A forefront to machine translation technology: deployment on the cloud as a service to enhance QoS parameters
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METHODOLOGIES AND APPLICATION
A forefront to machine translation technology: deployment on the cloud as a service to enhance QoS parameters Muskaan Singh1 · Ravinder Kumar2 · Inderveer Chana2
© Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract Machine translation system (MTS) constitutes of functionally heterogeneous modules for processing source language to a given target language. Deploying such an application on a stand-alone system requires much time, knowledge and complications. It even becomes more challenging for a common user to utilize such a complex application. This paper presents a MTS that has been developed using a combination of linguistic rich, rule-based and prominent neural-based approach. The proposed MTS is deployed on the cloud to offer translation as a cloud service and improve the quality of service (QoS) from a stand-alone system. It is developed on TensorFlow and deployed under the cluster of virtual machines in the Amazon web server (EC2). The significance of this paper is to demonstrate management of recurrent changes in term of corpus, domain, algorithm and rules. Further, the paper also compares the MTS as deployed on stand-alone machine and on cloud for different QoS parameters like response time, server load, CPU utilization and throughput. The experimental results assert that in the translation task, with the availability of elastic computing resources in the cloud environment, the job completion time irrespective of its size can be assured to be within a fixed time limit with high accuracy. Keywords Machine translation system · Amazon web server · Elastic computing unit · Quality of service
1 Introduction Soft computing (SC), introduced by Yager et al. (1994), is endeavoured to provide the imprecision of the real world (Zadeh 1996). It provides a reliable solution for complex problems such as estimation with adequate precision (Naderpour and Mirrashid 2020b). It comprises of three models fuzzy systems, artificial neural network (ANN) and optimization algorithm. The ANN model computations are inspired by the biological neural network of the brain. It provides approximate solutions for a nonlinear function as it comprises of several neurons as nonlinear components (Siddique and Adeli 2013). It has applications in various diverse areas such as control (Kim et al. 2019), classification (Naderpour Communicated by V. Loia.
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Muskaan Singh [email protected]
1
Language Technology and Machine Learning Research Lab, CSED, Thapar Institute of Engineering and Technology, Patiala, Punjab, India
2
CSED, Thapar Institute of Engineering and Technology, Patiala, Punjab, India
and Mirrashid 2019), biology (Dunn et al. 2019), construction (Naderpour et al. 2018), secure data aggregation and efficient data processing in the large-scale wireless sensor network (Shobana et al. 2020), remote sensing image classification of natural terrain features (Kundra and Sadawarti 2015), electric load forecasting (Zhang and Hong 2019; Dong et al. 2018) and forecasting the motion of
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