Mobile Edge Computing(MEC)is one of the most promising techniques for next-generation wireless communication systems.In this paper,we study the problem of dynamic caching,computation offloading,and resource allocation...Mobile Edge Computing(MEC)is one of the most promising techniques for next-generation wireless communication systems.In this paper,we study the problem of dynamic caching,computation offloading,and resource allocation in cache-assisted multi-user MEC systems with stochastic task arrivals.There are multiple computationally intensive tasks in the system,and each Mobile User(MU)needs to execute a task either locally or remotely in one or more MEC servers by offloading the task data.Popular tasks can be cached in MEC servers to avoid duplicates in offloading.The cached contents can be either obtained through user offloading,fetched from a remote cloud,or fetched from another MEC server.The objective is to minimize the long-term average of a cost function,which is defined as a weighted sum of energy consumption,delay,and cache contents’fetching costs.The weighting coefficients associated with the different metrics in the objective function can be adjusted to balance the tradeoff among them.The optimum design is performed with respect to four decision parameters:whether to cache a given task,whether to offload a given uncached task,how much transmission power should be used during offloading,and how much MEC resources to be allocated for executing a task.We propose to solve the problems by developing a dynamic scheduling policy based on Deep Reinforcement Learning(DRL)with the Deep Deterministic Policy Gradient(DDPG)method.A new decentralized DDPG algorithm is developed to obtain the optimum designs for multi-cell MEC systems by leveraging on the cooperations among neighboring MEC servers.Simulation results demonstrate that the proposed algorithm outperforms other existing strategies,such as Deep Q-Network(DQN).展开更多
This study conducts an in-depth analysis of the current state,influencing factors,and strategies for the distribution of educational resources between urban and rural areas in China.It aims to provide an empirical fou...This study conducts an in-depth analysis of the current state,influencing factors,and strategies for the distribution of educational resources between urban and rural areas in China.It aims to provide an empirical foundation and specific recommendations for policymakers and educational practitioners.The research reveals significant disparities in teacher allocation,teaching facilities,and educational funding between urban and rural regions,which adversely impact the quality and equity of education.Despite national policies promoting educational equity,discrepancies in local implementation hinder the effectiveness of these policies.The study underscores the profound influence of education on social development,noting that educational gaps can restrict individual social mobility and affect overall societal progress and economic growth.Policy recommendations include increasing investment in rural education,establishing dynamic resource allocation mechanisms,enhancing rural teacher remuneration and social status,and promoting the application of educational technology.The study acknowledges limitations in sample selection and data collection,suggesting future research directions such as the impact of educational policies on specific groups and the psychosocial effects of educational inequality.Interdisciplinary research and systematic policy evaluation and feedback mechanisms are encouraged to ensure policies adapt to new educational demands.展开更多
文摘Mobile Edge Computing(MEC)is one of the most promising techniques for next-generation wireless communication systems.In this paper,we study the problem of dynamic caching,computation offloading,and resource allocation in cache-assisted multi-user MEC systems with stochastic task arrivals.There are multiple computationally intensive tasks in the system,and each Mobile User(MU)needs to execute a task either locally or remotely in one or more MEC servers by offloading the task data.Popular tasks can be cached in MEC servers to avoid duplicates in offloading.The cached contents can be either obtained through user offloading,fetched from a remote cloud,or fetched from another MEC server.The objective is to minimize the long-term average of a cost function,which is defined as a weighted sum of energy consumption,delay,and cache contents’fetching costs.The weighting coefficients associated with the different metrics in the objective function can be adjusted to balance the tradeoff among them.The optimum design is performed with respect to four decision parameters:whether to cache a given task,whether to offload a given uncached task,how much transmission power should be used during offloading,and how much MEC resources to be allocated for executing a task.We propose to solve the problems by developing a dynamic scheduling policy based on Deep Reinforcement Learning(DRL)with the Deep Deterministic Policy Gradient(DDPG)method.A new decentralized DDPG algorithm is developed to obtain the optimum designs for multi-cell MEC systems by leveraging on the cooperations among neighboring MEC servers.Simulation results demonstrate that the proposed algorithm outperforms other existing strategies,such as Deep Q-Network(DQN).
文摘This study conducts an in-depth analysis of the current state,influencing factors,and strategies for the distribution of educational resources between urban and rural areas in China.It aims to provide an empirical foundation and specific recommendations for policymakers and educational practitioners.The research reveals significant disparities in teacher allocation,teaching facilities,and educational funding between urban and rural regions,which adversely impact the quality and equity of education.Despite national policies promoting educational equity,discrepancies in local implementation hinder the effectiveness of these policies.The study underscores the profound influence of education on social development,noting that educational gaps can restrict individual social mobility and affect overall societal progress and economic growth.Policy recommendations include increasing investment in rural education,establishing dynamic resource allocation mechanisms,enhancing rural teacher remuneration and social status,and promoting the application of educational technology.The study acknowledges limitations in sample selection and data collection,suggesting future research directions such as the impact of educational policies on specific groups and the psychosocial effects of educational inequality.Interdisciplinary research and systematic policy evaluation and feedback mechanisms are encouraged to ensure policies adapt to new educational demands.
