Parallel Algorithm Design for Big Data Processing in Cloud Computing Environments

Abstract

The rise of big data has necessitated the development of novel techniques to process and analyze large volumes of data efficiently. Cloud computing has emerged as a prominent platform for processing big data due to its scalability, flexibility, and cost-effectiveness. However, processing big data on cloud computing environments presents a significant challenge due to the enormous amount of data that needs to be processed. In this paper, we propose a parallel algorithm design for big data processing in cloud computing environments. Our proposed algorithm is designed to exploit the parallelism inherent in cloud computing environments to achieve efficient processing of large volumes of data. We evaluate our algorithm using two case studies, one involving the analysis of healthcare data and the other involving the analysis of financial data. Our results show that our algorithm is effective in achieving significant speedup in the processing of large volumes of data, making it suitable for use in big data processing applications.