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International Journal of Advanced Computer Science and Applications(IJACSA), Volume 8 Issue 8, 2017.
Abstract: This paper aims to propose a new massively distributed virtual machine with scalable and efficient parallel computing models for High Performance Computing (HPC). The message passing paradigm of the Processing Units has a significant impact on HPC with high communication cost that penalizes the performance of these models. Accordingly, the proposed micro-services model allows the HPC applications to enhance the processing power with low communication cost. Thus, the model based Micro-services Virtual Processing Units (MsVPUs) cooperate using asynchronous communication mechanism through the Advanced Message Queuing Protocol (AMQP) protocol in order to maintain the scalability of the Single Program Multiple Data (SPMD) applications. Additionally, this mechanism enhances also the efficiency of the model based load balancing service with time optimized load balancing strategy. The proposed virtual machine is tested and validated through an application of fine grained parallel programs for big data classification. Experimental results present reduced execution time compared to the virtual machine based mobile agent’s model.
Fatéma Zahra Benchara, Mohamed Youssfi, Omar Bouattane, Ouafae Serrar and Hassan Ouajji, “Toward a New Massively Distributed Virtual Machine based Cloud Micro-Services Team Model for HPC: SPMD Applications” International Journal of Advanced Computer Science and Applications(IJACSA), 8(8), 2017. http://dx.doi.org/10.14569/IJACSA.2017.080831
@article{Benchara2017,
title = {Toward a New Massively Distributed Virtual Machine based Cloud Micro-Services Team Model for HPC: SPMD Applications},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2017.080831},
url = {http://dx.doi.org/10.14569/IJACSA.2017.080831},
year = {2017},
publisher = {The Science and Information Organization},
volume = {8},
number = {8},
author = {Fatéma Zahra Benchara and Mohamed Youssfi and Omar Bouattane and Ouafae Serrar and Hassan Ouajji}
}
Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.