Optimization of Resource Sharing Writing Corpus Based on Cloud Computing

Considering large-scale writing corpora, handling calculations of big data sets and performing complex computational tasks, the computational process, can become vast, so there are scalable and elastic computing requirements and resource management needs are required. In this study, we aimed to compare two optimization schemes according to different data processing steps using cloud computing and local storage for the experiments. Cloud computing scheme would help to get elastic computing and storage with high-capacity and the local storage scheme would depend only on local hardware resources. The experimental results show that there is no difference on data processing time and resource usage compared to data processing task performance. We have evaluated the performance through different schemes and found the cloud mode has a shorter data processing time and has more advantages with the resource utilization. The cloud-based mode does have more advantages for the data processing tasks. The results show that the cloud-based mode could complete the data processing tasks in 2-5 seconds, in most of our computations, whereas storage mode should require 7-13 seconds for the most data processing tasks.