With the rapid development of 5G technology,the proportion of video traffic on the Internet is increasing,bringing pressure on the network infrastructure.Edge computing technology provides a feasible solution for opti...With the rapid development of 5G technology,the proportion of video traffic on the Internet is increasing,bringing pressure on the network infrastructure.Edge computing technology provides a feasible solution for optimizing video content distribution.However,the limited edge node cache capacity and dynamic user requests make edge caching more complex.Therefore,we propose a recommendation-driven edge Caching network architecture for the Full life cycle of video streaming(FlyCache)designed to improve users’Quality of Experience(QoE)and reduce backhaul traffic consumption.FlyCache implements intelligent caching management across three key stages:before-playback,during-playback,and after-playback.Specifically,we introduce a cache placement policy for the before-playback stage,a dynamic prefetching and cache admission policy for the during-playback stage,and a progressive cache eviction policy for the after-playback stage.To validate the effectiveness of FlyCache,we developed a user behavior-driven edge caching simulation framework incorporating recommendation mechanisms.Experiments conducted on the MovieLens and synthetic datasets demonstrate that FlyCache outperforms other caching strategies in terms of byte hit rate,backhaul traffic,and delayed startup rate.展开更多
Two-dimensional double-layer honeycomb(DLHC)materials are known for their diverse physical properties,but superconductivity has been a notably absent characteristic in this structure.We address this gap by investigati...Two-dimensional double-layer honeycomb(DLHC)materials are known for their diverse physical properties,but superconductivity has been a notably absent characteristic in this structure.We address this gap by investigating M_(2)N_(2)(M=Nb,Ta)with DLHC structure using first-principles calculations.Our results show that M_(2)N_(2)are stable and metallic,exhibiting superconducting behavior.Specifically,Nb_(2)N_(2)and Ta_(2)N_(2)display superconducting transition temperatures of 6.8 K and 8.8 K,respectively.Their electron-phonon coupling is predominantly driven by the coupling between metal d-orbitals and low-frequency metal-dominated vibration modes.Interestingly,two compounds also exhibit non-trivial band topology.Thus,M_(2)N_(2)are promising platforms for studying the interplay between topology and superconductivity and fill the gap in superconductivity research for DLHC materials.展开更多
In the Satellite-integrated Internet of Things(S-IoT),data freshness in the time-sensitive scenarios could not be guaranteed over the timevarying topology with current distribution strategies aiming to reduce the tran...In the Satellite-integrated Internet of Things(S-IoT),data freshness in the time-sensitive scenarios could not be guaranteed over the timevarying topology with current distribution strategies aiming to reduce the transmission delay.To address this problem,in this paper,we propose an age-optimal caching distribution mechanism for the high-timeliness data collection in S-IoT by adopting a freshness metric,as called age of information(AoI)through the caching-based single-source multidestinations(SSMDs)transmission,namely Multi-AoI,with a well-designed cross-slot directed graph(CSG).With the proposed CSG,we make optimizations on the locations of cache nodes by solving a nonlinear integer programming problem on minimizing Multi-AoI.In particular,we put up forward three specific algorithms respectively for improving the Multi-AoI,i.e.,the minimum queuing delay algorithm(MQDA)based on node deviation from average level,the minimum propagation delay algorithm(MPDA)based on the node propagation delay reduction,and a delay balanced algorithm(DBA)based on node deviation from average level and propagation delay reduction.The simulation results show that the proposed mechanism can effectively improve the freshness of information compared with the random selection algorithm.展开更多
基金supported by the National Natural Science Foundation of China(NSFC)[Grant No.62072469].
文摘With the rapid development of 5G technology,the proportion of video traffic on the Internet is increasing,bringing pressure on the network infrastructure.Edge computing technology provides a feasible solution for optimizing video content distribution.However,the limited edge node cache capacity and dynamic user requests make edge caching more complex.Therefore,we propose a recommendation-driven edge Caching network architecture for the Full life cycle of video streaming(FlyCache)designed to improve users’Quality of Experience(QoE)and reduce backhaul traffic consumption.FlyCache implements intelligent caching management across three key stages:before-playback,during-playback,and after-playback.Specifically,we introduce a cache placement policy for the before-playback stage,a dynamic prefetching and cache admission policy for the during-playback stage,and a progressive cache eviction policy for the after-playback stage.To validate the effectiveness of FlyCache,we developed a user behavior-driven edge caching simulation framework incorporating recommendation mechanisms.Experiments conducted on the MovieLens and synthetic datasets demonstrate that FlyCache outperforms other caching strategies in terms of byte hit rate,backhaul traffic,and delayed startup rate.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.12074213 and 11574108)the National Key R&D Program of China(Grant No.2022YFA1403103)+2 种基金the Major Basic Program of Natural Science Foundation of Shandong Province(Grant No.ZR2021ZD01)the Natural Science Foundation of Shandong Province(Grant No.ZR2023MA082)the Project of Introduction and Cultivation for Young Innovative Talents in Colleges and Universities of Shandong Province。
文摘Two-dimensional double-layer honeycomb(DLHC)materials are known for their diverse physical properties,but superconductivity has been a notably absent characteristic in this structure.We address this gap by investigating M_(2)N_(2)(M=Nb,Ta)with DLHC structure using first-principles calculations.Our results show that M_(2)N_(2)are stable and metallic,exhibiting superconducting behavior.Specifically,Nb_(2)N_(2)and Ta_(2)N_(2)display superconducting transition temperatures of 6.8 K and 8.8 K,respectively.Their electron-phonon coupling is predominantly driven by the coupling between metal d-orbitals and low-frequency metal-dominated vibration modes.Interestingly,two compounds also exhibit non-trivial band topology.Thus,M_(2)N_(2)are promising platforms for studying the interplay between topology and superconductivity and fill the gap in superconductivity research for DLHC materials.
基金supports from the Major Key Project of PCL (PCL2021A031)Shenzhen Science Technology Program (GXWD20201230155427003-20200824093323001)
文摘In the Satellite-integrated Internet of Things(S-IoT),data freshness in the time-sensitive scenarios could not be guaranteed over the timevarying topology with current distribution strategies aiming to reduce the transmission delay.To address this problem,in this paper,we propose an age-optimal caching distribution mechanism for the high-timeliness data collection in S-IoT by adopting a freshness metric,as called age of information(AoI)through the caching-based single-source multidestinations(SSMDs)transmission,namely Multi-AoI,with a well-designed cross-slot directed graph(CSG).With the proposed CSG,we make optimizations on the locations of cache nodes by solving a nonlinear integer programming problem on minimizing Multi-AoI.In particular,we put up forward three specific algorithms respectively for improving the Multi-AoI,i.e.,the minimum queuing delay algorithm(MQDA)based on node deviation from average level,the minimum propagation delay algorithm(MPDA)based on the node propagation delay reduction,and a delay balanced algorithm(DBA)based on node deviation from average level and propagation delay reduction.The simulation results show that the proposed mechanism can effectively improve the freshness of information compared with the random selection algorithm.
