Recent advancements in satellite technologies and the declining cost of access to space have led to the emergence of large satellite constellations in Low Earth Orbit(LEO).However,these constellations often rely on be...Recent advancements in satellite technologies and the declining cost of access to space have led to the emergence of large satellite constellations in Low Earth Orbit(LEO).However,these constellations often rely on bent-pipe architecture,resulting in high communication costs.Existing onboard inference architectures suffer from limitations in terms of low accuracy and inflexibility in the deployment and management of in-orbit applications.To address these challenges,we propose a cloud-native-based satellite design specifically tailored for Earth Observation tasks,enabling diverse computing paradigms.In this work,we present a case study of a satellite-ground collaborative inference system deployed in the Tiansuan constellation,demonstrating a remarkable 50%accuracy improvement and a substantial 90%data reduction.Our work sheds light on in-orbit energy,where in-orbit computing accounts for 17%of the total onboard energy consumption.Our approach represents a significant advancement of cloud-native satellite,aiming to enhance the accuracy of in-orbit computing while simultaneously reducing communication cost.展开更多
The Internet of Things(IoTs)has become an essential component of the 5th Generation(5G)network and beyond,accelerating the transition to digital society.The increasing signaling traffic generated by billions of IoT de...The Internet of Things(IoTs)has become an essential component of the 5th Generation(5G)network and beyond,accelerating the transition to digital society.The increasing signaling traffic generated by billions of IoT devices has placed significant strain on the 5G Core network(5GC)control plane.To address this issue,the 3rd Gener-ation Partnership Project(3GPP)first proposed a Service-Based Architecture(SBA),intending to create a flexible,scalable,and agile cloud-native 5GC.However,considering the coupling of protocol states and functions,there are still many challenges to fully utilize the benefits of the cloud computing and orchestrate the 5GC in a cloud-native manner.We propose a Message-Level StateLess Design(ML-SLD)to provide a cloud-native 5GC from an architectural standpoint in this paper.Firstly,we propose an innovative mechanism for servitization of the N2 interface to maintain the connection between Radio Access Network(RAN)and the 5GC,avoiding interruptions and dropouts of large-scale user data.Furthermore,we propose an On-demand Message Forwarding(OMF)al-gorithm to reduce the impact of cloud fluctuations on the performance of cloud-native 5GC.Finally,we create a prototype that is based on the OpenAirInterface(OAI)5G core network projects,with all Network Functions(NFs)packaged in dockers and deployed in a kubernetes-based cloud environment.Several experiments have been built with UERANSIM and Chaosblade simulation tools.The findings demonstrate the viability and efficiency of our proposed methods.展开更多
MapReduce is one of the most classic and powerful parallel computing models in the field of big data.It is still active in the big data system ecosystem and is currently evolving towards cloud-native environment.Among...MapReduce is one of the most classic and powerful parallel computing models in the field of big data.It is still active in the big data system ecosystem and is currently evolving towards cloud-native environment.Among them,due to its elasticity and ease-to-use,Serverless computing is one of the most promising directions of cloud-native technology.To support MapReduce big data computing capabilities in a Serverless environment can give full play to Serverless’s advantages.However,due to different underlying system architecture,three issues will be encountered when running MapReduce jobs in the Serverless environment.Firstly,the scheduling strategy is difficult to fully utilize the available resources.Secondly,reading Shuffle index data on cloud storage is inefficient and expensive.Thirdly,cloud storage Input/Output(I/O)request latency has a long tail effect.To solve these problems,this paper proposes three strategies with a MapReduce parallel processing framework in Serverless environment.Experimental results show that compared with cutting-edge systems,our approach shortens job execution time by 25.6%on average and reduces job execution costs by 17.3%.展开更多
Leader election algorithms play an important role in orchestrating different processes on distributed systems, including next-generation transportation systems. This leader election phase is usually triggered after th...Leader election algorithms play an important role in orchestrating different processes on distributed systems, including next-generation transportation systems. This leader election phase is usually triggered after the leader has failed and has a high overhead in performance and state recovery. Further, these algorithms are not generally applicable to cloud-based native microservices-based applications where the resources available to the group and resources participating in a group continuously change and the current leader <span style="font-family:Verdana;">may exit the system with prior knowledge of the exit. Our proposed algo</span><span style="font-family:Verdana;">rithm, t</span><span style="font-family:Verdana;">he dynamic leader selection algorithm, provides several benefits through</span><span style="font-family:Verdana;"> selection (not, election) of a set of future leaders which are then alerted prior to </span><span style="font-family:Verdana;">the failure of the current leadership and handed over the leadership. A </span><span style="font-family:Verdana;">specific </span><span style="font-family:Verdana;">illustration of this algorithm is provided with reference to a peer-to-peer</span><span style="font-family:Verdana;"> distribution of autonomous cars in a 5G architecture for transportation networks. The proposed algorithm increases the efficiencies of applications that use the leader election algorithm and finds broad applicability in microservices-based applications.</span>展开更多
基金supported by National Natural Science Foundation of China(62032003).
文摘Recent advancements in satellite technologies and the declining cost of access to space have led to the emergence of large satellite constellations in Low Earth Orbit(LEO).However,these constellations often rely on bent-pipe architecture,resulting in high communication costs.Existing onboard inference architectures suffer from limitations in terms of low accuracy and inflexibility in the deployment and management of in-orbit applications.To address these challenges,we propose a cloud-native-based satellite design specifically tailored for Earth Observation tasks,enabling diverse computing paradigms.In this work,we present a case study of a satellite-ground collaborative inference system deployed in the Tiansuan constellation,demonstrating a remarkable 50%accuracy improvement and a substantial 90%data reduction.Our work sheds light on in-orbit energy,where in-orbit computing accounts for 17%of the total onboard energy consumption.Our approach represents a significant advancement of cloud-native satellite,aiming to enhance the accuracy of in-orbit computing while simultaneously reducing communication cost.
基金funded by the National Key Research and Development Program of China under Grant 2019YFB1803301Beijing Natural Science Foundation(L202002).
文摘The Internet of Things(IoTs)has become an essential component of the 5th Generation(5G)network and beyond,accelerating the transition to digital society.The increasing signaling traffic generated by billions of IoT devices has placed significant strain on the 5G Core network(5GC)control plane.To address this issue,the 3rd Gener-ation Partnership Project(3GPP)first proposed a Service-Based Architecture(SBA),intending to create a flexible,scalable,and agile cloud-native 5GC.However,considering the coupling of protocol states and functions,there are still many challenges to fully utilize the benefits of the cloud computing and orchestrate the 5GC in a cloud-native manner.We propose a Message-Level StateLess Design(ML-SLD)to provide a cloud-native 5GC from an architectural standpoint in this paper.Firstly,we propose an innovative mechanism for servitization of the N2 interface to maintain the connection between Radio Access Network(RAN)and the 5GC,avoiding interruptions and dropouts of large-scale user data.Furthermore,we propose an On-demand Message Forwarding(OMF)al-gorithm to reduce the impact of cloud fluctuations on the performance of cloud-native 5GC.Finally,we create a prototype that is based on the OpenAirInterface(OAI)5G core network projects,with all Network Functions(NFs)packaged in dockers and deployed in a kubernetes-based cloud environment.Several experiments have been built with UERANSIM and Chaosblade simulation tools.The findings demonstrate the viability and efficiency of our proposed methods.
基金supported in part by the National Natural Science Foundation of China(Nos.62072230 and U22A2031)Jiangsu Province Science and Technology Key Program(No.BE2021729)Collaborative Innovation Center of Novel Software Technology and Industrialization.
文摘MapReduce is one of the most classic and powerful parallel computing models in the field of big data.It is still active in the big data system ecosystem and is currently evolving towards cloud-native environment.Among them,due to its elasticity and ease-to-use,Serverless computing is one of the most promising directions of cloud-native technology.To support MapReduce big data computing capabilities in a Serverless environment can give full play to Serverless’s advantages.However,due to different underlying system architecture,three issues will be encountered when running MapReduce jobs in the Serverless environment.Firstly,the scheduling strategy is difficult to fully utilize the available resources.Secondly,reading Shuffle index data on cloud storage is inefficient and expensive.Thirdly,cloud storage Input/Output(I/O)request latency has a long tail effect.To solve these problems,this paper proposes three strategies with a MapReduce parallel processing framework in Serverless environment.Experimental results show that compared with cutting-edge systems,our approach shortens job execution time by 25.6%on average and reduces job execution costs by 17.3%.
文摘Leader election algorithms play an important role in orchestrating different processes on distributed systems, including next-generation transportation systems. This leader election phase is usually triggered after the leader has failed and has a high overhead in performance and state recovery. Further, these algorithms are not generally applicable to cloud-based native microservices-based applications where the resources available to the group and resources participating in a group continuously change and the current leader <span style="font-family:Verdana;">may exit the system with prior knowledge of the exit. Our proposed algo</span><span style="font-family:Verdana;">rithm, t</span><span style="font-family:Verdana;">he dynamic leader selection algorithm, provides several benefits through</span><span style="font-family:Verdana;"> selection (not, election) of a set of future leaders which are then alerted prior to </span><span style="font-family:Verdana;">the failure of the current leadership and handed over the leadership. A </span><span style="font-family:Verdana;">specific </span><span style="font-family:Verdana;">illustration of this algorithm is provided with reference to a peer-to-peer</span><span style="font-family:Verdana;"> distribution of autonomous cars in a 5G architecture for transportation networks. The proposed algorithm increases the efficiencies of applications that use the leader election algorithm and finds broad applicability in microservices-based applications.</span>
