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An Approach to Assure QoS of Machine Translation System on CloudAuthors: Pawan Kumar,Rashid Ahmad,Chaudhary B. D.,Mukul K Sinha Conference: The Fourth International Conference on Cloud Computing, GRIDs, and Virtualization (CLOUD COMPUTING 2013 2013) Date: 2013-05-27 Report no: IIIT/TR/2013/28 AbstractTransfer based Machine Translation (MT) System is a large complex functional application. When these MT systems are deployed with increasing translation load the Quality of Service (QoS) degrades (namely, job completion time increases, system throughput decreases, and system performance does not scale with increase in provision of resources). To improve QoS of the MT system MapReduce framework for distributed processing was explored. MT application, which has very large code size (order of 100 MB) of computation, transferring it across the data nodes of the cluster would be totally antithetical to the basic goal of throughput enhancement. To utilize the benefit of parallelism provided by Hadoop, a very large complex MT application has adopted a distinct approach to overcome this difficulty with no time penalty. This paper presents an engineering approach to delude MapReduce framework for parallelization of machine translation tasks on a large cluster of machines to assure QoS of MT system. This paper reports the initial results of the experiments done in our laboratory by running MT System under cluster of virtual machines in private cloud. Further this paper asserts that, with the availability of elastic computing resources in cloud environment, the job completion time for any translation, irrespective of its size, can be assured to be within a fixed time limit. Full paper: pdf Centre for Language Technologies Research Centre |
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