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dynamic computation offloading With Imperfect State Information in Energy Harvesting Small Cell Networks: A Partially Observable Stochastic Game

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IEEE WIRELESS COMMUNICATIONS LETTERS 2020年第8期9卷 1300-1304页

作者： Tang, Qinqin Xie, Renchao Huang, Tao Feng, Wei Liu, Yunjie BUPT State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China Purple Mt Labs Dept Future Networks Nanjing 211111 Peoples R China MIIT Dept Informat Technol Applicat & Software Serv Beijing 100804 Peoples R China

The combination of energy harvesting small cell networks (EH-SCNs) and mobile edge computing (MEC) has been considered as an effective means to improve the performance of mobile networks and provide users with a higher quality of service (QoS). In this letter, we investigate the decentralized computation offloading problem in heterogeneous EH-SCNs with MEC, where heterogeneous small cell base stations (SBSs) are rational individuals with interests to maximize their own benefits while considering their QoS requirements. Different from existing works, we address the challenge that heterogeneous SBSs may unwilling to expose their own information about the system state and offloading decisions. We formulate the problem as a partially observable stochastic game (POSG), in which SBSs can make optimal offloading decisions with imperfect state information. We analyze the local equilibrium, and propose a stochastic offloading algorithm to obtain the approximate optimal solution. Numerical results validate the effectiveness of the proposed scheme.

关键词： Task analysis Quality of service Energy consumption Servers Delays Energy harvesting Microcell networks dynamic computation offloading energy harvesting small cell networks mobile edge computing POSG

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dynamic computation offloading in time-varying environment for ultra-dense networks:a stochastic game approach

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The Journal of China Universities of Posts and Telecommunications 2021年第2期28卷 24-37页

作者： Xie Renchao Liu Xu Duan Xuefei Tang Qinqin Yu Fei Richard Huang Tao State Key Laboratory of Networking and Switching Technology Beijing University of Posts and TelecommunicationsBeijing 100876China Guangdong Communications and Networks Institute Guangzhou 510700China Department of Future Networks Purple Mountain LaboratoryNanjing 211111China Department of Systems and Computer Engineering Carleton UniversityOttawaCanada

To meet the demands of large-scale user access with computation-intensive and delay-sensitive applications,combining ultra-dense networks(UDNs)and mobile edge computing(MEC)are considered as important *** the MEC enabled UDNs,one of the most important issues is computation *** a number of work have been done toward this issue,the problem of dynamic computation offloading in time-varying environment,especially the dynamic computation offloading problem for multi-user,has not been fully ***,in order to fill this gap,the dynamic computation offloading problem in time-varying environment for multi-user is considered in this *** considering the dynamic changes of channel state and users’queue state,the dynamic computation offloading problem for multi-user is formulated as a stochastic game,which aims to optimize the delay and packet loss rate of *** find the optimal solution of the formulated optimization problem,Nash Q-learning(NQLN)algorithm is proposed which can be quickly converged to a Nash equilibrium ***,extensive simulation results are presented to demonstrate the superiority of NQLN *** is shown that NQLN algorithm has better optimization performance than the benchmark schemes.

关键词： dynamic computation offloading time-varying environment stochastic game ultra-dense networks(UDNs) mobile edge computing(MEC)

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Multi-User dynamic computation offloading and Resource Allocation in 5G MEC Heterogeneous Networks With Static and dynamic Subchannels

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2023年第11期72卷 14924-14938页

作者： Liu, Liqing Yuan, Xiaoming Chen, Decheng Zhang, Ning Sun, Haifeng Taherkordi, Amir Northeastern Univ Qinhuangdao Branch Campus Qinhuangdao 066004 Hebei Peoples R China Univ Windsor Dept Elect & Comp Engn Windsor ON N9B 3P4 Canada Southwest Univ Sci & Technol Mianyang 621053 Sichuan Peoples R China Univ Oslo Dept Informat N-0316 Oslo Norway

With the rapid development of Mobile Edge Computing (MEC) technology, the computationally intensive requests of end devices can be offloaded to MEC servers directly, which equipped at the edge of wireless networks. Through offloading, the performances such as the execution delay as well as the energy consumption can be effectively improved, which can significantly enhance the quality of user experience. Given the dynamics and randomness of computation requests arrival, the energy in the battery, the radio network environment, and the computation resource in the MEC server, it is a challenge to perform efficient offloading. Based on these problems, this article proposes dynamic optimization schemes with queuing theory for the cases of the static subchannel and dynamic subchannel during a time slot separately in 5G MEC heterogeneous networks with multiple MDs equipped with the function of energy harvesting. In the schemes, offloading decisions and radio allocation strategies will be dynamically coordinated. They are also jointly allocated along with changing wireless communication resources and computation demands aiming to minimize the system average execution delay. Specifically, it is assumed that the offloading requests can be transmitted through either macro base stations or small base stations. In the case of the static subchannel, a joint resource allocation and computation offloading scheme based on Lyapunov optimization and Simulated Annealing Genetic Algorithm (SAGA) is put forward. As for the dynamic subchannel, the leader-and-follower model is adopted and solved by SAGA and Sequential Quadratic Programming (SQP) method. At last, the effectiveness of the proposed schemes is verified through several simulations.

关键词： 5G mobile communication Vehicle dynamics Delays dynamic scheduling Servers Resource management Optimization 5G heterogeneous network dynamic computation offloading offloading strategy subchannel assignment scheme transmission path decision

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Topology-Aware dynamic computation offloading in Vehicular Networks 93

Topology-Aware Dynamic Computation Offloading in Vehicular N...

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IEEE 93rd Vehicular Technology Conference (VTC-Spring)

作者： Liu, Zhang Gau, Zhibin LiWang, Minghui Chen, Fangzhe Wang, Yilin Huang, Lianfen Tang, Yuliang Xiamen Univ Dept Informat & Commun Engn Xiamen 361005 Fujian Peoples R China Xiamen Univ Key Lab Digital Fujian IoT Commun Architecture & Xiamen 361005 Fujian Peoples R China Western Univ Dept Elect & Comp Engn London ON Canada

ISBN: (纸本)9781728189642

Driven by the tremendous in vehicular networks computation-intensive application demands, the incorporation of mobile edge computing (MEC) and vehicular cloud is convinced as a promising paradigm to fulfill computation offloading requirements. However, the changing vehicular communication topology (CVCT) poses a significant challenge for offloading directed acyclic graph (DAG) model application. Due to the precedence and connection constraint between different sub-jobs, the successful offloading of DAG-enabled apllication will be disturbed even interrupted without considering CVCT. To address this problem, we propose a topology-aware dynamic computaion offloading mechanism and adopt simulated annealing algorithm (TASA) to jointly optimize the energy consumption and completion time under dynamic environment, while guaranteeing the convergence of the proposed method. Simulation results reveal the effectiveness of the proposed method in overcoming CVCT's influence.

关键词： Vehicular networks mobile edge computing vehicle mobility communication topology dynamic computation offloading

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dynamic Resource Allocation and computation offloading for IoT Fog Computing System

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2021年第5期17卷 3348-3357页

作者： Chang, Zheng Liu, Liqing Guo, Xijuan Sheng, Quan Univ Elect Sci & Technol China Sch Comp Sci & Engn Chengdu 610051 Peoples R China Univ Jyvaskyla Fac Informat Technol FIN-40014 Jyvaskyla Finland Northeasten Univ Sch Comp & Commun Engn Qinhuangdao 066004 Hebei Peoples R China Yanshan Univ Coll Informat Sci & Engn Qinhuangdao 066004 Hebei Peoples R China Tianjin Univ Sch Precis Instrument & Optoelect Engn Tianjin 300072 Peoples R China

Fog computing system is able to facilitate computation-intensive applications and emerges as one of the promising technology for realizing the Internet of Things (IoT). By offloading the computational tasks to the fog node (FN) at the network edge, both the service latency and energy consumption can be improved, which is significant for industrial IoT applications. However, the dynamics of computational resource usages in the FN, the radio environment and the energy in the battery of IoT devices make the offloading mechanism design become challenging. Therefore, in this article, we propose a dynamic optimization scheme for the IoT fog computing system with multiple mobile devices (MDs), where the radio and computational resources, and offloading decisions, can be dynamically coordinated and allocated with the variation of radio resources and computation demands. Specifically, with the objective to minimize the system cost related to latency, energy consumption, and weights of MDs, we propose a joint computation offloading and radio resource allocation algorithm based on Lyapunov optimization. Through minimizing the derived upper bound of the Lyapunov drift-plus-penalty function, we divide the main problem into several subproblems at each time slot and address them accordingly. Through performance evaluation, the effectiveness of the proposed scheme can be verified.

关键词： Edge computing Resource management Optimization Cloud computing Task analysis Energy consumption computational modeling dynamic computation offloading edge computing energy harvesting fog computing Lyapunov optimization resource allocation

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dynamic Resource Allocation and computation offloading for Edge Computing System 9th

Dynamic Resource Allocation and Computation Offloading for E...

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16th IFIP WG 12.5 International Conference on Artificial Intelligence Applications and Innovations (AIAI) / 9th Mining Humanistic Data Workshop (MHDW) / 5th Workshop on 5G-Putting Intelligence to the Network Edge (5G-PINE)

作者： Chang, Zheng Liu, Liqing Guo, Xijuan Chen, Tao Ristaniemi, Tapani Univ Jyvaskyla Fac Informat Technol POB 35 Jyvaskyla 40014 Finland Yanshan Univ Coll Informat Sci & Engn Qinhuangdao 066004 Peoples R China VTT Tech Res Ctr Finland Espoo Finland

ISBN: (纸本)9783030491925;9783030491901;9783030491895

In this work, we propose a dynamic optimization scheme for an edge computing system with multiple users, where the radio and computational resources, and offloading decisions, can be dynamically allocated with the variation of computation demands, radio channels and the computation resources. Specifically, with the objective to minimize the energy consumption of the considered system, we propose a joint computation offloading, radio and computational resource allocation algorithm based on Lyapunov optimization. Through minimizing the derived upper bound of the Lyapunov drift-plus-penalty function, the main problem is divided into several sub-problems at each time slot and are addressed separately. The simulation results demonstrate the effectiveness of the proposed scheme.

关键词： Edge computing dynamic computation offloading Lyapunov optimization Resource allocation

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Graph Reinforcement Learning-based CNN Inference offloading in dynamic Edge Computing

Graph Reinforcement Learning-based CNN Inference Offloading ...

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IEEE Global Communications Conference (GLOBECOM)

作者： Li, Nan Iosifidis, Alexandros Zhang, Qi Aarhus Univ Dept Elect & Comp Engn DIGIT Aarhus Denmark

ISBN: (纸本)9781665435406

This paper studies the computational offloading of CNN inference in dynamic multi-access edge computing (MEC) networks. To address the uncertainties in communication time and Edge servers' available capacity, we use early-exit mechanism to terminate the computation earlier to meet the deadline of inference tasks. We design a reward function to trade off the communication, computation and inference accuracy, and formulate the offloading problem of CNN inference as a maximization problem with the goal of maximizing the average inference accuracy and throughput in long term. To solve the maximization problem, we propose a graph reinforcement learningbased early-exit mechanism (GRLE), which outperforms the state-of-the-art work, deep reinforcement learning-based online offloading (DROO) and its enhanced method, DROO with early-exit mechanism (DROOE), under different dynamic scenarios. The experimental results show that GRLE achieves the average accuracy up to 3.41x over graph reinforcement learning (GRL) and 1.45x over DROOE, which shows the advantages of GRLE for offloading decision-making in dynamic MEC.

关键词： dynamic computation offloading CNN inference Graph reinforcement learning Edge computing Service reliability

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