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Software and Hardware processing Method for Blockchain Light Node SPV Verification

Software and Hardware Processing Method for Blockchain Light...

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IEEE international conference on Big Data and Cloud Computing (BdCloud)

作者： Zhan’Gang Ma Jiandong Yan Feng Zou Xixin Cao dept. Integrated Circuits and Intelligent Systems School of Software and Microelectronics (of Peking University) Beijing China dept. Development Planning and Strategic Inverstment Ruiyuan Venture Capital Co. Ltd(of Changzhou Zhonglou Jinlong Holding Group) Changzhou China

With the application of blockchain light nodes in embedded devices, how to alleviate computing pressure brought by complex operations such as transaction’s SPV Verification for CPU of embedded devices and improve the performance of devices in these aspects has gradually become a research topic in industry and academia. This paper proposes a series of methods to improve the performance of blockchain SPV Verification from the perspectives of system architecture and hash computing unit: (1) According to the computational characteristics of SPV Verification, this paper customizes macro instructions and microinstructions for the coprocessor to meet the requirements of flexibility; The built-in dedicated cache holds transaction data fetched from external memory and intermediate data generated by internal Hash Computing Unit, which not only prepares transaction data for hash computation, but also avoids frequent access to the bus and external memory. (2) Techniques like two-round unfolded computing, timing-balanced pipeline architecture and optimized adders are adopted to improve the performance of SHA256 computation. (3) When double hash computing is required for transactions, Hash Computing Unit can directly perform the second hash computation based on the first hash computation, reducing the frequency of accessing to external memory, thereby improving the performance to a certain extent. Through these methods, the performance of the hardware coprocessor for SVP verification of transactions is more than double that of traditional solutions.

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Improved cuckoo search with artificial bee colony for efficient load balancing in cloud computing environment 3rd

Improved cuckoo search with artificial bee colony for effici...

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3rd international conference on Smart Innovations in Communications and Computational Sciences, ICSICCS 2020

作者： Kumar, Rakesh Chaturvedi, Abhay Department of Computer Engineering and Applications GLA University Mathura281406 India Department of Electronics GLA University Mathura281406 India

ISBN: (纸本)9789811553448

Task scheduling and load balancing are major issues in cloud environments. Utilization of resource is affected directly by this. In research field of cloud, aspect of load balancing has to be considered. On front as well as back end, it has a serious impact. For balancing load, improved cuckoo search + artificial bee colony (ICSABC) algorithm is proposed in this work. It is bio-inspired load balancing scheduling algorithm. On VMs, load balance level is improved by enabling process of scheduling efficiently. In cloud computing, most important work is task scheduling. Users are paying based on resource usage time. Among resources of system, load must be distributed evenly. It increases the utilization of resource and execution time is reduced. Best task-to-virtual machine mapping is computed by proposed algorithm. Speed of VM processing and submitted workload’s length has an effect on it. Best average VM load is achieved by proposed ICSABC as shown by results of experimentation. It also results in high accuracy and low complexity time, when compared with existing methods. © Springer Nature Singapore Pte Ltd 2021.

关键词： Cloud computing

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The Technique of Operational processing of Heterogeneous Surveillance Data in Assessing Situation in Geographic Information Systems

The Technique of Operational Processing of Heterogeneous Sur...

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IEEE international conference on Advanced Trends in Information Theory (ATIT)

作者： Mykhailo Rakushev Oleksandr Matsko Oleksandr Permiakov Oleksandr Lavrinchuk Oleksandr Koshlan Ihor Varlamov National Defence University of Ukraine Named After Ivan Cherniakhovskyi Kyiv Ukraine

ISBN: (纸本)9781665406673

The paper deals with the analysis of observational data in the assessment of the situation developed technique of heterogeneous data operational processing in assessing situation with the help of Geographic Information Systems, which allow to store raw data about surveillance objects, structured heterogeneous surveillance data due to the principle "Data Lake".The basis of the technique is the Model of Storage of Heterogeneous Data, which differs from the known ones by the presence of templates of objects of observation and templates of parameters of objects of observation, which allow distributed storage of both unstructured heterogeneous data and structured data according to a certain scheme, to access data.

关键词： Decision support systems Surveillance distributed databases Memory Big Data applications Market research Data models

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Enhancing Load-Balancing of MPI applications with Workshare 27th

Enhancing Load-Balancing of MPI Applications with Workshare

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27th international European conference on parallel and distributed Computing (Euro-Par)

作者： Dionisi, Thomas Bouhrour, Stephane Jaeger, Julien Carribault, Patrick Perache, Marc Exascale Comp Res Lab 2 Rue Piquetterie F-91680 Bruyeres Le Chatel France CEA DAM DIF F-91297 Arpajon France Univ Paris Saclay CEA Lab Informat Haute Performance Calcul & Simulat F-91680 Bruyeres Le Chatel France

ISBN: (纸本)9783030856656;9783030856649

Some high-performance parallel applications (e.g., simulation codes) are, by nature, prone to computational imbalance. With various elements, such as particles or multiple materials, evolving in a fixed space (with different boundary conditions), an MPI process can easily end up with more operations to perform than its neighbors. This computational imbalance causes performance loss. Load-balancing methods are used to limit such negative impacts. However, most load-balancing schemes rely on shared-memory models, and those handling MPI load-balancing use too much heavy machinery for efficient intra-node load-balancing. In this paper, we present the MPI Workshare concept. With MPI Workshare, we propose a programming interface based on directives, and the associated implementation, to leverage light MPI intranode load-balancing. In this work, we focus on loop worksharing. The similarity of our directives with OpenMP ones makes our interface easy to understand and to use. We provide an implementation of both the runtime and compiler directive support. Experimental results on well-known mini-applications (MiniFE, LULESH) show that MPI Workshare succeeds in maintaining the same level of performance as well-balanced workloads even with high imbalance parameter values.

关键词： Application programming interfaces (API)

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Spark Accelerated Implementation of parallel Attribute Reduction from Incomplete Data

Spark Accelerated Implementation of Parallel Attribute Reduc...

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international Joint conference on Rough Sets (IJCRS)

作者： Cao, Qian Luo, Chuan Li, Tianrui Chen, Hongmei Sichuan Univ Coll Comp Sci Chengdu 610065 Peoples R China Southwest Jiaotong Univ Sch Informat Sci & Technol Chengdu 610031 Peoples R China

ISBN: (纸本)9783030873349;9783030873332

Attribute reduction is a significant process of data preprocessing to overcome the challenges posed by multidimensional data analysis. Missing values in the data are usually unavoidable in the real applications, so it is important to select features with high importance efficiently in incomplete data. The theory of rough sets has been widely used in attribute reduction for uncertain data mining. To enable the rough set theory for large-scale incomplete data analysis, this paper develops a novel distributed attribute reduction algorithm based on Apache Spark cluster computing platform. By taking the advantage of positive approximation technique for reducing the data broadcasting gradually while reducing each redundant attribute iteratively, the proposed algorithm can significantly accelerate the attribute reduction in leveraging a computer cluster when processing large-scale incomplete data. Numerical experiments on different UCI data sets evidences the proposed parallel algorithm achieves high performance in terms of extensibility and scalability.

关键词： Attribute reduction Incomplete data Rough sets parallel computing Apache Spark

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M3AT: Monitoring Agents Assignment Model for Data-Intensive applications 28

M3AT: Monitoring Agents Assignment Model for Data-Intensive ...

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28th Euromicro international conference on parallel, distributed and Network-Based processing (PDP)

作者： Kashansky, Vladislav Kimovski, Dragi Prodan, Radu Agrawalt, Prateek Marozzo, Fabrizio Iuhaszl, Gabriel Justyna, Marek Garcia-Blas, Javier Univ Klagenfurt Inst Informat Technol Klagenfurt Austria South Ural State Univ Sch Elect Engn & Comp Sci Chelyabinsk Russia Lovely Profess Univ Phagwara India Univ Calabria DIMES Dept Calabria Italy West Univ Timisoara Timisoara Romania Poznan Supercomp & Networking Ctr Poznan Poland Univ Carlos III Madrid Dept Comp Sci & Engn Madrid Spain

ISBN: (纸本)9781728165820

Nowadays, massive amounts of data are acquired, transferred, and analyzed nearly in real-time by utilizing a large number of computing and storage elements interconnected through high-speed communication networks. However, one issue that still requires research effort is to enable efficient monitoring of applications and infrastructures of such complex systems. In this paper, we introduce an Integer Linear Programming (ILP) model called M3AT for optimized assignment of monitoring agents and aggregators on large-scale computing systems. We identified a set of requirements from three representative data-intensive applications and exploited them to define the model's input parameters. We evaluated the scalability of M3AT using the Constraint Integer Programing (SCIP) solver with default configuration based on synthetic data sets. Preliminary results show that the model provides optimal assignments for sub-systems composed of up to 200 monitoring agents with complex I/O policies, while keeping the number of aggregators constant and demonstrates variable sensitivity with respect to the scale of monitoring data aggregators and limitation policies imposed.

关键词： Monitoring systems high performance computing aggregation systems control data-intensive systems generalized assignment problem SCIP optimization suite

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Comparative Analysis of Stereo-Matching Technologies for Emphasizing Stereo-Vision applications

Comparative Analysis of Stereo-Matching Technologies for Emp...

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Advances in Computing, Communication Control and Networking (ICAC3N), international conference on

作者： V. Kavitha Swami Dayananda College of Arts & Science Manjakudi India

ISBN: (纸本)9781665438124

Stereo vision is a growing research domain which seeks the attention of various researchers to attain deeper scene extraction. This work provides an extensive analysis towards the stereo-matching algorithms and stereo-vision to resolve the problems related to it. The analysis towards the stereo matching technologies is executed with benchmark standards with the focus on stereo vision methods. Thus the comparison of stereo matching algorithms can be done through the implementation of stereo vision application in a particular domain so that the results for the algorithms are comparatively analyzed. In most cases, the analysis and comparison are performed with statistical analysis and emphasize the benefits of various stereo algorithms. Some approaches give higher computational cost with expected outcomes over the lower time frame and provides competency towards parallel processing. The results obtained from the various stereo matching algorithms through the identified different parameters of bad pixels.

关键词： Costs Target tracking Image resolution Statistical analysis Navigation Process control parallel processing

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DROAllocator: A Dynamic Resource-Aware Operator Allocation Framework in distributed Streaming processing 17th

DROAllocator: A Dynamic Resource-Aware Operator Allocation ...

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17th IFIP WG 10.3 international conference on Network and parallel Computing, NPC 2020

作者： Liu, Fan Jin, Zongze Mu, Weimin Zhu, Weilin Zhang, Yun Wang, Weiping Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9783030794774

With the rapid development of Internet services and the Internet of Things (IoT), many studies focus on operator allocation to enhance the DSPAs’ (data stream processing applications) performance and resource utilization. However, the existing approaches ignore the dynamic changes of the node resources to allocate the operator instances to guarantee the performance, which increasing the number of migration leads to the waste of resources and the instability. To address these issues, we propose a framework named DROAllocator to select the appropriate nodes to allocate the operator instances. By capturing the change tendency of the node resources and the operator performance, our allocation mechanism decreases the number of migration to enhance the performance. The experimental results show the DROAllocator not only decrease the number of migrations to allocate the operator instances to ensure the end-to-end throughput and the latency, but also enhance the resource utilization. © 2021, IFIP international Federation for Information processing.

关键词： Internet of things

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distributed Novelty Detection at the Edge for IoT Network Security 21st

Distributed Novelty Detection at the Edge for IoT Network Se...

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21st international conference on Computational Science and Its applications (ICCSA)

作者： Puhl, Luis Cassales, Guilherme Weigert Guardia, Helio Crestana Senger, Hermes Univ Fed Sao Carlos Sao Carlos Brazil

ISBN: (纸本)9783030869700;9783030869694

The ongoing implementation of the Internet of Things (IoT) is sharply increasing the number and variety of small devices on edge networks. Likewise, the attack opportunities for hostile agents also increases, requiring more effort from network administrators and strategies to detect and react to those threats. For a network security system to operate in the context of edge and IoT, it has to comply with processing, storage, and energy requirements alongside traditional requirements for stream and network analysis like accuracy and scalability. Using a previously defined architecture (IDSA-IoT), we address the construction and evaluation of a support mechanism for distributed Network Intrusion Detection Systems based on the MINAS Data Stream Novelty Detection algorithm. We discuss the algorithm steps, how it can be deployed in a distributed environment, the impacts on the accuracy and evaluate performance and scalability using a cluster of constrained devices commonly found in IoT scenarios. The obtained results show a negligible accuracy loss in the distributed version but also a small reduction in the execution time using low profile devices. Although not efficient, the parallel version showed to be viable as the proposed granularity provides equivalent accuracy and viable response times.

关键词： Novelty detection Intrusion detection Data streams distributed system Edge computing Internet of Things

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An Accelerated Approach to parallel Ensemble Techniques Targeting Healthcare and Environmental applications

An Accelerated Approach to Parallel Ensemble Techniques Targ...

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international conference on Energy, Power and Environment: Towards Sustainable Growth (ICEPE)

作者： Tejasri Kari Leelavani N Sayeera Banu A DhanuShree R K.B. Jagannatha Santhi Natarajan BMS Institute of Technology and Management Visvesvaraya Technological University Belgaum Karnataka India

ISBN: (纸本)9781665430869

Ensemble learning techniques adopt comprehensive learning methodologies that produce optimized predictions with reduced variance and bias. The structured Random Forest ensemble learning technique equips a set of weak and diverse decision trees, resulting in an active hybrid learning ensemble. Plagued with high computational complexity, Random Forest Ensemble continues to be the preferred technique when accuracy is of primary importance for learners. Efforts to accelerate the Random Forest Ensembles are in place, however failing to efficiently utilize the data transmission bandwidth between the host and the accelerator hardware. This paper provides an architectural overview of a reconfigurable accelerator based architecture of the Random Forest Ensemble with an efficient data path model for data streaming. The paper also derives the need for an accelerated parallel ensemble method by deriving the results from equivalent sequential software implementations of the algorithm. The validation of the results have been done on healthcare application involving breast cancer classification and environmental applications involving temperature prediction and fuel consumption.

关键词： Medical services Vegetation parallel processing Prediction algorithms Hardware parallel architectures Classification algorithms

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