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service caching and Task Offloading for Mobile Edge Computing-Enabled Intelligent Connected Vehicles

Journal of Shanghai Jiaotong university(Science) 2021年第5期26卷 670-679页

作者： HUANG Mengting YI Yuhan ZHANG Guanglin College of Information Science and Technology Donghua UniversityShanghai 201620China

The development of intelligent connected vehicles(ICVs)has tremendously inspired the emergence of a new computing paradigm called mobile edge computing(MEC),which meets the demands of delay-sensitive on-vehicle *** existing studies focusing on the issue of task offloading in ICVs assume that the MEC server can directly complete computation tasks without considering the necessity of service ***,this is unrealistic in practice because a large number of tasks require the use of corresponding third-party libraries and databases,that is,service ***,we investigate the delay optimization in an MEC-enabled ICVs system with multiple mobile vehicles,resource-limited base stations(BSs),and one cloud *** aim to determine the optimal service caching and task offloading decisions to minimize the overall system delay using mixed-integer nonlinear *** address this problem,we first convert it into a quadratically constrained quadratic program and then propose an efficient semidefinite relaxation-based joint service caching and task offloading(JSCTO)algorithm to obtain the service caching and task offloading *** the simulations,we validate the efficiency of our proposed method by setting different numbers of vehicles and the storage capacity of *** results show that our proposed JSCTO algorithm can significantly decrease the total delay of all offloaded tasks compared with the cloud processing only scheme.

关键词： intelligent connected vehicle(ICV) mobile edge computing(MEC) service caching task offloading delay cost

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Learning-Based Joint service caching and Load Balancing for MEC Blockchain Networks

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China Communications 2023年第1期20卷 125-139页

作者： Wenqian Zhang Wenya Fan Guanglin Zhang Shiwen Mao College of Information Science and Technology Donghua UniversityShanghai 201620China Engineering Research Center of Digitized Textile and Apparel Technology Ministry of EducationShanghai 201620China Department of Electrical and Computer Engineering Auburn UniversityAuburnAL 36849-5201USA

Integrating the blockchain technology into mobile-edge computing(MEC)networks with multiple cooperative MEC servers(MECS)providing a promising solution to improving resource utilization,and helping establish a secure reward mechanism that can facilitate load balancing among *** addition,intelligent management of service caching and load balancing can improve the network utility in MEC blockchain networks with multiple types of *** this paper,we investigate a learningbased joint service caching and load balancing policy for optimizing the communication and computation resources allocation,so as to improve the resource utilization of MEC blockchain *** formulate the problem as a challenging long-term network revenue maximization Markov decision process(MDP)*** address the highly dynamic and high dimension of system states,we design a joint service caching and load balancing algorithm based on the double-dueling Deep Q network(DQN)*** simulation results validate the feasibility and superior performance of our proposed algorithm over several baseline schemes.

关键词： cooperative mobile-edge computing blockchain workload offloading service caching load balancing deep reinforcement learning(DRL)

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Deep Reinforcement Learning-Based Task Offloading and service Migrating Policies in service caching-Assisted Mobile Edge Computing

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China Communications 2024年第4期21卷 88-103页

作者： Ke Hongchang Wang Hui Sun Hongbin Halvin Yang School of Computer Technology and Engineering Changchun Institute of TechnologyChangchun 130012China College of Computer Science and Engineering Changchun University of TechnologyChangchun 130012China Department of Electronic and Electrical Engineering University College LondonLondonUK

Emerging mobile edge computing(MEC)is considered a feasible solution for offloading the computation-intensive request tasks generated from mobile wireless equipment(MWE)with limited computational resources and *** to the homogeneity of request tasks from one MWE during a longterm time period,it is vital to predeploy the particular service cachings required by the request tasks at the MEC *** this paper,we model a service caching-assisted MEC framework that takes into account the constraint on the number of service cachings hosted by each edge server and the migration of request tasks from the current edge server to another edge server with service caching required by ***,we propose a multiagent deep reinforcement learning-based computation offloading and task migrating decision-making scheme(MBOMS)to minimize the long-term average weighted *** proposed MBOMS can learn the near-optimal offloading and migrating decision-making policy by centralized training and decentralized *** and comprehensive simulation results reveal that our proposed MBOMS can converge well after training and outperforms the other five baseline algorithms.

关键词： deep reinforcement learning mobile edge computing service caching service migrating

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