Distributed computing is an important topic in the field of wireless communications and networking,and its high efficiency in handling large amounts of data is particularly noteworthy.Although distributed computing be...Distributed computing is an important topic in the field of wireless communications and networking,and its high efficiency in handling large amounts of data is particularly noteworthy.Although distributed computing benefits from its ability of processing data in parallel,the communication burden between different servers is incurred,thereby the computation process is detained.Recent researches have applied coding in distributed computing to reduce the communication burden,where repetitive computation is utilized to enable multicast opportunities so that the same coded information can be reused across different servers.To handle the computation tasks in practical heterogeneous systems,we propose a novel coding scheme to effectively mitigate the "straggling effect" in distributed computing.We assume that there are two types of servers in the system and the only difference between them is their computational capabilities,the servers with lower computational capabilities are called stragglers.Given any ratio of fast servers to slow servers and any gap of computational capabilities between them,we achieve approximately the same computation time for both fast and slow servers by assigning different amounts of computation tasks to them,thus reducing the overall computation time.Furthermore,we investigate the informationtheoretic lower bound of the inter-communication load and show that the lower bound is within a constant multiplicative gap to the upper bound achieved by our scheme.Various simulations also validate the effectiveness of the proposed scheme.展开更多
Federated learning often experiences slow and unstable convergence due to edge-side data heterogeneity.This problem becomes more severe when edge participation rate is low,as the information collected from different e...Federated learning often experiences slow and unstable convergence due to edge-side data heterogeneity.This problem becomes more severe when edge participation rate is low,as the information collected from different edge devices varies significantly.As a result,communication overhead increases,which further slows down the convergence process.To address this challenge,we propose a simple yet effective federated learning framework that improves consistency among edge devices.The core idea is clusters the lookahead gradients collected from edge devices on the cloud server to obtain personalized momentum for steering local updates.In parallel,a global momentum is applied during model aggregation,enabling faster convergence while preserving personalization.This strategy enables efficient propagation of the estimated global update direction to all participating edge devices and maintains alignment in local training,without introducing extra memory or communication overhead.We conduct extensive experiments on benchmark datasets such as Cifar100 and Tiny-ImageNet.The results confirm the effectiveness of our framework.On CIFAR-100,our method reaches 55%accuracy with 37 fewer rounds and achieves a competitive final accuracy of 65.46%.Even under extreme non-IID scenarios,it delivers significant improvements in both accuracy and communication efficiency.The implementation is publicly available at https://github.com/sjmp525/CollaborativeComputing/tree/FedCCM(accessed on 20 October 2025).展开更多
The shortage of computation methods and storage devices has largely limited the development of multiobjective optimization in industrial processes.To improve the operational levels of the process industries,we propose...The shortage of computation methods and storage devices has largely limited the development of multiobjective optimization in industrial processes.To improve the operational levels of the process industries,we propose a multi-objective optimization framework based on cloud services and a cloud distribution system.Real-time data from manufacturing procedures are first temporarily stored in a local database,and then transferred to the relational database in the cloud.Next,a distribution system with elastic compute power is set up for the optimization framework.Finally,a multi-objective optimization model based on deep learning and an evolutionary algorithm is proposed to optimize several conflicting goals of the blast furnace ironmaking process.With the application of this optimization service in a cloud factory,iron production was found to increase by 83.91 t∙d^(-1),the coke ratio decreased 13.50 kg∙t^(-1),and the silicon content decreased by an average of 0.047%.展开更多
In LEO(Low Earth Orbit)satellite communication systems,the satellite network is made up of a large number of satellites,the dynamically changing network environment affects the results of distributed computing.In orde...In LEO(Low Earth Orbit)satellite communication systems,the satellite network is made up of a large number of satellites,the dynamically changing network environment affects the results of distributed computing.In order to improve the fault tolerance rate,a novel public blockchain consensus mechanism that applies a distributed computing architecture in a public network is proposed.Redundant calculation of blockchain ensures the credibility of the results;and the transactions with calculation results of a task are stored distributed in sequence in Directed Acyclic Graphs(DAG).The transactions issued by nodes are connected to form a net.The net can quickly provide node reputation evaluation that does not rely on third parties.Simulations show that our proposed blockchain has the following advantages:1.The task processing speed of the blockchain can be close to that of the fastest node in the entire blockchain;2.When the tasks’arrival time intervals and demanded working nodes(WNs)meet certain conditions,the network can tolerate more than 50%of malicious devices;3.No matter the number of nodes in the blockchain is increased or reduced,the network can keep robustness by adjusting the task’s arrival time interval and demanded WNs.展开更多
To security support large-scale intelligent applications,distributed machine learning based on blockchain is an intuitive solution scheme.However,the distributed machine learning is difficult to train due to that the ...To security support large-scale intelligent applications,distributed machine learning based on blockchain is an intuitive solution scheme.However,the distributed machine learning is difficult to train due to that the corresponding optimization solver algorithms converge slowly,which highly demand on computing and memory resources.To overcome the challenges,we propose a distributed computing framework for L-BFGS optimization algorithm based on variance reduction method,which is a lightweight,few additional cost and parallelized scheme for the model training process.To validate the claims,we have conducted several experiments on multiple classical datasets.Results show that our proposed computing framework can steadily accelerate the training process of solver in either local mode or distributed mode.展开更多
A dynamic multi-beam resource allocation algorithm for large low Earth orbit(LEO)constellation based on on-board distributed computing is proposed in this paper.The allocation is a combinatorial optimization process u...A dynamic multi-beam resource allocation algorithm for large low Earth orbit(LEO)constellation based on on-board distributed computing is proposed in this paper.The allocation is a combinatorial optimization process under a series of complex constraints,which is important for enhancing the matching between resources and requirements.A complex algorithm is not available because that the LEO on-board resources is limi-ted.The proposed genetic algorithm(GA)based on two-dimen-sional individual model and uncorrelated single paternal inheri-tance method is designed to support distributed computation to enhance the feasibility of on-board application.A distributed system composed of eight embedded devices is built to verify the algorithm.A typical scenario is built in the system to evalu-ate the resource allocation process,algorithm mathematical model,trigger strategy,and distributed computation architec-ture.According to the simulation and measurement results,the proposed algorithm can provide an allocation result for more than 1500 tasks in 14 s and the success rate is more than 91%in a typical scene.The response time is decreased by 40%com-pared with the conditional GA.展开更多
Recently, wireless distributed computing （WDC） concept has emerged promising manifolds improvements to current wireless technotogies. Despite the various expected benefits of this concept, significant drawbacks were...Recently, wireless distributed computing （WDC） concept has emerged promising manifolds improvements to current wireless technotogies. Despite the various expected benefits of this concept, significant drawbacks were addressed in the open literature. One of WDC key challenges is the impact of wireless channel quality on the load of distributed computations. Therefore, this research investigates the wireless channel impact on WDC performance when the tatter is applied to spectrum sensing in cognitive radio （CR） technology. However, a trade- off is found between accuracy and computational complexity in spectrum sensing approaches. Increasing these approaches accuracy is accompanied by an increase in computational complexity. This results in greater power consumption and processing time. A novel WDC scheme for cyclostationary feature detection spectrum sensing approach is proposed in this paper and thoroughly investigated. The benefits of the proposed scheme are firstly presented. Then, the impact of the wireless channel of the proposed scheme is addressed considering two scenarios. In the first scenario, workload matrices are distributed over the wireless channel展开更多
Distributed cryptographic computing system plays an important role since cryptographic computing is extremely computation sensitive. However, no general cryptographic computing system is available. Grid technology can...Distributed cryptographic computing system plays an important role since cryptographic computing is extremely computation sensitive. However, no general cryptographic computing system is available. Grid technology can give an efficient computational support for cryptographic applications. Therefore, a general-purpose grid-based distributed computing system called DCCS is put forward in this paper. The architecture of DCCS is simply described at first. The policy of task division adapted in DCCS is then presented. The method to manage subtask is further discussed in detail. Furthermore, the building and execution process of a computing job is revealed. Finally, the details of DCCS implementation under Globus Toolkit 4 are illustrated.展开更多
This paper discusses the model of how the Agent is applied to implement distributed computing of Ada95 and presents a dynamic allocation strategy for distributed computing that based on pre-allocationand Agent. The ...This paper discusses the model of how the Agent is applied to implement distributed computing of Ada95 and presents a dynamic allocation strategy for distributed computing that based on pre-allocationand Agent. The aim of this strategy is realizing dynamic equilibrium allocation.展开更多
In order to realize distributed computing of Ada95, this paper discusses Ada95's distributed system model and an implement model of Ada95's distributed computing-- workstation cluster model. Under this model,...In order to realize distributed computing of Ada95, this paper discusses Ada95's distributed system model and an implement model of Ada95's distributed computing-- workstation cluster model. Under this model, we presents a pre-allocation strategy for allocating the computation quantity of distributed units evenly among workstations and also reducing the communication expense between those distributed units.展开更多
As a parallel programming model, Map-Reduce is used for distributed computing of massive data. Map-Reduce model encapsulates the details of parallel implementation, fault-tolerant processing, local computing and load ...As a parallel programming model, Map-Reduce is used for distributed computing of massive data. Map-Reduce model encapsulates the details of parallel implementation, fault-tolerant processing, local computing and load balancing, etc., provides a simple but powerful interface. In case of having no clear idea about distributed and parallel programming, this interface can be utilized to save development time. This paper introduces the method of using Hadoop, the open-source Map-Reduce software platform, to combine PCs to carry out scalable parallel computing. Our experiment using 12 PCs to compute N-body problem based on Map-Reduce model shows that we can get a 9.8x speedup ratio. This work indicates that the Map-Reduce can be applied in scalable parallel computing.展开更多
Although the Internet of Things has been widely applied,the problems of cloud computing in the application of digital smart medical Big Data collection,processing,analysis,and storage remain,especially the low efficie...Although the Internet of Things has been widely applied,the problems of cloud computing in the application of digital smart medical Big Data collection,processing,analysis,and storage remain,especially the low efficiency of medical diagnosis.And with the wide application of the Internet of Things and Big Data in the medical field,medical Big Data is increasing in geometric magnitude resulting in cloud service overload,insufficient storage,communication delay,and network congestion.In order to solve these medical and network problems,a medical big-data-oriented fog computing architec-ture and BP algorithm application are proposed,and its structural advantages and characteristics are studied.This architecture enables the medical Big Data generated by medical edge devices and the existing data in the cloud service center to calculate,compare and analyze the fog node through the Internet of Things.The diagnosis results are designed to reduce the business processing delay and improve the diagnosis effect.Considering the weak computing of each edge device,the artificial intelligence BP neural network algorithm is used in the core computing model of the medical diagnosis system to improve the system computing power,enhance the medical intelligence-aided decision-making,and improve the clinical diagnosis and treatment efficiency.In the application process,combined with the characteristics of medical Big Data technology,through fog architecture design and Big Data technology integration,we could research the processing and analysis of heterogeneous data of the medical diagnosis system in the context of the Internet of Things.The results are promising:The medical platform network is smooth,the data storage space is sufficient,the data processing and analysis speed is fast,the diagnosis effect is remarkable,and it is a good assistant to doctors’treatment effect.It not only effectively solves the problem of low clinical diagnosis,treatment efficiency and quality,but also reduces the waiting time of patients,effectively solves the contradiction between doctors and patients,and improves the medical service quality and management level.展开更多
In distributed quantum computing(DQC),quantum hardware design mainly focuses on providing as many as possible high-quality inter-chip connections.Meanwhile,quantum software tries its best to reduce the required number...In distributed quantum computing(DQC),quantum hardware design mainly focuses on providing as many as possible high-quality inter-chip connections.Meanwhile,quantum software tries its best to reduce the required number of remote quantum gates between chips.However,this“hardware first,software follows”methodology may not fully exploit the potential of DQC.Inspired by classical software-hardware co-design,this paper explores the design space of application-specific DQC architectures.More specifically,we propose Auto Arch,an automated quantum chip network(QCN)structure design tool.With qubits grouping followed by a customized QCN design,AutoArch can generate a near-optimal DQC architecture suitable for target quantum algorithms.Experimental results show that the DQC architecture generated by Auto Arch can outperform other general QCN architectures when executing target quantum algorithms.展开更多
In this paper, we adopt Java platform to achieve a multi-tier distributed object enterprise computing model which provides an open, flexible, robust and cross-platform standard for enterprise applications of new gener...In this paper, we adopt Java platform to achieve a multi-tier distributed object enterprise computing model which provides an open, flexible, robust and cross-platform standard for enterprise applications of new generation. In addition to this model, we define remote server objects as session or entity objects according to their roles in a distributed application server, which separate information details from business operations for software reuse. A web store system is implement by using this multi-tier distributed object enterprise computing model.展开更多
Virtualization and distributed parallel architecture are typical cloud computing technologies.In the area of virtuatization technology,this article discusses physical resource pooling,resource pool management and use,...Virtualization and distributed parallel architecture are typical cloud computing technologies.In the area of virtuatization technology,this article discusses physical resource pooling,resource pool management and use,cluster fault location and maintenance,resource pool grouping,and construction and application of heterogeneous virtualization platforms.In the area of distributed technology,distributed file system and KeyNalue storage engine are discussed.A solution is proposed for the host bottleneck problem,and a standard storage interface is proposed for the distributed file system.A directory-based storage scheme for Key/Value storage engine is also proposed.展开更多
Load-time series data in mobile cloud computing of Internet of Vehicles(IoV)usually have linear and nonlinear composite characteristics.In order to accurately describe the dynamic change trend of such loads,this study...Load-time series data in mobile cloud computing of Internet of Vehicles(IoV)usually have linear and nonlinear composite characteristics.In order to accurately describe the dynamic change trend of such loads,this study designs a load prediction method by using the resource scheduling model for mobile cloud computing of IoV.Firstly,a chaotic analysis algorithm is implemented to process the load-time series,while some learning samples of load prediction are constructed.Secondly,a support vector machine(SVM)is used to establish a load prediction model,and an improved artificial bee colony(IABC)function is designed to enhance the learning ability of the SVM.Finally,a CloudSim simulation platform is created to select the perminute CPU load history data in the mobile cloud computing system,which is composed of 50 vehicles as the data set;and a comparison experiment is conducted by using a grey model,a back propagation neural network,a radial basis function(RBF)neural network and a RBF kernel function of SVM.As shown in the experimental results,the prediction accuracy of the method proposed in this study is significantly higher than other models,with a significantly reduced real-time prediction error for resource loading in mobile cloud environments.Compared with single-prediction models,the prediction method proposed can build up multidimensional time series in capturing complex load time series,fit and describe the load change trends,approximate the load time variability more precisely,and deliver strong generalization ability to load prediction models for mobile cloud computing resources.展开更多
With the continuous use of cloud and distributed computing, the threats associated with data and information technology (IT) in such an environment have also increased. Thus, data security and data leakage prevention ...With the continuous use of cloud and distributed computing, the threats associated with data and information technology (IT) in such an environment have also increased. Thus, data security and data leakage prevention have become important in a distributed environment. In this aspect, mobile agent-based systems are one of the latest mechanisms to identify and prevent the intrusion and leakage of the data across the network. Thus, to tackle one or more of the several challenges on Mobile Agent-Based Information Leakage Prevention, this paper aim at providing a comprehensive, detailed, and systematic study of the Distribution Model for Mobile Agent-Based Information Leakage Prevention. This paper involves the review of papers selected from the journals which are published in 2009 and 2019. The critical review is presented for the distributed mobile agent-based intrusion detection systems in terms of their design analysis, techniques, and shortcomings. Initially, eighty-five papers were identified, but a paper selection process reduced the number of papers to thirteen important reviews.展开更多
In this study, we delve into the realm of efficient Big Data Engineering and Extract, Transform, Load (ETL) processes within the healthcare sector, leveraging the robust foundation provided by the MIMIC-III Clinical D...In this study, we delve into the realm of efficient Big Data Engineering and Extract, Transform, Load (ETL) processes within the healthcare sector, leveraging the robust foundation provided by the MIMIC-III Clinical Database. Our investigation entails a comprehensive exploration of various methodologies aimed at enhancing the efficiency of ETL processes, with a primary emphasis on optimizing time and resource utilization. Through meticulous experimentation utilizing a representative dataset, we shed light on the advantages associated with the incorporation of PySpark and Docker containerized applications. Our research illuminates significant advancements in time efficiency, process streamlining, and resource optimization attained through the utilization of PySpark for distributed computing within Big Data Engineering workflows. Additionally, we underscore the strategic integration of Docker containers, delineating their pivotal role in augmenting scalability and reproducibility within the ETL pipeline. This paper encapsulates the pivotal insights gleaned from our experimental journey, accentuating the practical implications and benefits entailed in the adoption of PySpark and Docker. By streamlining Big Data Engineering and ETL processes in the context of clinical big data, our study contributes to the ongoing discourse on optimizing data processing efficiency in healthcare applications. The source code is available on request.展开更多
Today we witness the exponential growth of scientific research. This fast growth is possible thanks to the rapid development of computing systems since its first days in 1947 and the invention of transistor till the p...Today we witness the exponential growth of scientific research. This fast growth is possible thanks to the rapid development of computing systems since its first days in 1947 and the invention of transistor till the present days with high performance and scalable distributed computing systems. This fast growth of computing systems was first observed by Gordon E. Moore in 1965 and postulated as Moore’s Law. For the development of the scalable distributed computing systems, the year 2000 was a very special year. The first GHz speed processor, GB size memory and GB/s data transmission through network were achieved. Interestingly, in the same year the usable Grid computing systems emerged, which gave a strong impulse to a rapid development of distributed computing systems. This paper recognizes these facts that occurred in the year 2000, as the G-phenomena, a millennium cornerstone for the rapid development of scalable distributed systems evolved around the Grid and Cloud computing paradigms.展开更多
基金supported by NSF China(No.T2421002,62061146002,62020106005)。
文摘Distributed computing is an important topic in the field of wireless communications and networking,and its high efficiency in handling large amounts of data is particularly noteworthy.Although distributed computing benefits from its ability of processing data in parallel,the communication burden between different servers is incurred,thereby the computation process is detained.Recent researches have applied coding in distributed computing to reduce the communication burden,where repetitive computation is utilized to enable multicast opportunities so that the same coded information can be reused across different servers.To handle the computation tasks in practical heterogeneous systems,we propose a novel coding scheme to effectively mitigate the "straggling effect" in distributed computing.We assume that there are two types of servers in the system and the only difference between them is their computational capabilities,the servers with lower computational capabilities are called stragglers.Given any ratio of fast servers to slow servers and any gap of computational capabilities between them,we achieve approximately the same computation time for both fast and slow servers by assigning different amounts of computation tasks to them,thus reducing the overall computation time.Furthermore,we investigate the informationtheoretic lower bound of the inter-communication load and show that the lower bound is within a constant multiplicative gap to the upper bound achieved by our scheme.Various simulations also validate the effectiveness of the proposed scheme.
基金supported by the National Natural Science Foundation of China(62462040)the Yunnan Fundamental Research Projects(202501AT070345)the Major Science and Technology Projects in Yunnan Province(202202AD080013).
文摘Federated learning often experiences slow and unstable convergence due to edge-side data heterogeneity.This problem becomes more severe when edge participation rate is low,as the information collected from different edge devices varies significantly.As a result,communication overhead increases,which further slows down the convergence process.To address this challenge,we propose a simple yet effective federated learning framework that improves consistency among edge devices.The core idea is clusters the lookahead gradients collected from edge devices on the cloud server to obtain personalized momentum for steering local updates.In parallel,a global momentum is applied during model aggregation,enabling faster convergence while preserving personalization.This strategy enables efficient propagation of the estimated global update direction to all participating edge devices and maintains alignment in local training,without introducing extra memory or communication overhead.We conduct extensive experiments on benchmark datasets such as Cifar100 and Tiny-ImageNet.The results confirm the effectiveness of our framework.On CIFAR-100,our method reaches 55%accuracy with 37 fewer rounds and achieves a competitive final accuracy of 65.46%.Even under extreme non-IID scenarios,it delivers significant improvements in both accuracy and communication efficiency.The implementation is publicly available at https://github.com/sjmp525/CollaborativeComputing/tree/FedCCM(accessed on 20 October 2025).
基金This work was supported in part by the National Natural Science Foundation of China(61933015).
文摘The shortage of computation methods and storage devices has largely limited the development of multiobjective optimization in industrial processes.To improve the operational levels of the process industries,we propose a multi-objective optimization framework based on cloud services and a cloud distribution system.Real-time data from manufacturing procedures are first temporarily stored in a local database,and then transferred to the relational database in the cloud.Next,a distribution system with elastic compute power is set up for the optimization framework.Finally,a multi-objective optimization model based on deep learning and an evolutionary algorithm is proposed to optimize several conflicting goals of the blast furnace ironmaking process.With the application of this optimization service in a cloud factory,iron production was found to increase by 83.91 t∙d^(-1),the coke ratio decreased 13.50 kg∙t^(-1),and the silicon content decreased by an average of 0.047%.
基金funded in part by the National Natural Science Foundation of China (Grant no. 61772352, 62172061, 61871422)National Key Research and Development Project (Grants nos. 2020YFB1711800 and 2020YFB1707900)+2 种基金the Science and Technology Project of Sichuan Province (Grants no. 2021YFG0152, 2021YFG0025, 2020YFG0479, 2020YFG0322, 2020GFW035, 2020GFW033, 2020YFH0071)the R&D Project of Chengdu City (Grant no. 2019-YF05-01790-GX)the Central Universities of Southwest Minzu University (Grants no. ZYN2022032)
文摘In LEO(Low Earth Orbit)satellite communication systems,the satellite network is made up of a large number of satellites,the dynamically changing network environment affects the results of distributed computing.In order to improve the fault tolerance rate,a novel public blockchain consensus mechanism that applies a distributed computing architecture in a public network is proposed.Redundant calculation of blockchain ensures the credibility of the results;and the transactions with calculation results of a task are stored distributed in sequence in Directed Acyclic Graphs(DAG).The transactions issued by nodes are connected to form a net.The net can quickly provide node reputation evaluation that does not rely on third parties.Simulations show that our proposed blockchain has the following advantages:1.The task processing speed of the blockchain can be close to that of the fastest node in the entire blockchain;2.When the tasks’arrival time intervals and demanded working nodes(WNs)meet certain conditions,the network can tolerate more than 50%of malicious devices;3.No matter the number of nodes in the blockchain is increased or reduced,the network can keep robustness by adjusting the task’s arrival time interval and demanded WNs.
基金partly supported by National Key Basic Research Program of China(2016YFB1000100)partly supported by National Natural Science Foundation of China(NO.61402490)。
文摘To security support large-scale intelligent applications,distributed machine learning based on blockchain is an intuitive solution scheme.However,the distributed machine learning is difficult to train due to that the corresponding optimization solver algorithms converge slowly,which highly demand on computing and memory resources.To overcome the challenges,we propose a distributed computing framework for L-BFGS optimization algorithm based on variance reduction method,which is a lightweight,few additional cost and parallelized scheme for the model training process.To validate the claims,we have conducted several experiments on multiple classical datasets.Results show that our proposed computing framework can steadily accelerate the training process of solver in either local mode or distributed mode.
基金This work was supported by the National Key Research and Development Program of China(2021YFB2900603)the National Natural Science Foundation of China(61831008).
文摘A dynamic multi-beam resource allocation algorithm for large low Earth orbit(LEO)constellation based on on-board distributed computing is proposed in this paper.The allocation is a combinatorial optimization process under a series of complex constraints,which is important for enhancing the matching between resources and requirements.A complex algorithm is not available because that the LEO on-board resources is limi-ted.The proposed genetic algorithm(GA)based on two-dimen-sional individual model and uncorrelated single paternal inheri-tance method is designed to support distributed computation to enhance the feasibility of on-board application.A distributed system composed of eight embedded devices is built to verify the algorithm.A typical scenario is built in the system to evalu-ate the resource allocation process,algorithm mathematical model,trigger strategy,and distributed computation architec-ture.According to the simulation and measurement results,the proposed algorithm can provide an allocation result for more than 1500 tasks in 14 s and the success rate is more than 91%in a typical scene.The response time is decreased by 40%com-pared with the conditional GA.
文摘Recently, wireless distributed computing （WDC） concept has emerged promising manifolds improvements to current wireless technotogies. Despite the various expected benefits of this concept, significant drawbacks were addressed in the open literature. One of WDC key challenges is the impact of wireless channel quality on the load of distributed computations. Therefore, this research investigates the wireless channel impact on WDC performance when the tatter is applied to spectrum sensing in cognitive radio （CR） technology. However, a trade- off is found between accuracy and computational complexity in spectrum sensing approaches. Increasing these approaches accuracy is accompanied by an increase in computational complexity. This results in greater power consumption and processing time. A novel WDC scheme for cyclostationary feature detection spectrum sensing approach is proposed in this paper and thoroughly investigated. The benefits of the proposed scheme are firstly presented. Then, the impact of the wireless channel of the proposed scheme is addressed considering two scenarios. In the first scenario, workload matrices are distributed over the wireless channel
基金Supported by the National Basic Research Program of China (973 Program 2004CB318004), the National Natural Science Foundation of China (NSFC90204016) and the National High Technology Research and Development Program of China (2003AA144030)
文摘Distributed cryptographic computing system plays an important role since cryptographic computing is extremely computation sensitive. However, no general cryptographic computing system is available. Grid technology can give an efficient computational support for cryptographic applications. Therefore, a general-purpose grid-based distributed computing system called DCCS is put forward in this paper. The architecture of DCCS is simply described at first. The policy of task division adapted in DCCS is then presented. The method to manage subtask is further discussed in detail. Furthermore, the building and execution process of a computing job is revealed. Finally, the details of DCCS implementation under Globus Toolkit 4 are illustrated.
文摘This paper discusses the model of how the Agent is applied to implement distributed computing of Ada95 and presents a dynamic allocation strategy for distributed computing that based on pre-allocationand Agent. The aim of this strategy is realizing dynamic equilibrium allocation.
文摘In order to realize distributed computing of Ada95, this paper discusses Ada95's distributed system model and an implement model of Ada95's distributed computing-- workstation cluster model. Under this model, we presents a pre-allocation strategy for allocating the computation quantity of distributed units evenly among workstations and also reducing the communication expense between those distributed units.
基金Project supported by the Shanghai Leading Academic Discipline Project(Grant No.J50103)the National High-Technology Research and Development Program of China(Grant No.2009AA012201)+1 种基金the Major Technology R&D Program of Shanghai(Grant No.08DZ501600)the Science and Technology Pillar Project of Jiangxi(Grant No.2010BGB00604)
文摘As a parallel programming model, Map-Reduce is used for distributed computing of massive data. Map-Reduce model encapsulates the details of parallel implementation, fault-tolerant processing, local computing and load balancing, etc., provides a simple but powerful interface. In case of having no clear idea about distributed and parallel programming, this interface can be utilized to save development time. This paper introduces the method of using Hadoop, the open-source Map-Reduce software platform, to combine PCs to carry out scalable parallel computing. Our experiment using 12 PCs to compute N-body problem based on Map-Reduce model shows that we can get a 9.8x speedup ratio. This work indicates that the Map-Reduce can be applied in scalable parallel computing.
基金supported by 2020 Foshan Science and Technology Project(Numbering:2020001005356),Baoling Qin received the grant.
文摘Although the Internet of Things has been widely applied,the problems of cloud computing in the application of digital smart medical Big Data collection,processing,analysis,and storage remain,especially the low efficiency of medical diagnosis.And with the wide application of the Internet of Things and Big Data in the medical field,medical Big Data is increasing in geometric magnitude resulting in cloud service overload,insufficient storage,communication delay,and network congestion.In order to solve these medical and network problems,a medical big-data-oriented fog computing architec-ture and BP algorithm application are proposed,and its structural advantages and characteristics are studied.This architecture enables the medical Big Data generated by medical edge devices and the existing data in the cloud service center to calculate,compare and analyze the fog node through the Internet of Things.The diagnosis results are designed to reduce the business processing delay and improve the diagnosis effect.Considering the weak computing of each edge device,the artificial intelligence BP neural network algorithm is used in the core computing model of the medical diagnosis system to improve the system computing power,enhance the medical intelligence-aided decision-making,and improve the clinical diagnosis and treatment efficiency.In the application process,combined with the characteristics of medical Big Data technology,through fog architecture design and Big Data technology integration,we could research the processing and analysis of heterogeneous data of the medical diagnosis system in the context of the Internet of Things.The results are promising:The medical platform network is smooth,the data storage space is sufficient,the data processing and analysis speed is fast,the diagnosis effect is remarkable,and it is a good assistant to doctors’treatment effect.It not only effectively solves the problem of low clinical diagnosis,treatment efficiency and quality,but also reduces the waiting time of patients,effectively solves the contradiction between doctors and patients,and improves the medical service quality and management level.
基金Project supported by the National Key R&D Program of China(Grant No.2023YFA1009403)the National Natural Science Foundation of China(Grant Nos.62072176 and 62472175)the“Digital Silk Road”Shanghai International Joint Lab of Trustworthy Intelligent Software(Grant No.22510750100)。
文摘In distributed quantum computing(DQC),quantum hardware design mainly focuses on providing as many as possible high-quality inter-chip connections.Meanwhile,quantum software tries its best to reduce the required number of remote quantum gates between chips.However,this“hardware first,software follows”methodology may not fully exploit the potential of DQC.Inspired by classical software-hardware co-design,this paper explores the design space of application-specific DQC architectures.More specifically,we propose Auto Arch,an automated quantum chip network(QCN)structure design tool.With qubits grouping followed by a customized QCN design,AutoArch can generate a near-optimal DQC architecture suitable for target quantum algorithms.Experimental results show that the DQC architecture generated by Auto Arch can outperform other general QCN architectures when executing target quantum algorithms.
文摘In this paper, we adopt Java platform to achieve a multi-tier distributed object enterprise computing model which provides an open, flexible, robust and cross-platform standard for enterprise applications of new generation. In addition to this model, we define remote server objects as session or entity objects according to their roles in a distributed application server, which separate information details from business operations for software reuse. A web store system is implement by using this multi-tier distributed object enterprise computing model.
文摘Virtualization and distributed parallel architecture are typical cloud computing technologies.In the area of virtuatization technology,this article discusses physical resource pooling,resource pool management and use,cluster fault location and maintenance,resource pool grouping,and construction and application of heterogeneous virtualization platforms.In the area of distributed technology,distributed file system and KeyNalue storage engine are discussed.A solution is proposed for the host bottleneck problem,and a standard storage interface is proposed for the distributed file system.A directory-based storage scheme for Key/Value storage engine is also proposed.
基金This work was supported by Shandong medical and health science and technology development plan project(No.202012070393).
文摘Load-time series data in mobile cloud computing of Internet of Vehicles(IoV)usually have linear and nonlinear composite characteristics.In order to accurately describe the dynamic change trend of such loads,this study designs a load prediction method by using the resource scheduling model for mobile cloud computing of IoV.Firstly,a chaotic analysis algorithm is implemented to process the load-time series,while some learning samples of load prediction are constructed.Secondly,a support vector machine(SVM)is used to establish a load prediction model,and an improved artificial bee colony(IABC)function is designed to enhance the learning ability of the SVM.Finally,a CloudSim simulation platform is created to select the perminute CPU load history data in the mobile cloud computing system,which is composed of 50 vehicles as the data set;and a comparison experiment is conducted by using a grey model,a back propagation neural network,a radial basis function(RBF)neural network and a RBF kernel function of SVM.As shown in the experimental results,the prediction accuracy of the method proposed in this study is significantly higher than other models,with a significantly reduced real-time prediction error for resource loading in mobile cloud environments.Compared with single-prediction models,the prediction method proposed can build up multidimensional time series in capturing complex load time series,fit and describe the load change trends,approximate the load time variability more precisely,and deliver strong generalization ability to load prediction models for mobile cloud computing resources.
文摘With the continuous use of cloud and distributed computing, the threats associated with data and information technology (IT) in such an environment have also increased. Thus, data security and data leakage prevention have become important in a distributed environment. In this aspect, mobile agent-based systems are one of the latest mechanisms to identify and prevent the intrusion and leakage of the data across the network. Thus, to tackle one or more of the several challenges on Mobile Agent-Based Information Leakage Prevention, this paper aim at providing a comprehensive, detailed, and systematic study of the Distribution Model for Mobile Agent-Based Information Leakage Prevention. This paper involves the review of papers selected from the journals which are published in 2009 and 2019. The critical review is presented for the distributed mobile agent-based intrusion detection systems in terms of their design analysis, techniques, and shortcomings. Initially, eighty-five papers were identified, but a paper selection process reduced the number of papers to thirteen important reviews.
文摘In this study, we delve into the realm of efficient Big Data Engineering and Extract, Transform, Load (ETL) processes within the healthcare sector, leveraging the robust foundation provided by the MIMIC-III Clinical Database. Our investigation entails a comprehensive exploration of various methodologies aimed at enhancing the efficiency of ETL processes, with a primary emphasis on optimizing time and resource utilization. Through meticulous experimentation utilizing a representative dataset, we shed light on the advantages associated with the incorporation of PySpark and Docker containerized applications. Our research illuminates significant advancements in time efficiency, process streamlining, and resource optimization attained through the utilization of PySpark for distributed computing within Big Data Engineering workflows. Additionally, we underscore the strategic integration of Docker containers, delineating their pivotal role in augmenting scalability and reproducibility within the ETL pipeline. This paper encapsulates the pivotal insights gleaned from our experimental journey, accentuating the practical implications and benefits entailed in the adoption of PySpark and Docker. By streamlining Big Data Engineering and ETL processes in the context of clinical big data, our study contributes to the ongoing discourse on optimizing data processing efficiency in healthcare applications. The source code is available on request.
基金in part,supported by the European Commission through the EU FP7 SEE GRID SCI and SCI BUS projectsby the Grant 098-0982562-2567 awarded by the Ministry of Science,Education and Sports of the Republic of Croatia.
文摘Today we witness the exponential growth of scientific research. This fast growth is possible thanks to the rapid development of computing systems since its first days in 1947 and the invention of transistor till the present days with high performance and scalable distributed computing systems. This fast growth of computing systems was first observed by Gordon E. Moore in 1965 and postulated as Moore’s Law. For the development of the scalable distributed computing systems, the year 2000 was a very special year. The first GHz speed processor, GB size memory and GB/s data transmission through network were achieved. Interestingly, in the same year the usable Grid computing systems emerged, which gave a strong impulse to a rapid development of distributed computing systems. This paper recognizes these facts that occurred in the year 2000, as the G-phenomena, a millennium cornerstone for the rapid development of scalable distributed systems evolved around the Grid and Cloud computing paradigms.
