I IntroductionSmartphones have become more complex in terms of functions and third-party applications, and this makes lhem a living space for malware. People store private information such as accounts and passwordson ...I IntroductionSmartphones have become more complex in terms of functions and third-party applications, and this makes lhem a living space for malware. People store private information such as accounts and passwordson their smartphones, the loss of which could have serious con- sequences.展开更多
This paper proposes an analytical mining tool for big graph data based on MapReduce and bulk synchronous parallel (BSP) com puting model. The tool is named Mapreduce and BSP based Graphmining tool (MBGM). The core...This paper proposes an analytical mining tool for big graph data based on MapReduce and bulk synchronous parallel (BSP) com puting model. The tool is named Mapreduce and BSP based Graphmining tool (MBGM). The core of this mining system are four sets of parallel graphmining algorithms programmed in the BSP parallel model and one set of data extractiontransformationload ing (ETE) algorithms implemented in MapReduce. To invoke these algorithm sets, we designed a workflow engine which optimized for cloud computing. Finally, a welldesigned data management function enables users to view, delete and input data in the Ha doop distributed file system (HDFS). Experiments on artificial data show that the components of graphmining algorithm in MBGM are efficient.展开更多
This paper describes an improved design for WebRTC technolo- gy. With this design, WebRTC communication at client side, server side, and between these two sides is improved. HTML5 WebSocket, media negotiation and synt...This paper describes an improved design for WebRTC technolo- gy. With this design, WebRTC communication at client side, server side, and between these two sides is improved. HTML5 WebSocket, media negotiation and synthesis, network address translator (NAT)/firewall traversal, Session Initiation Protocol (SIP) signaling interaction, and P2P communication security are all used in this improved design. This solution solves cross- browser running problem of WebRTC applications, reduces reli- ance on client-side processing capability, and reduces band- width consumption. With this design, WebRTC also become more scalable.展开更多
Cloud computing technology is changing the development and usage patterns of IT infrastructure and applications. Virtualized and distributed systems as well as unified management and scheduling has greatly im proved c...Cloud computing technology is changing the development and usage patterns of IT infrastructure and applications. Virtualized and distributed systems as well as unified management and scheduling has greatly im proved computing and storage. Management has become easier, andOAM costs have been significantly reduced. Cloud desktop technology is develop ing rapidly. With this technology, users can flexibly and dynamically use virtual ma chine resources, companies' efficiency of using and allocating resources is greatly improved, and information security is ensured. In most existing virtual cloud desk top solutions, computing and storage are bound together, and data is stored as im age files. This limits the flexibility and expandability of systems and is insufficient for meetinz customers' requirements in different scenarios.展开更多
MapReduce is a programming model for processing large data sets, and Hadoop is the most popular open-source implementation of MapReduce. To achieve high performance, up to 190 Hadoop configuration parameters must be m...MapReduce is a programming model for processing large data sets, and Hadoop is the most popular open-source implementation of MapReduce. To achieve high performance, up to 190 Hadoop configuration parameters must be manually tunned. This is not only time-consuming but also error-pron. In this paper, we propose a new performance model based on random forest, a recently devel- oped machine-learning algorithm. The model, called RFMS, is used to predict the performance of a Hadoop system according to the system' s configuration parameters. RFMS is created from 2000 distinct fine-grained performance observations with different Hadoop configurations. We test RFMS against the measured performance of representative workloads from the Hadoop Micro-benchmark suite. The results show that the prediction accuracy of RFMS achieves 95% on average and up to 99%. This new, highly accurate prediction model can be used to automatically optimize the performance of Hadoop systems.展开更多
With user-generated content, anyone can De a content creator. This phenomenon has infinitely increased the amount of information circulated online, and it is beeoming harder to efficiently obtain required information....With user-generated content, anyone can De a content creator. This phenomenon has infinitely increased the amount of information circulated online, and it is beeoming harder to efficiently obtain required information. In this paper, we describe how natural language processing and text mining can be parallelized using Hadoop and Message Passing Interface. We propose a parallel web text mining platform that processes massive amounts data quickly and efficiently. Our web knowledge service platform is designed to collect information about the IT and telecommunications industries from the web and process this in-formation using natural language processing and data-mining techniques.展开更多
This paper presents a comprehensive exploration into the integration of Internet of Things(IoT),big data analysis,cloud computing,and Artificial Intelligence(AI),which has led to an unprecedented era of connectivity.W...This paper presents a comprehensive exploration into the integration of Internet of Things(IoT),big data analysis,cloud computing,and Artificial Intelligence(AI),which has led to an unprecedented era of connectivity.We delve into the emerging trend of machine learning on embedded devices,enabling tasks in resource-limited environ-ments.However,the widespread adoption of machine learning raises significant privacy concerns,necessitating the development of privacy-preserving techniques.One such technique,secure multi-party computation(MPC),allows collaborative computations without exposing private inputs.Despite its potential,complex protocols and communication interactions hinder performance,especially on resource-constrained devices.Efforts to enhance efficiency have been made,but scalability remains a challenge.Given the success of GPUs in deep learning,lever-aging embedded GPUs,such as those offered by NVIDIA,emerges as a promising solution.Therefore,we propose an Embedded GPU-based Secure Two-party Computation(EG-STC)framework for Artificial Intelligence(AI)systems.To the best of our knowledge,this work represents the first endeavor to fully implement machine learning model training based on secure two-party computing on the Embedded GPU platform.Our experimental results demonstrate the effectiveness of EG-STC.On an embedded GPU with a power draw of 5 W,our implementation achieved a secure two-party matrix multiplication throughput of 5881.5 kilo-operations per millisecond(kops/ms),with an energy efficiency ratio of 1176.3 kops/ms/W.Furthermore,leveraging our EG-STC framework,we achieved an overall time acceleration ratio of 5–6 times compared to solutions running on server-grade CPUs.Our solution also exhibited a reduced runtime,requiring only 60%to 70%of the runtime of previously best-known methods on the same platform.In summary,our research contributes to the advancement of secure and efficient machine learning implementations on resource-constrained embedded devices,paving the way for broader adoption of AI technologies in various applications.展开更多
基金supported in part by the Fundamental Research Funds for the Central Universities of China (Grant No.WK0110000007)the Specialized Research Fund for the Doctoral Program of Higher Education of China (Grant No.20113402120026)+2 种基金the Natural Science Foundation of Anhui Province,China (Grant No. 1208085QF112)the Foundation for Young Talents in College of Anhui Province,China (GrantNo.2012SQRL001ZD)the Research Fund of ZTE Corpo ration
文摘I IntroductionSmartphones have become more complex in terms of functions and third-party applications, and this makes lhem a living space for malware. People store private information such as accounts and passwordson their smartphones, the loss of which could have serious con- sequences.
基金supported by ZTE Industry-Academia-Research Cooperaton Funds
文摘This paper proposes an analytical mining tool for big graph data based on MapReduce and bulk synchronous parallel (BSP) com puting model. The tool is named Mapreduce and BSP based Graphmining tool (MBGM). The core of this mining system are four sets of parallel graphmining algorithms programmed in the BSP parallel model and one set of data extractiontransformationload ing (ETE) algorithms implemented in MapReduce. To invoke these algorithm sets, we designed a workflow engine which optimized for cloud computing. Finally, a welldesigned data management function enables users to view, delete and input data in the Ha doop distributed file system (HDFS). Experiments on artificial data show that the components of graphmining algorithm in MBGM are efficient.
基金supported by the National Science and Technology Major Project of the Ministry of Science and Technology of China(2011ZX0300200201)
文摘This paper describes an improved design for WebRTC technolo- gy. With this design, WebRTC communication at client side, server side, and between these two sides is improved. HTML5 WebSocket, media negotiation and synthesis, network address translator (NAT)/firewall traversal, Session Initiation Protocol (SIP) signaling interaction, and P2P communication security are all used in this improved design. This solution solves cross- browser running problem of WebRTC applications, reduces reli- ance on client-side processing capability, and reduces band- width consumption. With this design, WebRTC also become more scalable.
文摘Cloud computing technology is changing the development and usage patterns of IT infrastructure and applications. Virtualized and distributed systems as well as unified management and scheduling has greatly im proved computing and storage. Management has become easier, andOAM costs have been significantly reduced. Cloud desktop technology is develop ing rapidly. With this technology, users can flexibly and dynamically use virtual ma chine resources, companies' efficiency of using and allocating resources is greatly improved, and information security is ensured. In most existing virtual cloud desk top solutions, computing and storage are bound together, and data is stored as im age files. This limits the flexibility and expandability of systems and is insufficient for meetinz customers' requirements in different scenarios.
基金supported by the cooperation project of Research on Green Cloud IDC Resource Scheduling with ZTE Corporation
文摘MapReduce is a programming model for processing large data sets, and Hadoop is the most popular open-source implementation of MapReduce. To achieve high performance, up to 190 Hadoop configuration parameters must be manually tunned. This is not only time-consuming but also error-pron. In this paper, we propose a new performance model based on random forest, a recently devel- oped machine-learning algorithm. The model, called RFMS, is used to predict the performance of a Hadoop system according to the system' s configuration parameters. RFMS is created from 2000 distinct fine-grained performance observations with different Hadoop configurations. We test RFMS against the measured performance of representative workloads from the Hadoop Micro-benchmark suite. The results show that the prediction accuracy of RFMS achieves 95% on average and up to 99%. This new, highly accurate prediction model can be used to automatically optimize the performance of Hadoop systems.
文摘With user-generated content, anyone can De a content creator. This phenomenon has infinitely increased the amount of information circulated online, and it is beeoming harder to efficiently obtain required information. In this paper, we describe how natural language processing and text mining can be parallelized using Hadoop and Message Passing Interface. We propose a parallel web text mining platform that processes massive amounts data quickly and efficiently. Our web knowledge service platform is designed to collect information about the IT and telecommunications industries from the web and process this in-formation using natural language processing and data-mining techniques.
基金supported in part by Major Science and Technology Demonstration Project of Jiangsu Provincial Key R&D Program under Grant No.BE2023025in part by the National Natural Science Foundation of China under Grant No.62302238+2 种基金in part by the Natural Science Foundation of Jiangsu Province under Grant No.BK20220388in part by the Natural Science Research Project of Colleges and Universities in Jiangsu Province under Grant No.22KJB520004in part by the China Postdoctoral Science Foundation under Grant No.2022M711689.
文摘This paper presents a comprehensive exploration into the integration of Internet of Things(IoT),big data analysis,cloud computing,and Artificial Intelligence(AI),which has led to an unprecedented era of connectivity.We delve into the emerging trend of machine learning on embedded devices,enabling tasks in resource-limited environ-ments.However,the widespread adoption of machine learning raises significant privacy concerns,necessitating the development of privacy-preserving techniques.One such technique,secure multi-party computation(MPC),allows collaborative computations without exposing private inputs.Despite its potential,complex protocols and communication interactions hinder performance,especially on resource-constrained devices.Efforts to enhance efficiency have been made,but scalability remains a challenge.Given the success of GPUs in deep learning,lever-aging embedded GPUs,such as those offered by NVIDIA,emerges as a promising solution.Therefore,we propose an Embedded GPU-based Secure Two-party Computation(EG-STC)framework for Artificial Intelligence(AI)systems.To the best of our knowledge,this work represents the first endeavor to fully implement machine learning model training based on secure two-party computing on the Embedded GPU platform.Our experimental results demonstrate the effectiveness of EG-STC.On an embedded GPU with a power draw of 5 W,our implementation achieved a secure two-party matrix multiplication throughput of 5881.5 kilo-operations per millisecond(kops/ms),with an energy efficiency ratio of 1176.3 kops/ms/W.Furthermore,leveraging our EG-STC framework,we achieved an overall time acceleration ratio of 5–6 times compared to solutions running on server-grade CPUs.Our solution also exhibited a reduced runtime,requiring only 60%to 70%of the runtime of previously best-known methods on the same platform.In summary,our research contributes to the advancement of secure and efficient machine learning implementations on resource-constrained embedded devices,paving the way for broader adoption of AI technologies in various applications.