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computation offloading in Multi-Cell Networks With Collaborative Edge-Cloud Computing: A Game Theoretic Approach

IEEE TRANSACTIONS ON MOBILE COMPUTING 2024年第3期23卷 2093-2106页

作者： Wu, Liantao Sun, Peng Wang, Zhibo Li, Yanjun Yang, Yang Shanghai Tech Univ Sch Informat Sci & Technol Shanghai 201210 Peoples R China East China Normal Univ Software Engn Inst Shanghai 200062 Peoples R China Hunan Univ Coll Comp Sci & Elect Engn Changsha 410082 Peoples R China Zhejiang Univ Coll Comp Sci & Technol Hangzhou 310027 Peoples R China Zhejiang Univ Technol Sch Comp Sci & Technol Hangzhou 310023 Peoples R China Terminus Grp Beijing 100027 Peoples R China Peng Cheng Lab Shenzhen 518055 Peoples R China Shenzhen Smart City Technol Dev Grp Co Ltd Shenzhen 518046 Peoples R China

With the widespread application of 5G and the Internet of things (IoT), edge computing and cloud computing have been collaboratively utilized for task offloading and processing. However, though the massive devices (e.g., smartphones) are organized into multi-cells, most of the existing works do not explore the computation offloading for edge-cloud computing under inter-cell interference. Thus, the offloading decisions may be inappropriate as the transmission rate is overestimated. To address this issue, we propose COMEC, a novel computation offloading scheme in Multi-cell networks with Edge-Cloud collaboration, which could minimize the total cost in terms of delay and energy consumption. Specifically, we first formulate COMEC as an optimization problem taking into account inter-cell interference. Then, considering the offloading decisions of all users are coupled, a non-cooperative game is formulated to minimize the total cost of each user in a distributed manner. We prove that this game is a general (ordinal) potential game and possesses a pure strategy Nash equilibrium (NE). Based on the finite improvement property of the potential game, we develop the corresponding computation offloading algorithm to achieve the NE. Finally, simulation results show that the proposed scheme can achieve superior performance in overall system cost compared with other baselines.

关键词： Servers Games Task analysis Costs Delays Cloud computing Energy consumption Collaborative edge-cloud computing computation offloading potential game multi-cell interference

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computation offloading and Band Selection for IoT Devices in Multi-Access Edge Computing

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ACM TRANSACTIONS ON MODELING AND COMPUTER SIMULATION 2025年第2期35卷

作者： Ray, Kaustabha Banerjee, Ansuman IBM India Res Lab Bangalore India Indian Stat Inst Kolkata West Bengal India

The advent of Multi-Access Edge Computing (MEC) has enabled service providers to mitigate high network latencies often encountered in accessing cloud services. The key idea of MEC involves service providers deploying containerized application services on MEC servers situated near Internet-of-Things (IoT) device users. The users access these services via wireless base stations with ultra low latency. computation tasks of IoT devices can then either be executed locally on the devices or on the MEC servers. A key cornerstone of the MEC environment is an offloading policy utilized to determine whether to execute computation tasks on IoT devices or to offload the tasks to MEC servers for processing. In this work, we propose a two-phase Probabilistic Model Checking-based offloading policy catering to IoT device user preferences. The first stage evaluates the tradeoffs between local vs. server execution while the second stage evaluates the tradeoffs between choice of wireless communication bands for offloaded tasks. We present experimental results in practical scenarios on data gathered from an IoT test-bed setup with benchmark applications to show the benefits of an adaptive preference-aware approach over conventional approaches in the MEC offloading context.

关键词： Multi-access edge computing computation offloading probabilistic model checking

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computation offloading and Resource Allocation Optimization for Mobile Edge Computing-Aided UAV-RIS Communications

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IEEE ACCESS 2024年 12卷 107971-107983页

作者： Truong, Phuc Q. Do-Duy, Tan Masaracchia, Antonino Vo, Nguyen-Son Phan, Van-Ca Ha, Dac-Binh Duong, Trung Q. Ho Chi Minh City Univ Technol & Educ Fac Elect & Elect Engn Dept Comp & Commun Engn Ho Chi Minh City 700000 Vietnam Queens Univ Belfast Sch Elect Elect Engn & Comp Sci Belfast BT7 1NN Antrim North Ireland Queen Mary Univ London Sch Elect Engn & Comp Sci London E1 4NS England Duy Tan Univ Inst Fundamental & Appl Sci Ho Chi Minh City 700000 Vietnam Duy Tan Univ Fac Elect Elect Engn Da Nang 550000 Vietnam Mem Univ Newfoundland Fac Engn & Appl Sci St John NF A1C 5S7 Canada

The concept of Mobile Edge Computing (MEC) has been recently highlighted as a key enabling technology for the deployment of sixth-generation (6G) wireless network services. On the other hand, the possibility of combining Unmanned Aerial Vehicles (UAV) with Reconfigurable Intelligent Surfaces (RIS) has also been recognized as a powerful communication paradigm able to provide improved propagation characteristics of wireless communication channels, as well as increased capacity and extended coverage. Then, the possibility of merging the characteristics of such a communication paradigm with the one provided through MEC represents a valid solution to fulfill the main requirements of 6G networks. In this paper, we consider the combination of computation offloading and resource allocation in an MEC-based system where the MEC server is hosted by a massive MIMO base station, which serves multiple macro-cells assisted by a UAV-equipped RIS. In this context, we focus on minimising the latency for executing tasks of all user equipment (UE) within the considered scenario. To tackle this problem, we formulate an optimisation problem that jointly optimises computation offloading from user equipment (UE) towards the MEC server, and communication resources in the underlying UAV-assisted and RIS-aided network. The extensive simulation results demonstrate how the proposed method outperforms in terms of providing reduced latency for the considered system when compared with other conventional schemes.

关键词： Autonomous aerial vehicles Optimization Resource management Task analysis 6G mobile communication Wireless networks Base stations Edge computing Reconfigurable intelligent surfaces computation offloading mobile edge computing reconfigurable intelligent surfaces resource allocation unmanned aerial vehicle

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computation offloading and Trajectory Planning of Multi-UAV-Enabled MEC: A Knowledge-Assisted Multiagent Reinforcement Learning Approach

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2024年第5期73卷 7077-7088页

作者： Li, Xulong Qin, Yunhui Huo, Jiahao Wei, Huangfu Univ Sci & Technol Beijing Beijing Adv Innovat Ctr Mat Genome Engn Beijing Engn & Technol Res Ctr Convergence Network Sch Comp & Commun Engn Beijing 100083 Peoples R China Beijing Inst Technol Sch Cyberspace Sci & Technol Beijing 100081 Peoples R China Beijing Inst Technol Yangtze Delta Reg Acad Jiaxing 314019 Peoples R China

Compared with centralized cloud computing, mobile edge computing (MEC) enables Internet of Things (IoT) devices to offload computation-intensive tasks to the edge of the network closer to them for processing, which can effectively save energy consumption of IoT devices and alleviate network congestion and high latency problems. However, the traditional terrestrial MEC system cannot adapt to scenarios such as rapid network recovery after disasters and emergency rescue due to its poor flexibility and high deployment cost, and assembling edge servers to unmanned aerial vehicles (UAVs) to assist in rapidly building mobile edge networks is a feasible solution. Therefore, this paper considers a multi-UAV-enabled MEC network with the optimization objectives of maximizing the processing success rate of computational tasks and fairness of system, while minimizing the processing delay of computational tasks. We investigate the computation offloading problem and the trajectory planning problem from the perspectives of IoT devices and UAVs, respectively. Then model them as Markov decision processes (MDPs) and propose a joint optimization scheme based on expert knowledge-assisted multi-agent reinforcement learning algorithms. Simulation results show that the proposed algorithm has significant advantages over baseline algorithms in terms of processing success rate and delay of computational tasks as well as fairness of the system.

关键词： Internet of Things Optimization Task analysis Autonomous aerial vehicles Trajectory planning Reinforcement learning computational modeling Mobile edge computing (MEC) unmanned aerial vehicle (UAV) multi-agent reinforcement learning (MARL) computation offloading trajectory planning

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computation offloading and Quantization Schemes for Federated Satellite-Ground Graph Networks

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2024年第10期23卷 14140-14154页

作者： Gong, Yongkang Yu, Dongxiao Cheng, Xiuzhen Yuen, Chau Bennis, Mehdi Debbah, Merouane Shandong Univ SDU Sch Comp Sci & Technol Qingdao 266237 Peoples R China Nanyang Technol Univ NTU Sch Elect & Elect Engn Singapore 639798 Singapore Univ Oulu Ctr Wireless Commun Oulu 90014 Finland Khalifa Univ Sci & Technol KU 6G Res Ctr Abu Dhabi U Arab Emirates Univ Paris Saclay Cent Supelec F-91192 Gif Sur Yvette France

Satellite-Ground integrated networks (SGINs) are regarded as promising network architecture, which can provide global coverage, large broadband and mega access services for massive terrestrial users. Furthermore, it is beneficial to reducing network congestion, releasing network resources and achieving computation offloading functions. However, the SGIN graph structure is time-varying and highly complex, lack of fixed node orders or reference nodes, which result in dynamic multi-modal features. Hence, we consider a SGIN directed graph model to minimize the total latency while improving the model prediction accuracy, and then perform the computation offloading and quantization schemes. Specifically, we envision a spatial graph convolutional neural network framework to adapt to the dynamic SGIN graph nodes and size, and then propose a centrally deep reinforcement learning aided multi-node federated learning (CDRFL) framework to optimize the CPU cycle frequency, transmission bandwidth and the number of quantization bits to accelerate the convergence round. Extensive theoretical analyses verify the graph permutation property between SGIN graph structure and optimization problems, and demonstrate the upper bound of quantization error via massive mathematical derivation. Finally, the experimental results indicate that the proposed CDRFL framework outperforms some existing benchmarks with reference to FL convergence analysis, average latency and transmission energy consumption for all independent identically distribution (IID) and non-IID data.

关键词： Quantization (signal) Satellites computational modeling Servers Wireless communication Task analysis Convergence Satellite-ground integrated networks (SGINs) graph structure spatial graph convolutional neural network framework centrally deep reinforcement learning aided multi-node federated learning (CDRFL) computation offloading quantization schemes

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computation offloading with Resource Allocation Based on DDPG in MEC

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JOURNAL OF INFORMATION PROCESSING SYSTEMS 2024年第2期20卷 226-238页

作者： Moon, Sungwon Lim, Yujin Sookmyung Womens Univ Dept IT Engn Seoul South Korea

Recently, multi-access edge computing (MEC) has emerged as a promising technology to alleviate the computing burden of vehicular terminals and efficiently facilitate vehicular applications. The vehicle can improve the quality of experience of applications by offloading their tasks to MEC servers. However, channel conditions are time -varying due to channel interference among vehicles, and path loss is time -varying due to the mobility of vehicles. The task arrival of vehicles is also stochastic. Therefore, it is difficult to determine an optimal offloading with resource allocation decision in the dynamic MEC system because offloading is affected by wireless data transmission. In this paper, we study computation offloading with resource allocation in the dynamic MEC system. The objective is to minimize power consumption and maximize throughput while meeting the delay constraints of tasks. Therefore, it allocates resources for local execution and transmission power for offloading. We define the problem as a Markov decision process, and propose an offloading method using deep reinforcement learning named deep deterministic policy gradient. Simulation shows that, compared with existing methods, the proposed method outperforms in terms of throughput and satisfaction of delay constraints.

关键词： computation offloading DDPG MEC Resource Allocation

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computation offloading and Resource Allocation in Satellite-Terrestrial Integrated Networks: A Deep Reinforcement Learning Approach

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IEEE ACCESS 2024年 12卷 97184-97195页

作者： Xie, Junfeng Jia, Qingmin Chen, Youxing Wang, Wei North Univ China Sch Informat & Commun Engn Taiyuan 030051 Peoples R China Future Network Res Ctr Purple Mt Labs Nanjing 211111 Peoples R China

Satellite networks can enhance global network coverage without geographical restrictions. By applying edge computing paradigm to satellite networks, they can provide communication and computation services for Internet of Remote Things (IoRT) mobile devices (IMDs) at any time. Thus, in 6G mobile systems, satellite networks serve as a complement to terrestrial networks. This paper places its emphasis on satellite-terrestrial integrated networks (STINs). computation offloading and resource allocation (CORA) play a crucial role in STINs given the limited communication and computation resources of satellites. In this paper, we investigate the CORA to minimize system energy consumption while ensuring the latency tolerance via jointly optimizing the offloading decision and the allocation of radio and computation resources. Taking into account the dynamic characteristics of network conditions, the CORA problem is formulated as a Markov decision process (MDP). Subsequently, an algorithm based on twin delayed deep deterministic policy gradient (TD3) is designed to automatically determine the optimal decisions. Finally, the convergence and superiority of the proposed algorithm in terms of energy efficiency are evaluated through extensive simulation experiments.

关键词： computation offloading resource allocation STINs energy efficiency TD3 computation offloading resource allocation STINs energy efficiency TD3

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computation offloading for Cloud-Edge Collaborative Virtual Power Plant Frequency Regulation Service

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IEEE TRANSACTIONS ON SMART GRID 2024年第5期15卷 5232-5244页

作者： Lin, Chengrong Hu, Bo Shao, Changzheng Xie, Kaigui Peng, Jimmy Chih-Hsien Chongqing Univ Dept Elect Engn Chongqing 400044 Peoples R China Natl Univ Singapore Elect & Comp Engn Dept Singapore Singapore

This article proposes a computation offloading method to improve the revenue of a virtual power plant (VPP). The cloud-edge collaboration framework is developed to facilitate the computation offloading task, while handling the communication and computational delays encountered in the offered frequency regulation. The impact of such time delays on a VPP's frequency regulation is worth further investigation. To this end, this study developed a revenue optimization model for a VPP based on the related regulation performance. An evaluation model was also built to account for the impact of time delay on a VPP. Both models are used to quantify the benefits of computation offloading to boost the revenues of the VPP. Moreover, a necessary condition for using the computation offloading technique is proposed. A greedy algorithm is designed to make the offloading decisions. Numerical studies demonstrated the proposed offloading method could improve VPP revenue by more than 31% compared to the method without computation offloading.

关键词： Servers Cloud computing Delays Collaboration Task analysis Costs Frequency control Virtual power plant frequency regulation cloud computing edge computing computation offloading

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computation offloading and heterogeneous task caching in MEC-enabled vehicular networks

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JOURNAL OF SUPERCOMPUTING 2023年第15期79卷 17098-17122页

作者： Wu, Ruizhi Li, Bo Yunnan Univ Sch Informat Sci & Engn Kunming 650091 Yunnan Peoples R China

Given the rapid growth of business types and the characteristics of delay-sensitive tasks in vehicular networks, vehicles' computing power is insufficient to handle the demands of a wide range of computing tasks. Considering the privacy of vehicles' tasks in the future IoV, we categorize vehicular tasks into two groups, which is our definition about heterogeneous tasks. To fulfill the massive demands for processing and storage resources, more and more heterogeneous tasks are going to be processed in mobile edge computing (MEC) servers. In this paper, we have proposed a vehicular network architecture that allows for heterogeneous tasks to be processed on the roadside units co-located with MEC servers. Based on this architecture, taking into account communication, task requesting, computing, and caching, a mixed integer nonlinear programming problem is formulated to jointly optimize the task completion delay and energy consumption. To solve the problem, a grey wolf optimizer-joint optimization algorithm is designed to obtain a better offloading and caching decision. Experimental results show that this algorithm has significantly improved performance over other algorithms.

关键词： Vehicular networks computation offloading Edge caching Heterogeneous tasks

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computation offloading and Resource Allocation for Vehicle-Assisted Edge Computing Networks With Joint Access and Backhaul

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IEEE ACCESS 2024年 12卷 110248-110259页

作者： Huang, Bo Zhou, Yan Zhang, Xu Chen, Jie Shang, Li Suzhou Vocat Univ Sch Elect Informat Engn Suzhou 215104 Peoples R China

The edge computing utilizes vehicles as resources to assist in computational offloading can shorten the distance between users and computing servers, thereby improving the reliability of communication between them. In this paper, we investigate a Vehicle-assisted Edge Computing (VEC) model by jointly considering wireless access and backhaul links, and formulate an optimization problem that combines computational offloading and resource allocation, aiming at minimizing system delay. Further, the formulated problem is decomposed into two subproblems, e.g., computation offloading and resource allocation. In particular, we propose a new computational offloading approach that models the offloading decision for joint wireless access and backhaul as a potential game. The Nash equilibrium is guaranteed by the rational design of potential function, and the corresponding solution is solved by a backward induction method. On the other hand, the resource allocation subproblem is transformed from a nonconvex to a convex optimization problem based on equivalent transformation with successive convex approximation methods and finally derives the optimal solution satisfying Karush-Kuhn-Tucker (KKT) conditions. Simulation results show that the proposed algorithm have near-optimal performance and is superior to the state-of-the-art over a wide range of parameter settings.

关键词： computational modeling computation offloading access/backhaul link resource allocation edge computing computational modeling computation offloading access/backhaul link resource allocation edge computing

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