A forefront to machine translation technology: deployment on the cloud as a service to enhance QoS parameters

Muskaan Singh, Ravinder Kumar, Inderveer Chana

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.