Virtualizing a Cluster to Optimize the Problems of High Scientific Complexity within an Organization

Abstract

The Image Processing Research Laboratory (INTI-Lab) of the Universidad de Ciencias y Humanidades has several research projects related to computer science needing high computational resources. Some of these projects are associated with climate prediction, molecule modeling, physical simulations, and others these applications generate a significant amount of data, regarding the big data issue, despite having excellent hardware features, the final result is obtained after hours or days of calculation depending on the algorithm complexity. For this reason, it is not possible to present optimal solutions at an ideal time. .In this work, we propose the virtualization and configuration of a high-performance cluster (HPC) known commercially as a "supercomputer" that is composed of several computers connected to a high-speed network to behave like a single computer. The virtualization is used to run a scientific algorithm that will apply performance tests using four virtual computers to demonstrate that the reduction of time is achieved by using more machines and thus be able to be implemented in the laboratories of the institution.

Authors and Affiliations

Enrique Lee HuamanĂ­, Patricia Condori, Avid Roman-Gonzalez

Keywords

Related Articles

Towards Understanding Internet of Things Security and its Empirical Vulnerabilities: A Survey

The Internet of things is no longer a concept; it is a reality already changing our lives. It aims to interconnect almost all daily used devices to help them exchange contextualized data in order to offer services adequa...

Estimation of Water Quality Parameters Using the Regression Model with Fuzzy K-Means Clustering

The traditional methods in remote sensing used for monitoring and estimating pollutants are generally relied on the spectral response or scattering reflected from water. In this work, a new method has been proposed to fi...

Deep Gated Recurrent and Convolutional Network Hybrid Model for Univariate Time Series Classification

Hybrid LSTM-fully convolutional networks (LSTM-FCN) for time series classification have produced state-of-the-art classification results on univariate time series. We empirically show that replacing the LSTM with a gated...

Optimizing Power-Based Indoor Tracking System for Wireless Sensor Networks using ZigBee

Evolution of wireless and digital communication gives birth to the smaller but powerful battery-equipped devices which are easy to maintain and perform the desired tasks. ZigBee is a Wireless Personal Area Network (WPAN)...

Web Assessment of Libyan Government e-Government Services

Libya has started transferring traditional govern-ment services into e-government services. The e-government initiative involves the use of websites to offer various services such as civil registration, financial transac...

Download PDF file
  • EP ID EP597499
  • DOI 10.14569/IJACSA.2019.0100679
  • Views 85
  • Downloads 0

How To Cite

Enrique Lee HuamanĂ­, Patricia Condori, Avid Roman-Gonzalez (2019). Virtualizing a Cluster to Optimize the Problems of High Scientific Complexity within an Organization. International Journal of Advanced Computer Science & Applications, 10(6), 618-622. https://europub.co.uk/articles/-A-597499