Machine Translation System as Virtual Appliance: For Scalable Service Deployment on Cloud

Machine Translation (MT) system is a complex natural language processing (NLP) system composed of large number of heterogeneous modules. Deployment of such a complex system even on a stand alone system is a cumbersome, knowledge intensive and time consuming task, taking hours to load, configure and run the system. As an MT system goes through frequent and regular updates, mainly to improve its accuracy and performance, the cumbersome task of its deployment is required to be repeated on release of each new version. Further, when such a system is needed to be deployed on a cloud infrastructure, mainly to facilitate auto-scaling of computational resources for varying load conditions, the task of deployment gets even more complicated and more time consuming. This paper proposes that every software version of a complex NLP application like MT system should be built and released as a virtual appliance that can be deployed with a very little setup time and with ease even by a common user. It discusses the experiments performed to build the MT system into a virtual appliance, for stand alone system deployment as well as for cloud deployment, and reports the deployment time measurements in both the scenario. Deployment of the virtual MT appliance took 130 seconds in stand alone system; its deployment on a large number of virtual machines in the cloud environment took 150 seconds on an average, in contrast to several hours taken for the deployment of MT applications earlier.