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Honeyeye: A Network Traffic Collection Framework for distributed ICS Honeynets 18

Honeyeye: A Network Traffic Collection Framework for Distrib...

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18th IEEE Int Symp on parallel and distributed Proc with Applicat (ISPA) / 10th IEEE Int Conf on Big Data and Cloud Comp (BDCloud) / IEEE Int Symp on Social Comp and Networking (SocialCom) / IEEE Int Conf on Sustainable Comp and Commun (SustainCom)

作者： Sheng, Chuan Yao, Yu Li, Dongbiao An, Hongna Yang, Wei Northeastern Univ Sch Comp Sci & Engn Shenyang Peoples R China Northeastern Univ Software Coll Shenyang Peoples R China

ISBN: (纸本)9781665414852

The honeynet, as an important security resource, is increasingly used in the industrial field in order to detect, analyze, and prevent network attacks against industrial control systems (ICSs). However, conventional network traffic collection methods used in honeynets cannot meet the more and more complex requirements of large-scale and distributed honeynets. This paper presents a new network traffic collection framework for distributed ICS honeynets called Honeyeye. Honeyeye can provide some different running modes for different application scenarios and purposes. Honeyeye can not only collect and save network traffic, but also parse it into readable data and convert it into the required format. By this way, Honeyeye tends to provide network administrators and intrusion detection systems (IDSs) with more understandable and directly available data rather than captured opaque binary data. Experimental results show that the framework is effective in parsing, converting, and transmitting ICS honeynet traffic.

关键词： Industrial Control System distributed ICS Honeynet Network Traffic Collection

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Practical distributed Programming in C++ 20

Practical Distributed Programming in C++

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29th international symposium on High-Performance parallel and distributed Computing, HPDC 2020

作者： Drocco, Maurizio Castellana, Vito Giovanni Minutoli, Marco IBM TJ Watson Yorktown HeightsNY United States Pacific Northwest National Laboratory RichlandWA United States

ISBN: (纸本)9781450370523

The need for coupling high performance with productivity is steering the recent evolution of the C++ language where low-level aspects of parallel and distributed computing are now part of the standard or under discussion for inclusion. The Standard Template Library (STL) includes containers and algorithms as primary notions, coupled with execution policies that allow exploiting parallel platforms (e.g., multi-cores) on top of a well-defined operational semantics. In this work, we discuss the design of a stack for STL-compliant containers, iterators, algorithms, and execution policies targeting distributed-memory systems. Finally, we evaluate the proposed approach by analyzing the performance of our proof-of-concept implementation over a set of STL algorithms. © 2020 ACM.

关键词： Containers

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Development and application of face recognition system based on occlusion

Development and application of face recognition system based...

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Information Science, parallel and distributed systems (ISPDS), international Conference on

作者： Jing Luo Guangzhou Nanyang Polytechnic College Guangzhou China

ISBN: (纸本)9781665417396

This paper studies the automatic identification of students in and out of the campus. Due to the outbreak of epidemic disease in the past year, it is necessary to timely confirm the students in the school and conduct access supervision. At the same time, it is necessary to improve the identification efficiency and reduce the workload of the staff. On this basis, the face recognition system under occlusion is studied. Research shows that the occlusion face recognition system has high recognition efficiency and high accuracy. Therefore, the development of face recognition system is reasonable and has strong applicability.

关键词： Information science Epidemics databases Face recognition Software Hardware Diseases

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The MIT Supercloud Workload Classification Challenge

The MIT Supercloud Workload Classification Challenge

引用

IEEE international symposium on parallel and distributed Processing Workshops and Phd Forum (IPDPSW)

作者： Benny J. Tang Qiqi Chen Matthew L. Weiss Nathan C. Frey Joseph McDonald David Bestor Charles Yee William Arcand William Bergeron Chansup Byun Daniel Edelman Michael Houle Matthew Hubbell Michael Jones Jeremy Kepner Anna Klein Adam Michaleas Peter Michaleas Lauren Milechin Julia Mullen Andrew Prout Albert Reuther Antonio Rosa Andrew Bowne Lindsey McEvoy Baolin LI Devesh Tiwari Jiay Gadepally Siddharth Samsi MIT MIT Lincoln Laboratory US Air Force Northeastern University

ISBN: (数字)9781665497473

ISBN: (纸本)9781665497480

High-Performance Computing (HPC) centers and cloud providers support an increasingly diverse set of applications on heterogenous hardware. As Artificial Intelligence (AI) and Machine Learning (ML) workloads have become an increasingly larger share of the compute workloads, new approaches to optimized resource usage, allocation, and deployment of new AI frameworks are needed. By identifying compute workloads and their utilization characteristics, HPC systems may be able to better match available resources with the application demand. By leveraging datacenter instrumentation, it may be possible to develop AI-based approaches that can identify workloads and provide feedback to researchers and datacenter operators for improving operational efficiency. To enable this research, we released the MIT Supercloud Dataset, which provides de-tailed monitoring logs from the MIT Supercloud cluster. This dataset includes CPU and GPU usage by jobs, memory usage, and file system logs. In this paper, we present a workload classification challenge based on this dataset. We introduce a labelled dataset that can be used to develop new approaches to workload classification and present initial results based on existing approaches. The goal of this challenge is to foster algorithmic innovations in the analysis of compute workloads that can achieve higher accuracy than existing methods. Data and code will be made publicly available via the Datacenter Challenge website: https://***.

关键词： Technological innovation distributed processing File systems Instruments High performance computing Graphics processing units Machine learning

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A Methodology to Build Decision Analysis Tools Applied to distributed Reinforcement Learning

A Methodology to Build Decision Analysis Tools Applied to Di...

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IEEE international symposium on parallel and distributed Processing Workshops and Phd Forum (IPDPSW)

作者： Cè dric Prigent Loï c Cudennec Alexandru Costan Gabriel Antoniu University of Rennes Inria CNRS IRISA Rennes France DGA Maitrise de l&#x0027 Information Rennes France

ISBN: (数字)9781665497473

ISBN: (纸本)9781665497480

As Artificial Intelligence-based applications become more and more complex, speeding up the learning phase (which is typically computation-intensive) becomes more and more necessary. distributed machine learning (ML) appears adequate to address this problem. Unfortunately, ML also brings new development frameworks, methodologies and high-level program-ming languages that do not fit to the regular high-performance computing design flow. This paper introduces a methodology to build a decision making tool that allows ML experts to arbitrate between different frameworks and deployment configurations, in order to fulfill project objectives such as the accuracy of the resulting model, the computing speed or the energy consumption of the learning computation. The proposed methodology is applied to an industrial-grade case study in which reinforcement learning is used to train an autonomous steering model for a cargo airdrop system. Results are presented within a Pareto front that lets ML experts choose an appropriate solution, a framework and a deployment configuration, based on the current operational situation. While the proposed approach can effortlessly be applied to other machine learning problems, as for many decision making systems, the selected solutions involve a trade-off between several antagonist evaluation criteria and require experts from different domains to pick the most efficient solution from the short list. Nevertheless, this methodology speeds up the development process by clearly discarding, or, on the contrary, including combinations of frameworks and configurations, which has a significant impact for time and budget-constrained projects.

关键词： Energy consumption Runtime Computational modeling Atmospheric modeling High performance computing Reinforcement learning Learning (artificial intelligence)

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A parallel Novelty Search Metaheuristic Applied to a Wildfire Prediction System

A Parallel Novelty Search Metaheuristic Applied to a Wildfir...

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IEEE international symposium on parallel and distributed Processing Workshops and Phd Forum (IPDPSW)

作者： Jan Strappa Paola Caymes-Scutari Germá n Bianchini Facultad Regional Mendoza Consejo Nacional de Investigaciones Cient&#x00ED ficas y T&#x00E9 cnicas (CONICET) Mendoza Universidad Tecnol&#x00F3 gica Nacional Argentina cnicas (CONICET) Universidad Tecnol&#x00F3 gica Nacional Mendoza Argentina Facultad Regional Mendoza Universidad Tecnol&#x00F3

ISBN: (数字)9781665497473

ISBN: (纸本)9781665497480

Wildfires are a highly prevalent multi-causal environmental phenomenon. The impact of this phenomenon includes human losses, environmental damage and high economic costs. To mitigate these effects, several computer simulation systems have been developed in order to predict fire behavior based on a set of input parameters, also called a scenario (wind speed and direction; temperature; etc.). However, the results of a simulation usually have a high degree of error due to the uncertainty in the values of some variables, because they are not known, or because their measurement may be imprecise, erroneous, or impossible to perform in real time. Previous works have proposed the combination of multiple results in order to reduce this uncertainty. State-of-the-art methods are based on parallel optimization strategies that use a fitness function to guide the search among all possible scenarios. Although these methods have shown improvements in the quality of predictions, they have some limitations related to the algorithms used for the selection of scenarios. To overcome these limitations, in this work we propose to apply the Novelty Search paradigm, which replaces the objective function by a measure of the novelty of the solutions found, which allows the search to continuously generate solutions with behaviors that differ from one another. This approach avoids local optima and may be able to find useful solutions that would be difficult or impossible to find by other algorithms. As with existing methods, this proposal may also be adapted to other propagation models (floods, avalanches or landslides).

关键词： Temperature measurement Uncertainty Wind speed Fires Prediction algorithms Search problems Time measurement

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Teaching High-Performance Computing in Developing Countries: A Case Study in Mexican Universities

Teaching High-Performance Computing in Developing Countries:...

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IEEE international symposium on parallel and distributed Processing Workshops and Phd Forum (IPDPSW)

作者： Joel Antonio Trejo-Sá nchez Francisco Javier Herná ndez-Ló pez Miguel Á ngel Uh Zapata José Luis Ló pez-Martí nez Daniel Fajardo-Delgado Julio Cé sar Ramí rez-Pacheco Department of Computer Science CONACYT-Centro de Investigaci&#x00F3 n en Matemdtic&#x00E1 s A.C. M&#x00E9 rida M&#x00E9 xico Department of Computer Science Universidad Auton&#x00F3 ma de Yucat&#x00E1 n M&#x00E9 Department of Systems and Computation Tecnol&#x00F3 gico Nacional de M&#x00E9 xico Ciudad Guzm&#x00E1 Department of Engineering Universidad de Quintana Roo Can&#x00FA

ISBN: (数字)9781665497473

ISBN: (纸本)9781665497480

Teaching High-Performance Computing (HPC) to undergraduate programs represents a significant challenge in most universities in developing countries like Mexico. Deficien-cies in the required infrastructure and equipment, inadequate curricula in computer engineering programs (and resistance to change them), students' lack of interest, motivation, or knowledge of this area are the main difficulties to overcome. The COVID-19 pandemic represents an additional challenge to these difficulties in teaching HPC in these programs. Despite the detriments, some strategies have been developed to incorporate the HPC concepts to Mexican students without necessarily modifying the traditional curricula. This paper presents a case study over four public universities in Mexico based on our experience as instructors. We also propose a course that introduces the HPC principles considering the heterogeneous background of the students in such universities. The results are about the number of students enrolling in related classes and participating in extra-curricular projects.

关键词： Resistance Knowledge engineering COVID-19 distributed processing Pandemics High performance computing Conferences

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A combined fast/cycle accurate simulation tool for reconfigurable accelerator evaluation: application to distributed data management 31

A combined fast/cycle accurate simulation tool for reconfigu...

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31st international Workshop on Rapid System Prototyping (RSP) - Shortening the Path from Specification to Prototype

作者： Lenormand, Erwan Goubier, Thierry Cudennec, Loic Charles, Henri-Pierre Univ Paris Saclay Inst LIST CEA PC 172 F-91191 Gif Sur Yvette France DGA MI Dept Artificial Intelligence BP 7 F-35998 Rennes Armees France Univ Grenoble Alpes CEA Inst LIST F-38000 Grenoble France

ISBN: (纸本)9781728184661

parallel computing systems based on reconfigurable accelerators are becoming (1) increasingly heterogeneous, (2) difficult to design and (3) complex to model. Such modeling of a parallel computing system helps to evaluate its performance and to improve its architecture before prototyping. This paper presents a simulation tool aiming to study the integration of reconfigurable accelerators in scalable distributed systems and runtimes, such as S-DSM systems, where S-DSM (software-distributed shared memory) is a paradigm to ease data management among distributed nodes. This tool allows us to simulate the execution of irregular compute kernels accessing distributed data. To deal with the complexity of modeling (3) the complete system we used a hybrid methodology. We integrated the simulation engine into the S-DSM. The distributed data management part is executed on the physical architecture allowing to generate precise and faithful latencies, and the accelerator simulation is cycle accurate. We used general sparse matrix-matrix multiplication (SpGEMM) as a case study. We show that the use of this tool makes it possible to analyze the behavior of an heterogeneous system (1) with rapid prototyping and simulation. The analysis of the results allowed to determine the correct sizing of the architecture (2) to obtain the best performance. The tool allowed to identify the bottleneck of our architecture and confirmed the possibility of hiding data access latencies. Our simulation platform allows to emulate a heterogeneous distributed system by introducing a slowdown between 1.2 and 3.7 times compared to the compute kernel simulation alone.

关键词： Hybrid simulation distributed systems Reconfigurable Computing

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The Research Progress of Enterprise Knowledge Recommendation Method

The Research Progress of Enterprise Knowledge Recommendation...

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Information Science, parallel and distributed systems (ISPDS), international Conference on

作者： Wenjing Guo Qinghua Zhang Gaofei Wang Heilongjiang University of Science and Technology Heilongjiang China Guizhou University of Commerce Guizhou China

ISBN: (纸本)9781665417396

Enterprise knowledge recommendation is an efficient recommendation method, which can effectively improve the phenomenon of knowledge tragedy and knowledge flooding in the process of enterprise knowledge management. Based on the WoS database, this paper uses CiteSpace software and bibliometric methods to systematically analyze the development trend of corporate knowledge recommendation methods, publishing organizations, publishing journals, citation frequency, and theme evolution. The results show that the research of enterprise knowledge recommendation method has already formed a system, and in the future, it will consider combining with more fields to promote the development of knowledge recommendation.

关键词： Information science databases Bibliometrics Organizations Market research Software Knowledge management

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Construction of Hierarchical Matrix on distributed Memory systems using a Task parallel Language

Construction of Hierarchical Matrix on Distributed Memory Sy...

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international symposium on Computing and Networking Workshops (CANDARW)

作者： Zhengyang Bai Tasuku Hiraishi Akihiro Ida Masahiro Yasugi Keiichiro Fukazawa Gradudate School of Informatics Kyoto University Kyoto Japan Department of Information and Computer Science Kyoto Tachibana University Kyoto Japan Research Institute for Value-Added-Information Generation Japan Agency for Marine-Earth Science and Technology Yokohama Japan Department of Computer Science and Networks Kyushu Institute of Technology Iizuka Japan Academic Center for Computing and Media Studies Kyoto University Kyoto Japan

A hierarchical matrix is an approximated form of the dense matrix that represents the N-by-N correlations of N objects. The construction of the hierarchical matrix is achieved by dividing a matrix into submatrices (partitioning), followed by calculating these submatrices' entries (filling). The matrix partitioning is divided into two sub-steps: cluster tree (CT) construction by dividing objects into clusters, hierarchically, and block cluster tree (BCT) construction by observing all cluster pairs at the same level of the cluster tree that satisfy a given admissibility condition. We can apply task parallelism to filling by treating each submatrix as a parallelization unit. In existing implementations, the partitioning is redundantly executed on every computing node, and filling is executed in parallel by assigning a set of tasks to each worker statically. However, we cannot get good load balance using such implementations because it is difficult to predict the workload of each task precisely. In this paper, we propose a new parallel implementation of the hierarchical matrix construction, where a BCT construction is executed in parallel using all workers of all computing nodes. When a worker finds a BCT leaf during BCT construction, it executes filling for the submatrix corresponding to the leaf. To get good load balance, we parallelized the BCT construction using the task parallel language Tascell, which enables easy and efficient parallelization of tree recursive algorithms by employing a dynamic load balancing strategy. In numerical experiments with 3D electric field analyses using up to 16 computing nodes each of which has 36 cores, our implementation achieved up to 1.9-fold speedups compared to an implementation using the static task assignment strategy.

关键词： parallel languages Three-dimensional displays Correlation Heuristic algorithms Conferences parallel processing Load management

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