Distributed Computing
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2021 ◽  
Vol 11 (1) ◽  
pp. 8
Alexander Feoktistov ◽  
Sergey Gorsky ◽  
Roman Kostromin ◽  
Roman Fedorov ◽  
Igor Bychkov

Nowadays, developing and applying advanced digital technologies for monitoring protected natural territories are critical problems. Collecting, digitalizing, storing, and analyzing spatiotemporal data on various aspects of the life cycle of such territories play a significant role in monitoring. Often, data processing requires the utilization of high-performance computing. To this end, the paper addresses a new approach to automation of implementing resource-intensive computational operations of web processing services in a heterogeneous distributed computing environment. To implement such an operation, we develop a workflow-based scientific application executed under the control of a multi-agent system. Agents represent heterogeneous resources of the environment and distribute the computational load among themselves. Software development is realized in the Orlando Tools framework, which we apply to creating and operating problem-oriented applications. The advantages of the proposed approach are in integrating geographic information services and high-performance computing tools, as well as in increasing computation speedup, balancing computational load, and improving the efficiency of resource use in the heterogeneous distributed computing environment. These advantages are shown in analyzing multidimensional time series.

A. Anusha ◽  
Dr. K. Kishore Raju ◽  

Due to the emergence of a new infectious disease (COVID-19), the worldwide data volume has been quickly increasing at a very high rate during the last two years. Due its infectious, and importance, in this paper, K-Means clustering procedure is applied on COVID data in MapReduce based distributed computing environment. The proposed system is store, process and tests the large volume of COVID-19 data. Experimental results had been proved that this process is adaptable to COVID-19 data in the formation of trusted clusters.

Oyekanmi Ezekiel Olufunminiyi ◽  
Oladoja Ilobekemen Perpetual ◽  
Omotehinwa Temidayo Oluwatosin

Cloud is specifically known to have difficulty in managing resource usage during task scheduling, this is an innate from distributed computing and virtualization. The common issue in cloud is load balancing management. This issue is more prominent in virtualization technology and it affects cloud providers in term of resource utilization and cost and to the users in term of Quality of Service (QoS). Efficient procedures are therefore necessary to achieve maximum resource utilization at a minimized cost. This study implemented a load balancing scheme called Improved Resource Aware Scheduling Algorithm (I-RASA) for resource provisioning to cloud users on a pay-as-you-go basis using CloudSim 3.0.3 package tool. I-RASA was compared with recent load balancing algorithms and the result shown in performance evaluation section of this paper is better than Max-min and RASA load balancing techniques. However, it sometimes outperforms or on equal balance with Improved Max-Min load balancing technique when using makespan, flow time, throughput, and resource utilization as the performance metrics.

Hongbin Zhuang ◽  
Wenzhong Guo ◽  
Xiaoyan Li ◽  
Ximeng Liu ◽  
Cheng-Kuan Lin

The processor failures in a multiprocessor system have a negative impact on its distributed computing efficiency. Because of the rapid expansion of multiprocessor systems, the importance of fault diagnosis is becoming increasingly prominent. The [Formula: see text]-component diagnosability of [Formula: see text], denoted by [Formula: see text], is the maximum number of nodes of the faulty set [Formula: see text] that is correctly identified in a system, and the number of components in [Formula: see text] is at least [Formula: see text]. In this paper, we determine the [Formula: see text]-component diagnosability of general networks under the PMC model and MM[Formula: see text] model. As applications, the component diagnosability is explored for some well-known networks, including complete cubic networks, hierarchical cubic networks, generalized exchanged hypercubes, dual-cube-like networks, hierarchical hypercubes, Cayley graphs generated by transposition trees (except star graphs), and DQcube as well. Furthermore, we provide some comparison results between the component diagnosability and other fault diagnosabilities.

П.В. Полухин

В работе предложены математические инструменты на основе достаточных статистик и декомпозиции выборок в сочетании с алгоритмами распределенных вычислений, позволяющие существенно повысить эффективность процедуры фильтрации. Filtering algorithms are used to assess the state of dynamic systems when solving various practical problems, such as voice synthesis and determining the geo-position and monitoring the movement of objects. In the case of complex hierarchical dynamic systems with a large number of time slices, the process of calculating probabilistic characteristics becomes very time-consuming due to the need to generate a large number of samples. The essence of optimization is to reduce the number of samples generated by the filter, increase their consistency and speed up computational operations. The paper offers mathematical tools based on sufficient statistics and sample decomposition in combination with distributed computing algorithms that can significantly improve the efficiency of the filtering procedure.

2022 ◽  
Vol 97 ◽  
pp. 107639
Tiantian Ding ◽  
Wenzhong Yang ◽  
Fuyuan Wei ◽  
Chao Ding ◽  
Peng Kang ◽  

2021 ◽  
Kamila Kolpashnikova

A brief tutorial on how to run optimal matching in Julia. The performance gains: it is twice faster than TraMineR on a dataset of about 20000 sequences with 96 steps each.

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