Our daily life is changing by the smart objects, such as smart watches, smart phones etc. They make the cyber world and the physical world integrated by their abundant abilities of sensing, communication and computati...
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ISBN:
(纸本)9781479982172
Our daily life is changing by the smart objects, such as smart watches, smart phones etc. They make the cyber world and the physical world integrated by their abundant abilities of sensing, communication and computation etc. Focusing on a wide range of the integrated network, a statistical based strategy was introduced to get a special kind of link between objects, the statistical probability communication link. To get a maximized information spread probability for grouped people, this paper introduced a distributed, yet efficient algorithm naming DMPID algorithm, for finding a sub-network to spread people oriented inforamtion. The DMPID algorithm take the size of the selection and the information spread probability into account, and made a balance between the two parameters. Extended simulation showed that the DMPID algorithm performs well in different distributed networks.
Resource allocation for multi-user across multiple data centers is an important problem in cloud computing environments. Many geographically-distributed users may request virtualized resources simultaneously. And the ...
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Non-negative matrix factorization (NMF) is a powerful dimension reduction method and has been widely used in many pattern recognition and computer vision problems. However, conventional NMF methods are neither robust ...
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ISBN:
(纸本)9781479919611
Non-negative matrix factorization (NMF) is a powerful dimension reduction method and has been widely used in many pattern recognition and computer vision problems. However, conventional NMF methods are neither robust enough as their loss functions are sensitive to outliers, nor discriminative because they completely ignore labels in a dataset. In this paper, we proposed a correntropy supervised NMF (CSNMF) to simultaneously overcome aforementioned deficiencies. In particular, CSNMF maximizes the correntropy between the data matrix and its reconstruction in low-dimensional space to inhibit outliers during learning the subspace, and narrows the minimizes the distances between coefficients of any two samples with the same class labels to enhance the subsequent classification performance. To solve CSNMF, we developed a multiplicative update rules and theoretically proved its convergence. Experimental results on popular face image datasets verify the effectiveness of CSNMF comparing with NMF, its supervised variants, and its robustified variants.
Virtualization is the foundation for cloud computing, and the virtualization can not be achieved without software defined, elastic, flexible and scalable virtual layers. Unfortunately, if multiple virtual storage devi...
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Virtualization is the foundation for cloud computing, and the virtualization can not be achieved without software defined, elastic, flexible and scalable virtual layers. Unfortunately, if multiple virtual storage devices are chained together, the system may be subject to severe performance degradation. While the read-ahead (RA) mechanism in storage devices plays a very important role to improve I/O performance, RA may not be effective as expected for multiple virtualization layers, since it is originally designed for one layer only. When I/O requests are passed through a long I/O path, they may trigger a chain reaction and lead to unnecessary data transmission and thus bandwidth waste. In this paper, we study the dynamic behavior of RA through multiple I/O layers and demonstrate that if controlled well, RA can greatly accelerate I/O speed. We present RAFlow, a RA control mechanism, to effectively improve I/O performance by strategically expanding RA window at each layer. Our real-world experiments show that it can achieve 20% to 50% performance improvement in I/O paths with up to 8 virtualized storage devices.
High-performance computing (HPC) clusters are currently faced with two major challenges - namely, the dynamic nature of new generation of applications and the heterogeneity of platforms - if they are going to be usefu...
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Hi-GAL is a large-scale survey of the Galactic plane, performed with Herschel in five infrared continuum bands between 70 and 500 µm. We present a band-merged catalogue of spatially matched sources and their prop...
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Static data-race detection is a powerful tool by providing clues for dynamic approaches to only instrument certain memory accesses. However, static data-race analysis suffers from high false positive rate. A key reaso...
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Extraordinary large datasets of high performance computing applications require improvement in existing storage and retrieval mechanisms. Moreover, enlargement of the gap between data processing and I/O operations'...
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ISBN:
(纸本)9781479915194
Extraordinary large datasets of high performance computing applications require improvement in existing storage and retrieval mechanisms. Moreover, enlargement of the gap between data processing and I/O operations' throughput will bound the system performance to storage and retrieval operations and remarkably reduce the overall performance of high performance computing clusters. File replication is a way to improve the performance of I/O operations and increase network utilization by storing several copies of every file. Furthermore, this will lead to a more reliable and fault-tolerant storage cluster. In order to improve the response time of I/O operations, we have proposed a mechanism that estimates the required number of replicas for each file based on its popularity. Besides that, the remaining space of storage cluster is considered in the evaluation of replication factors and the number of replicas is adapted to the storage state. We have implemented the proposed mechanism using HDFS and evaluated it using MapReduce framework. Evaluation results prove its capability to improve the response time of read operations and increase network utilization. Consequently, this mechanism reduces the overall response time of read operations by considering files' popularity in replication process and adapts the replication factor to the cluster state.
High-performance computing (HPC) clusters are currently faced with two major challenges - namely, the dynamic nature of new generation of applications and the heterogeneity of platforms - if they are going to be usefu...
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High-performance computing (HPC) clusters are currently faced with two major challenges - namely, the dynamic nature of new generation of applications and the heterogeneity of platforms - if they are going to be useful for exascale computing. Processes running these applications may well demand unpredictable requirements and changes to system configuration and capabilities at runtime, thereby requiring fast system response without sacrificing the transparency and integrity of the reconfigured empowered system that is running on a heterogeneous platform. While a challenge in and of itself, platform heterogeneity is both useful and instrumental in the handling of unpredictable requests. The realization of such a dynamically reconfigurable and heterogeneous HPC cluster system for exascale computing requires a model to guide running processes to determine if they need empowerment of the current cluster, and if yes, by how much. To show the feasibility of empowerment of traditional HPC clusters for exascale computing, we have selected Beowulf as a noble candidate cluster and present a mathematical model for the empowerment of Beowulf clusters for exascale computing (EBEC). We have developed the model in line with Beowulf's cluster approach and by using vector space algebra. In contrast to traditional hardware-oriented approaches to improvise the performance of clusters, we use a software approach to the development of the proposed model by emphasizing processes, which act as the creators of the cluster and thus should decide on system (re)configuration, as the principal building blocks of the system. We have also adopted a new approach to heterogeneity by considering heterogeneity at different levels including hardware, system software, application software, and system functionality. In addition to support for heterogeneity and dynamic reconfiguration, the proposed model includes support for scalability that is crucial to exascale computing too.
Social brainstorming has become a popular group creativity technique to create a large number of ideas for solving problems. The social computing, for its advance in information processing and network communication, c...
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