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Energy and delay co-aware intelligent computation offloading and resource allocation for fog computing networks

MULTIMEDIA TOOLS AND APPLICATIONS 2023年第19期83卷 56737-56762页

作者： Chen, Siguang Wang, Qian Zhu, Xi Nanjing Univ Posts & Telecommun Sch Internet Things Nanjing Peoples R China

In the data-rich everything-connected world, rapid and green data processing is essential, especially for some delay-sensitive and computation-intensive tasks. Motivated by these requirements, an energy and delay co-aware intelligent fog computation offloading and resource allocation scheme is conceived in this paper. Specifically, we formulate a weighted sum minimization problem of the task completion time and energy consumption at the local fog to achieve efficient task computation. A deep learning-based joint offloading decision and resource allocation (DL-JODRA) algorithm is developed to address this problem by jointly optimizing the offloading action, local CPU, bandwidth and external CPU occupation ratios. The optimal offloading decision based comprehensive optimization consideration of network resources further improves the network efficiency. Subsequently, to improve the efficiency and solution in a large-scale network scenario, a deep reinforcement learning-based joint offloading decision and resource allocation (DRL-JODRA) algorithm is constructed to obtain the optimal offloading and resource allocation policy. This approach is more suitable and stable for the solution of continuous-discrete action space by integrating the design of probabilistic discrete operation and inverting the gradient update. Finally, the extensive simulation results demonstrate that the proposed DL-JODRA and DRL-JODRA algorithms can rapidly achieve optimal offloading decision and resource allocation and gain a significant reduction in network costs (i.e., delay and energy) compared with other benchmark methods.

关键词： Fog computing computation offloading Deep learning Deep reinforcement learning Resource allocation Joint optimization

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DRL-Enabled computation offloading for AIGC Services in IIoT-Assisted Edge Computing Networks

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IEEE INTERNET OF THINGS JOURNAL 2025年第9期12卷 12829-12844页

作者： Zhang, Xingxing Li, Shaobo Tang, Jianhang Zhu, Keyu Zhang, Yang Sikdar, Biplab Guizhou Univ State Key Lab Publ Big Data Guiyang 550025 Peoples R China Natl Univ Singapore Dept Elect & Comp Engn Singapore 119077 Singapore Guizhou Inst Technol Sch Big Data Guiyang 550003 Peoples R China Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Nanjing 210016 Peoples R China

The widespread application of AI-generated content (AIGC) services has driven demand for efficient computational resources, making effective task scheduling and computation offloading in edge computing (EC) environments a critical research topic. However, the high computational requirements and low latency demands of AIGC services, combined with the limitations of EC, present challenges for existing offloading methods, such as unstable decision making in dynamic task environments and resource overloading. Here, we propose a decentralized AIGC task offloading architecture within an IoT-assisted EC network to optimize the quality of AIGC services. In this architecture, we define a multiobjective joint optimization problem for AIGC task offloading, aiming to simultaneously optimize key performance metrics, such as task latency, energy efficiency, and load balancing. To address this problem, we introduce an improved proximal policy optimization (PPO)-based deep reinforcement learning (DRL) algorithm, named TOPPO. By incorporating a policy update step size constraint and a clipping mechanism, TOPPO significantly enhances the stability of the training process and reduces fluctuations during policy updates. Additionally, the algorithm integrates an LSTM model to improve its ability to handle temporal dependencies. Through continuous interaction between the model and the environment, the offloading strategy is iteratively updated to ensure that diverse AIGC tasks are efficiently executed on IoT devices or edge servers. Extensive simulations and performance evaluations demonstrate that the proposed method achieves significant improvements in task latency, energy consumption, and load management during AIGC task processing.

关键词： Edge computing Optimization computational modeling Load management Cloud computing Real-time systems computational efficiency Servers Resource management Load modeling AI-generated content (AIGC) services computation offloading deep reinforcement learning (DRL) edge computing network (ECN) Intelligent Internet of Things (IIoT)

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computation offloading in E-RAN via Deep Reinforcement Learning 23

Computation Offloading in E-RAN via Deep Reinforcement Learn...

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2nd International Conference on Networks, Communications and Information Technology, CNCIT 2023

作者： Lu, Kun Zhou, Quan Li, Mingchu Dalian University of Technology Dalian Shi Ganjingzi Qu China

ISBN: (纸本)9798400700620

The current standards for fifth generation mobile communications pose significant performance challenges to current radio access networks. Elastic Radio Access Network (E-RAN) is currently considered an effective solution for future 5G access networks. Edge computing sinks computing and storage to edge nodes closer to users to meet the massive computing needs of large terminal devices. The integration of edge computing and wireless access network is an important trend in the field of Internet and communication. Therefore, task offloading and resource allocation in the E-RAN network architecture have become key issues that need to be addressed. We formalize the computational offload problem as a long-term optimization problem, with the goal of minimizing the total cost of system latency and energy consumption, and using an improved DQN network to determine offload decisions. Experimental results show that our proposed method can achieve better efficiency compared to other baseline methods. © 2023 ACM.

关键词： computation offloading

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Twin delayed deep deterministic policy gradient-based intelligent computation offloading for IoT

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Digital Communications and Networks 2023年第4期9卷 836-845页

作者： Siguang Chen Bei Tang Kun Wang School of Internet of Things Nanjing University of Posts and TelecommunicationsNanjing210003China Jiangsu Engineering Research Center of Communications and Network Technology Nanjing University of Posts and TelecommunicationsNanjing210003China Department of Electrical and Computer Engineering University of CaliforniaLos AngelesCA90095USA

In view of the randomness distribution of multiple users in the dynamic large-scale Internet of Things(IoT)scenario,comprehensively formulating available resources for fog nodes in the area and achieving computation services at low cost have become great *** a result,this paper studies an efficient and intelligent computation offloading mechanism with resource ***,an optimization problem is formulated to minimize the total energy consumption of all tasks under the joint optimization of computation offloading decisions,bandwidth resources and transmission ***,a Twin Delayed Deep Deterministic Policy Gradient-based Intelligent computation offloading(TD3PG-ICO)algorithm is proposed to solve this optimization *** combining the concept of the actor critic algorithm,the proposed algorithm designs two independent critic networks that can avoid the subjective prediction of a single critic network and better guide the policy network to generate the global optimal computation offloading ***,this algorithm introduces a continuous variable discretization operation to select the target offloading node with random *** available resources of the target node are dynamically allocated to improve the model decision-making ***,the simulation results show that this proposed algorithm has faster convergence speed and good *** can always approach the greedy algorithm with respect to the lowest total energy ***,compared with full local and Deep Q-learning Network(DQN)-based computation offloading schemes,the total energy consumption can be reduced by an average of 15.53%and 6.41%,respectively.

关键词： Fog computing computation offloading Deep reinforcement learning Resource allocation

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SGCS: An Intelligent Stackelberg-Game-Based computation offloading and Resource Pricing Scheme in Blockchain-Enabled MEC for IIoT

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IEEE INTERNET OF THINGS JOURNAL 2024年第16期11卷 26727-26740页

作者： Lin, Bing Chen, Xuzhan Chen, Xing Ma, Yun Xiong, Neal N. Fujian Normal Univ Coll Phys & Energy Fujian Prov Key Lab Quantum Manipulat & New Energy Fuzhou 350117 Peoples R China Fujian Normal Univ Fujian Prov Collaborat Innovat Ctr Adv High Field Fuzhou 350117 Peoples R China Peking Univ Sch Comp Sci Beijing 100871 Peoples R China Fuzhou Univ Coll Comp & Data Sci Fuzhou 350118 Peoples R China Fuzhou Univ Fujian Prov Key Lab Network Comp & Intelligent Inf Fuzhou 350118 Peoples R China Peking Univ Inst Artificial Intelligence Beijing Peoples R China Tianjin Univ Coll Intelligence & Comp Tianjin 300072 Peoples R China

Mobile edge computing (MEC) offers low-latency and flexible computing services for mobile devices (MDs) in Industrial Internet of Things (IIoT). Edge servers (ESs) in general belong to different subjects and will focus on their own interests. They may be reluctant to provide computation resources to MDs without appropriate incentives. Meanwhile, there is a trust issue in trading computation resources between ESs and MDs. Due to the complex interaction between ESs and MDs, it is a challenge for ESs to gain satisfactory revenue through reasonable resource pricing strategies, and for MDs to improve their Quality of Experience (QoE) through efficient computation offloading strategies. This article proposes a Stackelberg game-based computation offloading and resource pricing scheme (SGCS) in blockchain-enable MEC for IIoT. First, a blockchain-based resource trading framework is designed to enable trusted resource transactions. Second, a multileader multifollower Stackelberg game is presented to analyze the complex interactions in the multi-ES and multi-MD environments. Finally, the iterative proximal algorithm (IPA) for MDs' offloading decision and the subgradient-based iterative pricing algorithm (SIPA) for ESs' pricing decision are proposed, respectively, which guarantees that the game converges to a Stackelberg equilibrium (SE). Compared with multiagent deep deterministic policy gradient, genetic algorithm and PSO-GA (i.e., benchmark strategies), the average disutility of MDs with our proposed scheme is reduced by 6.95%-13.07%, 3.09%-20.41%, and 2.36%-17.22%, respectively. Moreover, with the increase of the number of MDs, our proposed scheme has better robustness, which can effectively deal with large-scale scenarios.

关键词： computation offloading Industrial Internet of Things (IIoT) mobile edge computing (MEC) Stackelberg game computation offloading Industrial Internet of Things (IIoT) mobile edge computing (MEC) Stackelberg game

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A Study for Inter-Satellite Cooperative computation offloading in LEO Satellite Networks

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China Communications 2025年第2期22卷 12-25页

作者： Gang Yuanshuo Zhang Yuexia Wu Peng Zheng Hui Fan Guangteng Key Laboratory of Modern Measurement and Control Technology Ministry of EducationBeijing Information Science and Technology UniversityBeijing 102206China 2 National Innovation Institute of Defense Technology Academy of Military ScienceBeijing 100071China

Low Earth orbit(LEO)satellite networks have the advantages of low transmission delay and low deployment cost,playing an important role in providing reliable services to ground *** paper studies an efficient inter-satellite cooperative computation offloading(ICCO)algorithm for LEO satellite ***,an ICCO system model is constructed,which considers using neighboring satellites in the LEO satellite networks to collaboratively process tasks generated by ground user terminals,effectively improving resource utilization ***,the optimization objective of minimizing the system task computation offloading delay and energy consumption is established,which is decoupled into two *** terms of computational resource allocation,the convexity of the problem is proved through theoretical derivation,and the Lagrange multiplier method is used to obtain the optimal solution of computational *** deal with the task offloading decision,a dynamic sticky binary particle swarm optimization algorithm is designed to obtain the offloading decision by *** results show that the ICCO algorithm can effectively reduce the delay and energy consumption.

关键词： computation offloading inter-satellite co-operation LEO satellite networks

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DRL-Based V2V computation offloading for Blockchain-Enabled Vehicular Networks

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IEEE TRANSACTIONS ON MOBILE COMPUTING 2023年第7期22卷 3882-3897页

作者： Shi, Jinming Du, Jun Shen, Yuan Wang, Jian Yuan, Jian Han, Zhu Tsinghua Univ Dept Elect Engn Beijing 100084 Peoples R China Univ Houston Dept Elect & Comp Engn Houston TX 77004 USA Kyung Hee Univ Dept Comp Sci & Engn Seoul 446701 South Korea

Vehicular edge computing (VEC) is an effective method to increase the computing capability of vehicles, where vehicles share their idle computing resources with each other. However, due to the high mobility of vehicles, it is challenging to design an optimal task allocation policy that adapts to the dynamic vehicular environment. Further, vehicular computation offloading often occurs between unfamiliar vehicles, how to motivate vehicles to share their computing resources while guaranteeing the reliability of resource allocation in task offloading is one main challenge. In this paper, we propose a blockchain-enabled VEC framework to ensure the reliability and efficiency of vehicle-to-vehicle (V2V) task offloading. Specifically, we develop a deep reinforcement learning (DRL)-based computation offloading scheme for the smart contract of blockchain, where task vehicles can offload part of computation-intensive tasks to neighboring vehicles. To ensure the security and reliability in task offloading, we evaluate the reliability of vehicles in resource allocation by blockchain. Moreover, we propose an enhanced consensus algorithm based on practical Byzantine fault tolerance (PBFT), and design a consensus nodes selection algorithm to improve the efficiency of consensus and motivate base stations to improve reliability in task allocation. Simulation results validate the effectiveness of our proposed scheme for blockchain-enabled VEC.

关键词： Vehicular edge computing (VEC) computation offloading blockchain deep reinforcement learning (DRL)

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Online Distributed Optimization for Energy-Efficient computation offloading in Air-Ground Integrated Networks

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2023年第4期72卷 5110-5124页

作者： Zhao, Junhui Sun, Xiaoke Ma, Xiaoting Zhang, Huan Yu, Fei Richard Hu, Yanlin Beijing Jiaotong Univ Sch Elect & Informat Engn Beijing 100044 Peoples R China East China Jiaotong Univ Sch Informat Engn Nanchang 330013 Peoples R China China Telecom Corp Ltd Res Inst Beijing 100044 Peoples R China Coordinat Ctr China Natl Compter Network Emergency Response Tech Team Beijing 100044 Peoples R China Carleton Univ Dept Syst & Comp Engn Ottawa ON K1S 5B6 Canada

Driven by ever-increasing vehicular intelligent computation-intensive and delay-sensitive services, this paper in-vestigates the computing offloading in unmanned aerial vehicle (UAV)-assisted vehicular networks. Due to the limited onboard energy and computational resources of the mobile entities (i.e., the vehicles and the UAV), it is significant to explore the collab-orative computation among the vehicles, the UAV, and the ter-restrial computing servers for improving energy efficiency (EE) while trading off the service delay. Unlike existing work in the literature that is based on offline settings with a global view, an online distributed mechanism is proposed to cope with the spatial and temporal variations of the networks. Specifically, upon the arriving tasks and the real-time channel conditions, mobile entities adaptively decide about the task offloading and computational resources allocation in parallel. Moreover, the UAV also designs its trajectory with the residual battery capacity taken into account. Theoretical analysis shows that the developed approach can achieve the EE-delay tradeoff as [O(1/V ), O (V )] with V being a control parameter, and can strike a flexible balance between them by tuning V. Numerical results verify the theoretical analysis and reveal that the performance gain can be obtained over conventional methods in the EE performance.

关键词： Task analysis Servers Autonomous aerial vehicles computational modeling Optimization Trajectory Resource management Air-ground integrated networks computation offloading online distributed algorithm

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Joint computation offloading and Resource Allocation for Edge-Cloud Collaboration in Internet of Vehicles via Deep Reinforcement Learning

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IEEE SYSTEMS JOURNAL 2023年第2期17卷 2500-2511页

作者： Huang, Jiwei Wan, Jiangyuan Lv, Bofeng Ye, Qiang Chen, Ying China Univ Petr Beijing Key Lab Petr Data Min Beijing 102249 Peoples R China Mem Univ Newfoundland Dept Comp Sci St John NF A1C 5S7 Canada Beijing Informat Sci & Technol Univ Comp Sch Beijing 100101 Peoples R China

Mobile edge computing (MEC) and cloud computing (CC) have been considered as the key technologies to improve the task processing efficiency for Internet of Vehicles (IoV). In this article, we consider a random traffic flow and dynamic network environment scenario where MEC and CC are collaborated for processing delay-sensitive and computation-intensive tasks in IoV. We study the joint optimization of computation offloading and resource allocation (CORA) with the objective of minimizing the system cost of processing tasks subject to the processing delay and transmission rate constraints. To attack the challenges brought by the dynamic environment, we use the Markov decision process model for formulating the dynamic optimization problem, and apply a deep reinforcement learning (DRL) technique to deal with high-dimensional and continuous states and action spaces. Then, we design a CORA algorithm, which is able to effectively learn the optimal scheme by adapting to the network dynamics. Extensive simulation experiments are conducted, in which we compare the CORA algorithm with both non-DRL algorithms and DRL algorithms. The experimental results show that the CORA algorithm outperforms others with excellent training convergence and performance in processing delay and processing cost.

关键词： Task analysis Delays Vehicle dynamics computational modeling Bandwidth Heuristic algorithms Roads Cloud computing computation offloading deep reinforcement learning (DRL) Internet of Vehicles (IoV) mobile edge computing resource allocation

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Energy-efficient computation offloading strategy with task priority in cloud assisted multi-access edge computing

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2023年第1期148卷 298-313页

作者： He, Zhenli Xu, Yanan Liu, Di Zhou, Wei Li, Keqin Yunnan Univ Engn Res Ctr Cyberspace Kunming 650500 Peoples R China Shenzhen Univ Coll Elect & Informat Engn Shenzhen 518060 Peoples R China Yunnan Univ Sch Software Kunming 650500 Peoples R China Norwegian Univ Sci & Technol Dept Comp Sci N-7491 Trondheim Norway SUNY Coll New Paltz Dept Comp Sci New Paltz NY 12561 USA

Multi-access edge computing (MEC) provides cloud-like services at the edge of the radio access network close to mobile devices (MDs). This infrastructure can provide low-latency services to MDs and significantly reduce the pressure on the backbone network. However, the computing resources configured on an edge server (ES) are limited compared to a cloud data center (DC). It is difficult for ESs to satisfy the demands of MDs anytime and anywhere. Thus, a new paradigm that combines DC with ESs has been proposed to provide better capability and flexibility, namely, cloud-assisted MEC (CA-MEC). In CA-MEC, MDs can offload tasks to ESs and the DC, which means more elasticity and more complicated offloading decisions. This paper studies MDs' energy-efficient computation offloading strategy in CA-MEC, which considers two different priority tasks. First, we establish mathematical models to characterize the CA-MEC environment. Second, we mathematically analyze the MD's average task response time and average power consumption. Third, we propose efficient numerical algorithms to obtain a computation offloading strategy to optimize the energy efficiency of the target MD. Finally, we demonstrate several numerical examples and construct a comparative experiment to show the effectiveness of our algorithms. & COPY;2023 Elsevier B.V. All rights reserved.

关键词： computation offloading Energy efficiency Multi-access edge computing Queueing model Task priority

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