Harnessing Cloud Computing for Scalable Scientific Workflows: Solutions and Strategies

Abstract

This paper analyses the implementation of cloud computing in scientific workflows, particularly how they obtain scalability, efficiency, and cost-effectiveness. The five key elements are explored in particular: resource optimization, cost-saving strategies, application compatibility, data management challenges, and security implications. Qualitative methods, such as expert interviews and case studies, find that cloud computing improves the performance of workflows, lowers costs, and prolongs support from applications. More so, issues remain in the fronts of resource variability, pricing models, bottlenecks in data transfer, and security vulnerabilities. The study concludes by emphasizing the need for hybrid models of cloud, advanced optimization techniques, and robust security frameworks for future research and practical implementation.