Network technology is the basis for large-scale high-efficiency network computing, such as supercomputing, cloud computing, big data processing, and artificial intelligence computing. The network technologies of netwo...Network technology is the basis for large-scale high-efficiency network computing, such as supercomputing, cloud computing, big data processing, and artificial intelligence computing. The network technologies of network computing systems in different fields not only learn from each other but also have targeted design and optimization. Considering it comprehensively,three development trends, i.e., integration, differentiation, and optimization, are summarized in this paper for network technologies in different fields. Integration reflects that there are no clear boundaries for network technologies in different fields, differentiation reflects that there are some unique solutions in different application fields or innovative solutions under new application requirements,and optimization reflects that there are some optimizations for specific scenarios. This paper can help academic researchers consider what should be done in the future and industry personnel consider how to build efficient practical network systems.展开更多
Multi-access edge computing(MEC)presents computing services at the edge of networks to address the enormous processing requirements of intelligent applications.Due to the maneuverability of unmanned aerial vehicles(UA...Multi-access edge computing(MEC)presents computing services at the edge of networks to address the enormous processing requirements of intelligent applications.Due to the maneuverability of unmanned aerial vehicles(UAVs),they can be used as temporal aerial edge nodes for providing edge services to ground users in MEC.However,MEC environment is usually dynamic and complicated.It is a challenge for multiple UAVs to select appropriate service strategies.Besides,most of existing works study UAV-MEC with the assumption that the flight heights of UAVs are fixed;i.e.,the flying is considered to occur with reference to a two-dimensional plane,which neglects the importance of the height.In this paper,with consideration of the co-channel interference,an optimization problem of energy efficiency is investigated to maximize the number of fulfilled tasks,where multiple UAVs in a threedimensional space collaboratively fulfill the task computation of ground users.In the formulated problem,we try to obtain the optimal flight and sub-channel selection strategies for UAVs and schedule strategies for tasks.Based on the multi-agent deep deterministic policy gradient(MADDPG)algorithm,we propose a curiosity-driven and twin-networks-structured MADDPG(CTMADDPG)algorithm to solve the formulated problem.It uses the inner reward to facilitate the state exploration of agents,avoiding convergence at the sub-optimal strategy.Furthermore,we adopt the twin critic networks for update stabilization to reduce the probability of Q value overestimation.The simulation results show that CTMADDPG is outstanding in maximizing the energy efficiency of the whole system and outperforms the other benchmarks.展开更多
基金Project supported by the National Natural Science Foundation of China (Nos. 61972412, 62202486, and 12102468)。
文摘Network technology is the basis for large-scale high-efficiency network computing, such as supercomputing, cloud computing, big data processing, and artificial intelligence computing. The network technologies of network computing systems in different fields not only learn from each other but also have targeted design and optimization. Considering it comprehensively,three development trends, i.e., integration, differentiation, and optimization, are summarized in this paper for network technologies in different fields. Integration reflects that there are no clear boundaries for network technologies in different fields, differentiation reflects that there are some unique solutions in different application fields or innovative solutions under new application requirements,and optimization reflects that there are some optimizations for specific scenarios. This paper can help academic researchers consider what should be done in the future and industry personnel consider how to build efficient practical network systems.
基金Project supported by the National Natural Science Foundation of China(Nos.62202486 and U22B2005)。
文摘Multi-access edge computing(MEC)presents computing services at the edge of networks to address the enormous processing requirements of intelligent applications.Due to the maneuverability of unmanned aerial vehicles(UAVs),they can be used as temporal aerial edge nodes for providing edge services to ground users in MEC.However,MEC environment is usually dynamic and complicated.It is a challenge for multiple UAVs to select appropriate service strategies.Besides,most of existing works study UAV-MEC with the assumption that the flight heights of UAVs are fixed;i.e.,the flying is considered to occur with reference to a two-dimensional plane,which neglects the importance of the height.In this paper,with consideration of the co-channel interference,an optimization problem of energy efficiency is investigated to maximize the number of fulfilled tasks,where multiple UAVs in a threedimensional space collaboratively fulfill the task computation of ground users.In the formulated problem,we try to obtain the optimal flight and sub-channel selection strategies for UAVs and schedule strategies for tasks.Based on the multi-agent deep deterministic policy gradient(MADDPG)algorithm,we propose a curiosity-driven and twin-networks-structured MADDPG(CTMADDPG)algorithm to solve the formulated problem.It uses the inner reward to facilitate the state exploration of agents,avoiding convergence at the sub-optimal strategy.Furthermore,we adopt the twin critic networks for update stabilization to reduce the probability of Q value overestimation.The simulation results show that CTMADDPG is outstanding in maximizing the energy efficiency of the whole system and outperforms the other benchmarks.
