In this paper,we explore a distributed collaborative caching and computing model to support the distribution of adaptive bit rate video streaming.The aim is to reduce the average initial buffer delay and improve the q...In this paper,we explore a distributed collaborative caching and computing model to support the distribution of adaptive bit rate video streaming.The aim is to reduce the average initial buffer delay and improve the quality of user experience.Considering the difference between global and local video popularities and the time-varying characteristics of video popularity,a two-stage caching scheme is proposed to push popular videos closer to users and minimize the average initial buffer delay.Based on both long-term content popularity and short-term content popularity,the proposed caching solution is decouple into the proactive cache stage and the cache update stage.In the proactive cache stage,we develop a proactive cache placement algorithm that can be executed in an off-peak period.In the cache update stage,we propose a reactive cache update algorithm to update the existing cache policy to minimize the buffer delay.Simulation results verify that the proposed caching algorithms can reduce the initial buffer delay efficiently.展开更多
The efficient integration of satellite and terrestrial networks has become an important component for 6 G wireless architectures to provide highly reliable and secure connectivity over a wide geographical area.As the ...The efficient integration of satellite and terrestrial networks has become an important component for 6 G wireless architectures to provide highly reliable and secure connectivity over a wide geographical area.As the satellite and cellular networks are developed separately these years,the integrated network should synergize the communication,storage,computation capabilities of both sides towards an intelligent system more than mere consideration of coexistence.This has motivated us to develop double-edge intelligent integrated satellite and terrestrial networks(DILIGENT).Leveraging the boost development of multi-access edge computing(MEC)technology and artificial intelligence(AI),the framework is entitled with the systematic learning and adaptive network management of satellite and cellular networks.In this article,we provide a brief review of the state-of-art contributions from the perspective of academic research and standardization.Then we present the overall design of the proposed DILIGENT architecture,where the advantages are discussed and summarized.Strategies of task offloading,content caching and distribution are presented.Numerical results show that the proposed network architecture outperforms the existing integrated networks.展开更多
With the rapid development of mobile devices and deep learning,mobile smart applications using deep learning technology have sprung up.It satisfies multiple needs of users,network operators and service providers,and r...With the rapid development of mobile devices and deep learning,mobile smart applications using deep learning technology have sprung up.It satisfies multiple needs of users,network operators and service providers,and rapidly becomes a main research focus.In recent years,deep learning has achieved tremendous success in image processing,natural language processing,language analysis and other research fields.Despite the task performance has been greatly improved,the resources required to run these models have increased significantly.This poses a major challenge for deploying such applications on resource-restricted mobile devices.Mobile intelligence needs faster mobile processors,more storage space,smaller but more accurate models,and even the assistance of other network nodes.To help the readers establish a global concept of the entire research direction concisely,we classify the latest works in this field into two categories,which are local optimization on mobile devices and distributed optimization based on the computational position of machine learning tasks.We also list a few typical scenarios to make readers realize the importance and indispensability of mobile deep learning applications.Finally,we conjecture what the future may hold for deploying deep learning applications on mobile devices research,which may help to stimulate new ideas.展开更多
基金the National Natural Science Foundation of China under grants 61901078,61871062,and U20A20157in part by the China University Industry-University-Research Collaborative Innovation Fund(Future Network Innovation Research and Application Project)under grant 2021FNA04008+5 种基金in part by the China Postdoctoral Science Foundation under grant 2022MD713692in part by the Chongqing Postdoctoral Science Special Foundation under grant 2021XM2018in part by the Natural Science Foundation of Chongqing under grant cstc2020jcyj-zdxmX0024in part by University Innovation Research Group of Chongqing under grant CXQT20017in part by the Science and Technology Research Program of Chongqing Municipal Education Commission under Grant KJQN202000626in part by the Youth Innovation Group Support Program of ICE Discipline of CQUPT under grant SCIE-QN-2022-04.
文摘In this paper,we explore a distributed collaborative caching and computing model to support the distribution of adaptive bit rate video streaming.The aim is to reduce the average initial buffer delay and improve the quality of user experience.Considering the difference between global and local video popularities and the time-varying characteristics of video popularity,a two-stage caching scheme is proposed to push popular videos closer to users and minimize the average initial buffer delay.Based on both long-term content popularity and short-term content popularity,the proposed caching solution is decouple into the proactive cache stage and the cache update stage.In the proactive cache stage,we develop a proactive cache placement algorithm that can be executed in an off-peak period.In the cache update stage,we propose a reactive cache update algorithm to update the existing cache policy to minimize the buffer delay.Simulation results verify that the proposed caching algorithms can reduce the initial buffer delay efficiently.
基金supportedin part by the National Science Foundation of China(NSFC)under Grant 61631005,Grant 61771065,Grant 61901048in part by the Zhijiang Laboratory Open Project Fund 2020LCOAB01in part by the Beijing Municipal Science and Technology Commission Research under Project Z181100003218015。
文摘The efficient integration of satellite and terrestrial networks has become an important component for 6 G wireless architectures to provide highly reliable and secure connectivity over a wide geographical area.As the satellite and cellular networks are developed separately these years,the integrated network should synergize the communication,storage,computation capabilities of both sides towards an intelligent system more than mere consideration of coexistence.This has motivated us to develop double-edge intelligent integrated satellite and terrestrial networks(DILIGENT).Leveraging the boost development of multi-access edge computing(MEC)technology and artificial intelligence(AI),the framework is entitled with the systematic learning and adaptive network management of satellite and cellular networks.In this article,we provide a brief review of the state-of-art contributions from the perspective of academic research and standardization.Then we present the overall design of the proposed DILIGENT architecture,where the advantages are discussed and summarized.Strategies of task offloading,content caching and distribution are presented.Numerical results show that the proposed network architecture outperforms the existing integrated networks.
基金supported by the National Key Research and Development Program of China with grant number 2020AAA0108800the National Science Foundation of China under Grant Nos.61772414,61532015,61532004,61721002,61472317,and 61502379+1 种基金the MOE Innovation Research Team No.IRT 17R86the Project of China Knowledge Centre for Engineering Science and Technology.
文摘With the rapid development of mobile devices and deep learning,mobile smart applications using deep learning technology have sprung up.It satisfies multiple needs of users,network operators and service providers,and rapidly becomes a main research focus.In recent years,deep learning has achieved tremendous success in image processing,natural language processing,language analysis and other research fields.Despite the task performance has been greatly improved,the resources required to run these models have increased significantly.This poses a major challenge for deploying such applications on resource-restricted mobile devices.Mobile intelligence needs faster mobile processors,more storage space,smaller but more accurate models,and even the assistance of other network nodes.To help the readers establish a global concept of the entire research direction concisely,we classify the latest works in this field into two categories,which are local optimization on mobile devices and distributed optimization based on the computational position of machine learning tasks.We also list a few typical scenarios to make readers realize the importance and indispensability of mobile deep learning applications.Finally,we conjecture what the future may hold for deploying deep learning applications on mobile devices research,which may help to stimulate new ideas.