Integration of Environmental Sustainability and Technological Performance in Healthcare Cloud Computing An ARAS Methodological Study
This research explores Benefits of Cloud Computing in Healthcare through an In-Depth Evaluation ARAS method. This study evaluates five major cloud computing simulators, Clouds, Cloud Analyst, Green Cloud, Network Cloud Sim, and Ground Sim, across four critical dimensions. Simulator capabilities, underlying platform integration, programming language support, and software compatibility. The study uses a systematic evaluation framework that includes normalized matrices and weighted ratings to determine the most effective Cloud-based solution for healthcare applications. The findings reveal that Green Cloud has proven to be a very effective solution, achieving the highest performance scores (Si: 0.283135, Ki: 0.924851), followed by Network Cloud Sim (Si: 0.177561, Ki: 0.579998). The analysis reveals that environmentally sustainable solutions, represented by the Green Cloud, can deliver optimal performance while maintaining strong technical capabilities. This finding is particularly important as healthcare organizations increasingly prioritize both operational efficiency and environmental responsibility. The study also highlights potential areas for improvement in traditional cloud computing tools, as evidenced by the low rankings of Cloud SIM and Cloud Analyst. These findings provide valuable insights for healthcare organizations looking to implement cloud computing solutions, and suggest that emphasis should be placed on solutions that balance environmental sustainability with strong technical performance. This study further expands the expanding knowledge on the use Cloud computing in healthcare provides valuable insights and practical advice for improving healthcare services and management. Organizations transitioning to cloud-based systems
Publication Details
- Journal
- Journal on Applied and Chemical Physics
- Published
- 2025-05-06
- DOI
- https://doi.org/10.46632/jacp/4/1/5
- Primary Topic
- Big Data and Business Intelligence
- Type
- article
- Field-Weighted Citation Impact
- 0.00