to use ground-based stations in mobile edge computing (MEC) and unmanned aerial vehicles (UAVs) to provide communication and computation offloading services in disaster areas. However, optimizing the initial number and three-dimensional position of deployed UAVs is a prerequisite for providing computing services to users. Additionally, due to the limited battery and computing power of UAVs, it is a major challenge to rationally design the UAV trajectory during the computational offloading period to ensure communication quality for mobile users and reduce the energy consumption for completing tasks. Thus, we propose a cooperative computation offloading strategy with on-demand deployment of multi-UAV in UAV-aided MEC. The strategy utilizes the predicted user trajectory for UAV deployment on the premise of the minimum path loss of users. Then, to minimize total energy consumption for completing tasks, a joint optimization problem comprising user association strategy, computing resource allocation strategy, and UAV trajectory is proposed, which is a mixed-integer nonlinear program (MINLP). Therefore, to find the suboptimal solution, we use the block coordinate descent method to solve the problem. Numerical results show that the proposed algorithm can efficiently reduce the path loss by up to 18.55% and the total energy consumption by 18.28% compared to the benchmarks.

关键词： Autonomous aerial vehicles Task analysis Energy consumption Trajectory Servers computational efficiency Three-dimensional displays Mobile edge computing (MEC) unmanned aerial vehicles (UAVs) computation offloading on-demand deployment

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LEO satellites selection-based computation offloading algorithm in aircraft-satellite multi-access edge computing networks

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COMPUTER COMMUNICATIONS 2024年 223卷 115-127页

作者： Zhang, Jiadong Zhang, Ruidong Shi, Wenxiao Jilin Univ Coll Commun Engn Changchun 130012 Peoples R China

The emergence of sixth -generation (6G) mobile networks and non -terrestrial networks (NTNs) has led to increased interest in low Earth orbit (LEO) satellite -based communication networks for their potential to provide global coverage and ubiquitous connectivity. In this paper, we investigate the LEO satellites selectionbased computation offloading problem in the aircraft-satellite multi-access edge computing (ASMEC) network, where LEO satellites and edge computing processors are integrated to provide ubiquitous and low -latency communication and computation services for aircraft during flights. In contrast to most existing works, which directly assume a fixed number of satellites or orbits in satellite -based MEC networks, we investigate the problem of how many satellites and which satellites to select in the ASMEC network. Our objective is to minimize the average total time delay of tasks during aircraft-satellite computation offloading. To achieve this, we formulate a nonlinear integer programming (NLIP) problem and propose the LEO satellites selectionbased computation offloading (LSSBCO) algorithm to solve it, which includes the shortest aircraft-satellite distance based access satellite selection (ASS -SD) algorithm and the nearest k ( t ) neighboring satellites selection (NSS- k ( t ) ) algorithm. We evaluate the performance of the LSSBCO algorithm in terms of the average total time delay, the maximum throughput, the average aircraft-satellite distance, the average connection duration, and the number of satellite handovers. Numerical results show that the proposed algorithm outperforms the benchmark algorithms with a lower average total time delay.

关键词： Aircraft-satellite communication computation offloading LEO satellites selection Multi-access edge computing Nonlinear integer programming Queuing system

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Joint computation offloading and Resource Allocation in Mobile-Edge Cloud Computing: A Two-Layer Game Approach

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IEEE TRANSACTIONS ON CLOUD COMPUTING 2025年第1期13卷 411-428页

作者： He, Zhenli Guo, Ying Zhai, Xiaolong Zhao, Mingxiong Zhou, Wei Li, Keqin Engn Res Ctr Cyberspace Yunnan Key Lab Software Engn Kunming 650091 Peoples R China Yunnan Univ Sch Software Kunming 650091 Peoples R China SUNY Coll New Paltz Dept Comp Sci New Paltz NY 12561 USA

Mobile-Edge Cloud Computing (MECC) plays a crucial role in balancing low-latency services at the edge with the computational capabilities of cloud data centers (DCs). However, many existing studies focus on single-provider settings or limit their analysis to interactions between mobile devices (MDs) and edge servers (ESs), often overlooking the competition that occurs among ESs from different providers. This article introduces an innovative two-layer game framework that captures independent self-interested competition among MDs and ESs, providing a more accurate reflection of multi-vendor environments. Additionally, the framework explores the influence of cloud-edge collaboration on ES competition, offering new insights into these dynamics. The proposed model extends previous research by developing algorithms that optimize task offloading and resource allocation strategies for both MDs and ESs, ensuring the convergence to Nash equilibrium in both layers. Simulation results demonstrate the potential of the framework to improve resource efficiency and system responsiveness in multi-provider MECC environments.

关键词： computation offloading game theory mobile- edge cloud computing mobile- edge cloud computing nash equilibrium resource allocation resource allocation resource allocation

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Optimal computation offloading Decisions Based on System Utility and Cost Balance

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IEEE ACCESS 2025年 13卷 68232-68244页

作者： Zhang, Bingxue Li, Xisheng You, Jia Univ Sci & Technol Beijing Sch Automat & Elect Engn Beijing 100083 Peoples R China Beijing Engn Res Ctr Ind Spectrum Imaging Beijing 100083 Peoples R China

Edge computing provides terminal users with computing resources and data processing capabilities by deploying edge nodes near Internet of Things (IoT) devices to meet the processing demands of terminal applications. In this study, we address the offloading problem of partitionable tasks in collaborative edge computing systems and propose a new evaluation model that jointly optimizes the time and energy costs along with the system utility. By calculating the full load rate of edge server clusters, we describe and evaluate the operational efficiency of the system, aiming to design a computation offloading decision scheme that not only reduces costs, but also enhances system utility. Based on the new evaluation model, an objective function is constructed to minimize the system's cost-utility ratio, and an improved particle swarm optimization algorithm with enhanced search capabilities is proposed to solve it by designing a computation offloading strategy. Experiments conducted in a simulation based on the edge system of an industrial park verified that the cost-utility balanced optimization model achieves at least 7% reduction in the cost-utility ratio across varying user scales compared to traditional cost optimization models, while achieving over 20% reduction in optimization efficiency under different edge server scales. The computation offloading efficiency is significantly improved, effectively boosting the overall system performance through the collaborative optimization of resource efficiency and execution costs.

关键词： Servers computational modeling Costs Edge computing Energy consumption Optimization Particle swarm optimization Load modeling Collaboration Internet of Things computation offloading cost-utility balanced operational efficiency of system

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Knowledge-Defined Edge Computing Networks Assisted Long-Term Optimization of computation offloading and Resource Allocation Strategy

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2024年第6期23卷 5316-5329页

作者： Yang, Kaiqi Wang, Xingwei He, Qiang Zhao, Liang Liu, Yufei Tarchi, Daniele Northeastern Univ Sch Comp Sci & Engn Shenyang 110819 Peoples R China Northeastern Univ Sch Med & Biol Informat Engn Shenyang 110819 Peoples R China Shenyang Aerosp Univ Sch Comp Sci Shenyang 110136 Peoples R China Univ Bologna Sch Elect & Informat Engn I-40126 Bologna Italy

With the proliferation of devices connected to the Internet of Things (IoT), the complexity of network management has increased. To intelligently manage large-scale networks, we propose a Knowledge-Defined Edge Computing Networks (KDECN) architecture. Edge Nodes (ENs) deployed in the KDECN architecture are responsible for collecting and preprocessing the relevant information uploaded by User Devices (UDs), and provide computation resources for UDs. Futhermore, since multiple UDs share system computation resources, one computing decision will affect the subsequent decision-making of other UDs. Thus, accurately predicting the demands for UD task requests is a key challenge to maximize long-term execution utility. To this end, we deploy the LSTM-based Task Request Demand Prediction (TRDP) method on the management plane of KDECN architecture to predict the task request quantity of UDs in each future time slot. In order to maximize long-term execution utility of the system, we propose a Deep Reinforcement Learning (DRL)-based Long-term computation offloading and computation Resource Allocation (L-CORA) algorithm. Specifically, the proposed L-CORA algorithm makes computing decisions based on the prediction of the offloading task quantity and the personalized demands of UDs to ensure the long-term quality of computing service. Extensive experiments with Shanghai real-world datasets to prove that the KDECN-based L-CORA algorithm effectively improves the average utility of the system.

关键词： computation offloading knowledge-defined networking mobile edge computing deep reinforcement learning

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Multiple-Time-Slot Multiple Access Binary computation offloading in the K-User Case

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2024年 72卷 3927-3944页

作者： Liu, Xiaomeng Davidson, Timothy N. McMaster Univ Dept Elect & Comp Engn Hamilton ON L8S 4K1 Canada

When multiple devices seek to offload computational tasks to their access point, the nature of the multiple access scheme plays a critical role in the system performance. For a system with heterogeneous tasks, we adopt a time-slotted signaling architecture in which different numbers of devices transmit in each slot, subject to individual power constraints. We consider the problem of jointly selecting the devices that will offload, along with optimizing their communication resources (their powers and rates in each time slot, and the time slot lengths) so as to minimize the a weighted sum of the energy expended by the devices. We employ a customized tree search algorithm for the offloading decisions in which a resource allocation problem is solved at each node. For time-division multiple access (TDMA) and "rate optimal" multiple access, we obtain reduced-dimension convex formulations of the resource allocation problem. For non-orthogonal multiple access (NOMA) with independent decoding (ID) or fixed-order sequential decoding (FOSD) we show that the resource allocation problem has a difference-of-convex structure and we develop a successive convex approximation algorithm with feasible point pursuit. Furthermore, for the FOSD scheme we obtain a closed-form expression that provides the optimal decoding order when it is feasible, and efficient algorithms for finding a good decoding order when it is not. Our results capture the inherent tradeoffs between the complexity of a multiple access scheme (and its resource allocation algorithm), and its performance in the computation offloading application.

关键词： Resource management Decoding Task analysis Signal processing algorithms Time division multiple access Performance evaluation NOMA Mobile edge computing computation offloading multiple access resource allocation

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