Mobile edge computing(MEC)-enabled satellite-terrestrial networks(STNs)can provide Internet of Things(IoT)devices with global computing services.Sometimes,the network state information is uncertain or unknown.To deal ...Mobile edge computing(MEC)-enabled satellite-terrestrial networks(STNs)can provide Internet of Things(IoT)devices with global computing services.Sometimes,the network state information is uncertain or unknown.To deal with this situation,we investigate online learning-based offloading decision and resource allocation in MEC-enabled STNs in this paper.The problem of minimizing the average sum task completion delay of all IoT devices over all time periods is formulated.We decompose this optimization problem into a task offloading decision problem and a computing resource allocation problem.A joint optimization scheme of offloading decision and resource allocation is then proposed,which consists of a task offloading decision algorithm based on the devices cooperation aided upper confidence bound(UCB)algorithm and a computing resource allocation algorithm based on the Lagrange multiplier method.Simulation results validate that the proposed scheme performs better than other baseline schemes.展开更多
Offloading application to cloud can augment mobile devices' computation capabilities for the emerging resource-hungry mobile application, however it can also consume both much time and energy for mobile device off...Offloading application to cloud can augment mobile devices' computation capabilities for the emerging resource-hungry mobile application, however it can also consume both much time and energy for mobile device offloading application remotely to cloud. In this paper, we develop a newly adaptive application offloading decision-transmission scheduling scheme which can solve above problem efficiently. Specifically, we first propose an adaptive application offloading model which allows multiple target clouds coexisting. Second, based on Lyapunov optimization theory, a low complexity adaptive offloading decision-transmission scheduling scheme has been proposed. And the performance analysis is also given. Finally, simulation results show that,compared with that all applications are executed locally, mobile device can save 68.557% average execution time and 67.095% average energy consumption under situations.展开更多
The traditional multi-access edge computing (MEC) capacity isoverwhelmed by the increasing demand for vehicles, leading to acute degradationin task offloading performance. There is a tremendous number ofresource-rich ...The traditional multi-access edge computing (MEC) capacity isoverwhelmed by the increasing demand for vehicles, leading to acute degradationin task offloading performance. There is a tremendous number ofresource-rich and idle mobile connected vehicles (CVs) in the traffic network,and vehicles are created as opportunistic ad-hoc edge clouds to alleviatethe resource limitation of MEC by providing opportunistic computing services.On this basis, a novel scalable system framework is proposed in thispaper for computation task offloading in opportunistic CV-assisted MEC.In this framework, opportunistic ad-hoc edge cloud and fixed edge cloudcooperate to form a novel hybrid cloud. Meanwhile, offloading decision andresource allocation of the user CVs must be ascertained. Furthermore, thejoint offloading decision and resource allocation problem is described asa Mixed Integer Nonlinear Programming (MINLP) problem, which optimizesthe task response latency of user CVs under various constraints. Theoriginal problem is decomposed into two subproblems. First, the Lagrangedual method is used to acquire the best resource allocation with the fixedoffloading decision. Then, the satisfaction-driven method based on trial anderror (TE) learning is adopted to optimize the offloading decision. Finally, acomprehensive series of experiments are conducted to demonstrate that oursuggested scheme is more effective than other comparison schemes.展开更多
This paper investigates the multi-Unmanned Aerial Vehicle(UAV)-assisted wireless-powered Mobile Edge Computing(MEC)system,where UAVs provide computation and powering services to mobile terminals.We aim to maximize the...This paper investigates the multi-Unmanned Aerial Vehicle(UAV)-assisted wireless-powered Mobile Edge Computing(MEC)system,where UAVs provide computation and powering services to mobile terminals.We aim to maximize the number of completed computation tasks by jointly optimizing the offloading decisions of all terminals and the trajectory planning of all UAVs.The action space of the system is extremely large and grows exponentially with the number of UAVs.In this case,single-agent learning will require an overlarge neural network,resulting in insufficient exploration.However,the offloading decisions and trajectory planning are two subproblems performed by different executants,providing an opportunity for problem-solving.We thus adopt the idea of decomposition and propose a 2-Tiered Multi-agent Soft Actor-Critic(2T-MSAC)algorithm,decomposing a single neural network into multiple small-scale networks.In the first tier,a single agent is used for offloading decisions,and an online pretrained model based on imitation learning is specially designed to accelerate the training process of this agent.In the second tier,UAVs utilize multiple agents to plan their trajectories.Each agent exerts its influence on the parameter update of other agents through actions and rewards,thereby achieving joint optimization.Simulation results demonstrate that the proposed algorithm can be applied to scenarios with various location distributions of terminals,outperforming existing benchmarks that perform well only in specific scenarios.In particular,2T-MSAC increases the number of completed tasks by 45.5%in the scenario with uneven terminal distributions.Moreover,the pretrained model based on imitation learning reduces the convergence time of 2T-MSAC by 58.2%.展开更多
基金supported by National Key Research and Development Program of China(2018YFC1504502).
文摘Mobile edge computing(MEC)-enabled satellite-terrestrial networks(STNs)can provide Internet of Things(IoT)devices with global computing services.Sometimes,the network state information is uncertain or unknown.To deal with this situation,we investigate online learning-based offloading decision and resource allocation in MEC-enabled STNs in this paper.The problem of minimizing the average sum task completion delay of all IoT devices over all time periods is formulated.We decompose this optimization problem into a task offloading decision problem and a computing resource allocation problem.A joint optimization scheme of offloading decision and resource allocation is then proposed,which consists of a task offloading decision algorithm based on the devices cooperation aided upper confidence bound(UCB)algorithm and a computing resource allocation algorithm based on the Lagrange multiplier method.Simulation results validate that the proposed scheme performs better than other baseline schemes.
基金supported by National Natural Science Foundation of China (Grant No.61261017, No.61571143 and No.61561014)Guangxi Natural Science Foundation (2013GXNSFAA019334 and 2014GXNSFAA118387)+3 种基金Key Laboratory of Cognitive Radio and Information Processing, Ministry of Education (No.CRKL150112)Guangxi Key Lab of Wireless Wideband Communication & Signal Processing (GXKL0614202, GXKL0614101 and GXKL061501)Sci.and Tech.on Info.Transmission and Dissemination in Communication Networks Lab (No.ITD-U14008/KX142600015)Graduate Student Research Innovation Project of Guilin University of Electronic Technology (YJCXS201523)
文摘Offloading application to cloud can augment mobile devices' computation capabilities for the emerging resource-hungry mobile application, however it can also consume both much time and energy for mobile device offloading application remotely to cloud. In this paper, we develop a newly adaptive application offloading decision-transmission scheduling scheme which can solve above problem efficiently. Specifically, we first propose an adaptive application offloading model which allows multiple target clouds coexisting. Second, based on Lyapunov optimization theory, a low complexity adaptive offloading decision-transmission scheduling scheme has been proposed. And the performance analysis is also given. Finally, simulation results show that,compared with that all applications are executed locally, mobile device can save 68.557% average execution time and 67.095% average energy consumption under situations.
基金supported by the National Natural Science Foundation of China (61871400)Natural Science Foundation of Jiangsu Province (BK20211227)Scientific Research Project of Liupanshui Normal University (LPSSYYBZK202207).
文摘The traditional multi-access edge computing (MEC) capacity isoverwhelmed by the increasing demand for vehicles, leading to acute degradationin task offloading performance. There is a tremendous number ofresource-rich and idle mobile connected vehicles (CVs) in the traffic network,and vehicles are created as opportunistic ad-hoc edge clouds to alleviatethe resource limitation of MEC by providing opportunistic computing services.On this basis, a novel scalable system framework is proposed in thispaper for computation task offloading in opportunistic CV-assisted MEC.In this framework, opportunistic ad-hoc edge cloud and fixed edge cloudcooperate to form a novel hybrid cloud. Meanwhile, offloading decision andresource allocation of the user CVs must be ascertained. Furthermore, thejoint offloading decision and resource allocation problem is described asa Mixed Integer Nonlinear Programming (MINLP) problem, which optimizesthe task response latency of user CVs under various constraints. Theoriginal problem is decomposed into two subproblems. First, the Lagrangedual method is used to acquire the best resource allocation with the fixedoffloading decision. Then, the satisfaction-driven method based on trial anderror (TE) learning is adopted to optimize the offloading decision. Finally, acomprehensive series of experiments are conducted to demonstrate that oursuggested scheme is more effective than other comparison schemes.
基金supported in part by the National Natural Science Foundation of China under Grant 62271306,Grant 62072410,and Grant 62331017in part by the Fundamental Research Funds for the Provincial Universities of Zhejiang under Grant RF-B2022002。
文摘This paper investigates the multi-Unmanned Aerial Vehicle(UAV)-assisted wireless-powered Mobile Edge Computing(MEC)system,where UAVs provide computation and powering services to mobile terminals.We aim to maximize the number of completed computation tasks by jointly optimizing the offloading decisions of all terminals and the trajectory planning of all UAVs.The action space of the system is extremely large and grows exponentially with the number of UAVs.In this case,single-agent learning will require an overlarge neural network,resulting in insufficient exploration.However,the offloading decisions and trajectory planning are two subproblems performed by different executants,providing an opportunity for problem-solving.We thus adopt the idea of decomposition and propose a 2-Tiered Multi-agent Soft Actor-Critic(2T-MSAC)algorithm,decomposing a single neural network into multiple small-scale networks.In the first tier,a single agent is used for offloading decisions,and an online pretrained model based on imitation learning is specially designed to accelerate the training process of this agent.In the second tier,UAVs utilize multiple agents to plan their trajectories.Each agent exerts its influence on the parameter update of other agents through actions and rewards,thereby achieving joint optimization.Simulation results demonstrate that the proposed algorithm can be applied to scenarios with various location distributions of terminals,outperforming existing benchmarks that perform well only in specific scenarios.In particular,2T-MSAC increases the number of completed tasks by 45.5%in the scenario with uneven terminal distributions.Moreover,the pretrained model based on imitation learning reduces the convergence time of 2T-MSAC by 58.2%.
