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IEEE Workshop on Advanced Research and Technology in Industry Applications (WARTIA)

作者： Zhao, Yinghui Yue, Xijuan Han, Chunming Chinese Acad Sci Inst Remote Sensing & Digital Earth Beijing 100094 Peoples R China

ISBN: (纸本)9781479969890

This paper proposes a working framework for SAR data parallel processing using computing cluster, which includes a master node and a number of computing nodes. This framework supports coarse-grained data processing and the master node controls the whole workflow and the data distribution strategy, by which the coarse-grained data are distributed to the computing nodes in order and the computing nodes are responsible for data processing. The experiment of the real data processing demonstrates that the efficiency of the computing cluster can be improved significantly under this framework.

关键词： SAR data processing computing cluster data distribution strategy coarse-grained

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Work in Progress: computing cluster using IoT Technologies

Work in Progress: Computing Cluster using IoT Technologies

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IEEE Global Engineering Education Conference (EDUCON) - Emerging Trends and Challenges of Engineering Education

作者： Madritsch, Christian Klinger, Thomas CUAS Villach Austria

ISBN: (纸本)9781538629574

This paper describes the design, implementation, and practical use in Engineering Education of a computing cluster using IoT Technologies. First, the development goal and its expected application is explained. Current lectures in Computer Science, Computer Engineering and Image Processing will benefit of the massive parallel computing cluster. Next, the design and implementation - carried out by a group of students are described in detail. Finally, the practical use of the cluster including a test application is shown. Here, current super computing methods like Message Passing Interface (MPI) and Open Multi Processing (OpenMP) are used to show the ability and functionality go the cluster.

关键词： computing cluster IoT Supercomputing OpenMP MPI

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Analysis of an Efficiency of Parallelization of Algorithms Running on computing cluster Based on Single-Board Diskless Computers Raspberry PI 3 Model B

Analysis of an Efficiency of Parallelization of Algorithms R...

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IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (ElConRus)

作者： Vavrenyuk, Aleksandr B. Matveeva, Darya, V Lukyantsev, Nikita M. Makarov, Victor V. Natl Res Nucl Univ MEPhI Moscow Engn Phys Inst Dept Comp Syst & Technol Moscow Russia

ISBN: (纸本)9781665404761

This article is devoted to the analysis of parallelization efficiency of algorithms for solving linear systems of equations. The article presents an assessment of the quality of optimization of two algorithms for distributing the computational load between cluster nodes. The hardware is a computing cluster based on single-board computers with Linux Debian 8 as an operation system. Hydra Process Manager with an Slurm resource manager that implements a queue for multi-user access to the system is used to run and debug parallel tasks. The resulting system is planned to be used in the future in the educational process of MEPhI (Moscow Engineering Physics Institute) for a laboratory workshop on parallel computing.

关键词： Parallel algorithms computing cluster Single-board computer

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Teaching Big Data and Cloud computing with a Physical cluster 17

Teaching Big Data and Cloud Computing with a Physical Cluste...

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ACM SIGCSE Technical Symposium on Computer Science Education (SIGCSE)

作者： Eickholt, Jesse Shrestha, Sharad Cent Michigan Univ Dept Comp Sci Mt Pleasant MI 48859 USA Cent Michigan Univ Dept Comp Sci Mt Pleasant MI 48859 USA

ISBN: (纸本)9781450346986

Cloud computing and Big Data continue to be disruptive forces in computing and have made inroads in the Computer Science curriculum, with courses in Cloud computing and Big Data being routinely offered at the graduate and undergraduate level. One major challenge in offering courses in Big Data and Cloud computing is resources. The question is how to provide students with authentic experiences making use of current Cloud and Big Data resources and tools and do so in a cost effective manner. Historically, three options, namely physical clusters, virtual clusters and cloud-based clusters, have been used to support Big Data and Cloud computing courses. Virtual clusters and cloudbased options are those that institutions have typically adopted and many arguments in favor of these options exist in the literature, citing cost and performance. Here we argue that teaching Big Data and Cloud computing courses can be done making use of a physical cluster and that many of the existing arguments fail to take into account many important factors in their calculations. These factors include the flexibility and control of a physical cluster in responding to changes in industry, the ability to work with much larger datasets, and the synergy and broad applicability of an appropriately equipped physical cluster for courses such as Cloud computing, Big Data and Data Mining. We present three possible configurations of a physical cluster which span the spectrum in terms of cost and provide cost comparisons of these configurations against virtual and cloud-based options, taking into account the unique requirements of an academic setting. While limitations do exist with a physical cluster and it is not an option for all situations, our analysis and experience indicates that there is great value in using a physical cluster to support teaching Cloud computing and Big Data courses and it should not be dismissed.

关键词： Cloud computing Big Data computing cluster

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Design and implementation of a scalable high-performance computing (HPC) cluster for omics data analysis: achievements, challenges and recommendations in LMICs

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GIGASCIENCE 2024年第13期13卷 giae060页

作者： Ghedira, Kais Khamessi, Oussema Hkimi, Chaima Kamoun, Selim Dhamer, Nader Daassi, Kamel Ben Salah, Wassim Othman, Houcemeddine Belhadj, Wahbi Ghorbal, Youssef Univ Tunis El Manar Inst Pasteur Tunis Lab Bioinformat Biomath & Biostat LR16IPT09 Tunis 1002 Tunisia Univ Manouba High Inst Biotechnol Sidi Thabet Ariana BP-66 Manouba 2010 Tunisia Univ Tunis El Manar Inst Pasteur Tunis Inst Pasteur Tunis Direct Informat Tunis 1002 Tunisia Univ Witwatersrand Sydney Brenner Inst Mol Biosci Fac Hlth Sci ZA-2193 Johannesburg South Africa European Bioinformat Inst European Bioinformat Inst EMBL EBI Hinxton CB10 1SD England Inst Pasteur Paris HPC Core Facil 25-28 Rue Dr Roux F-75015 Paris France

Background The advent of high-throughput technologies, including cutting-edge sequencing devices, has revolutionized biomedical data generation and processing. Nevertheless, big data applications require novel hardware and software for parallel computing and management to handle the ever-growing data size and analysis complexity. On-premise, high-performance computing (HPC) is increasingly used in biomedical research for big data stewardship. Findings In this work, we present Tunisia's first high-performance computational infrastructure for omics research. Method We highlight measurements and recommendations that may help institutions in other low- and middle-income countries that are eager to implement local HPC in facilities for bioinformatics research and omics data analyses.

关键词： bioinformatics HPC Tunisia infrastructure computational power computing cluster

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Towards Long-View computing Load Balancing in cluster Storage Systems

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IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS 2017年第6期28卷 1770-1784页

作者： Liu, Guoxin Shen, Haiying Wang, Haoyu Clemson Univ Dept ECE Clemson SC 29634 USA Univ Virginia Dept Comp Sci Charlottesville VA 22904 USA

In large-scale computing clusters, when the server storing a task's requested data does not have sufficient computing capacity for the task, current job schedulers either schedule the task to the closest server and transmit to it the requested data, or let the task wait until the server has sufficient computing capacity. The former solution generates network load while the latter solution increases task delay. To handle this problem, load balancing methods are needed to reduce the number of overloaded servers due to computing workloads. However, current load balancing methods do not aim to balance the computing load for the long term. Through trace analysis, we demonstrate the diversity of computing workloads of different tasks and the necessity of balancing the computing workloads among servers. Then, we propose a cost-efficient computing load Aware and Long-View load balancing approach (CALV). CALV is novel in that it achieves long-term computing load balance by migrating out an overloaded server data blocks contributing more computing workloads when the server is more overloaded and contribute less computing workloads when the server is more underloaded at different epochs during a time period. Based upon the task schedules, we further propose a task reassignment algorithm that reassigns tasks from an overloaded server to other data servers of the tasks to make it non-overloaded before CALV is conducted. The above methods are for the tasks whose submission times and execution latencies can be predicted. To handle unexpected tasks or insufficiently accurate predictions, we propose a dynamic load balancing method, in which an overloaded server dynamically redirects tasks to other data servers of the tasks, or replicates the tasks' requested data to other servers and redirects the tasks to those servers in order to become non-overloaded. Finally, we propose a proximity-aware tree based distributed load balancing method to reduce the reallocation cost and improve the

关键词： computing cluster data allocation load balancing data locality

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computing Load Aware and Long-View Load Balancing for cluster Storage Systems 3

Computing Load Aware and Long-View Load Balancing for Cluste...

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IEEE International Conference on Big Data

作者： Liu, Guoxin Shen, Haiying Wang, Haoyu Clemson Univ Dept Elect & Comp Engn Clemson SC 29631 USA

ISBN: (纸本)9781479999255

In large-scale computing clusters, when the server storing a task's input data does not have sufficient computing capacity, current job schedulers either schedule the task and transmit the input data to the closest server or let the task wait until the server has sufficient computing capacity, which generates network load or task delay. To handle this problem, load balancing methods are needed to reduce the number of overloaded servers due to computing workloads. However, current load balancing methods either do not consider the computing workload or assume that it is proportional to the number of data blocks in a server. Through trace analysis, we demonstrate the diversity of computing workloads of different tasks and the necessity of balancing the computing workloads among servers. Then, we propose a cost-efficient computing load Aware and Long-View load balancing approach (CALV). In addition to the computing load awareness, CALV is also novel in that it achieves long-term load balance by migrating out data blocks from an overloaded server that contribute more computing workloads when the server is more overloaded and contribute less computing workloads when the server is more underloaded at different epochs during a time period. CALV also has a lazy data block transmission method to improve the load balanced state and avoid network load peak. Trace-driven experiments in simulation and a real computing cluster show that CALV outperforms other methods in terms of balancing the computing workloads and cost efficiency.

关键词： computing cluster Data allocation Load balancing Data locality

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Multicloud Deployment of computing clusters for Loosely Coupled MTC Applications

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IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS 2011年第6期22卷 924-930页

作者： Moreno-Vozmediano, Rafael Montero, Ruben S. Llorente, Ignacio M. Univ Complutense Madrid Dept Arquitectura Comp & Automat Fac Informat E-28040 Madrid Spain

Cloud computing is gaining acceptance in many IT organizations, as an elastic, flexible, and variable-cost way to deploy their service platforms using outsourced resources. Unlike traditional utilities where a single provider scheme is a common practice, the ubiquitous access to cloud resources easily enables the simultaneous use of different clouds. In this paper, we explore this scenario to deploy a computing cluster on the top of a multicloud infrastructure, for solving loosely coupled Many-Task computing (MTC) applications. In this way, the cluster nodes can be provisioned with resources from different clouds to improve the cost effectiveness of the deployment, or to implement high-availability strategies. We prove the viability of this kind of solutions by evaluating the scalability, performance, and cost of different configurations of a Sun Grid Engine cluster, deployed on a multicloud infrastructure spanning a local data center and three different cloud sites: Amazon EC2 Europe, Amazon EC2 US, and ElasticHosts. Although the testbed deployed in this work is limited to a reduced number of computing resources (due to hardware and budget limitations), we have complemented our analysis with a simulated infrastructure model, which includes a larger number of resources, and runs larger problem sizes. Data obtained by simulation show that performance and cost results can be extrapolated to large-scale problems and cluster infrastructures.

关键词： Cloud computing computing cluster multicloud infrastructure loosely coupled applications

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An Online Visualization System for Streaming Log Data of computing clusters

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Tsinghua Science and Technology 2013年第2期18卷 196-205页

作者： Jing Xia Feiran Wu Fangzhou Guo Cong Xie Zhen Liu Wei Chen State Key Laboratory of CAD&CG Zhejiang University College of Computer Science and Technology Hangzhou Dianzi University

Monitoring a computing cluster requires collecting and understanding log data generated at the core, computer, and cluster levels at run time. Visualizing the log data of a computing cluster is a challenging problem due to the complexity of the underlying dataset： it is streaming, hierarchical, heterogeneous, and multi-sourced. This paper presents an integrated visualization system that employs a two-stage streaming process mode. Prior to the visual display of the multi-sourced information, the data generated from the clusters is gathered, cleaned, and modeled within a data processor. The visualization supported by a visual computing processor consists of a set of multivariate and time variant visualization techniques, including time sequence chart, treemap, and parallel coordinates. Novel techniques to illustrate the time tendency and abnormal status are also introduced. We demonstrate the effectiveness and scalability of the proposed system framework on a commodity cloud-computing platform.

关键词： computing cluster performance metrics monitoring streaming data visualization

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Control of Set of System Parameter Values by the Ant Colony Method

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AUTOMATION AND REMOTE CONTROL 2023年第8期84卷 893-903页

作者： Sinitsyn, I. N. Titov, Yu. P. Natl Res Univ Moscow Aviat Inst Moscow Russia Russian Acad Sci Fed Res Ctr Comp Sci & Control Moscow Russia

The paper considers the modification and application of the ant colony method for the problem of directed enumeration of the values of system parameters when performing calculated multiple calculations. Interaction with the user makes it possible to stop the process of exhaustive enumeration of sets of parameter values, and the application of a modification of the ant colony method will allow us to consider rational sets at early iterations. If the user does not terminate the algorithm, then the proposed modifications allow one to enumerate all solutions using the ant colony method. To modify the ant colony method, a new probabilistic formula and various algorithms of the ant colony method are proposed, allowing for each agent to find a new set of parameter values. The optimal algorithm, according to the research results, is the use of repeated endless cyclic search for a new solution. This modification allows you to consider all solutions, and at the same time, find all the optimal solutions among the first 5% of the considered solutions.

关键词： ant colony method parametric graph reordering computing cluster hyperparameter optimization

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