An empirical study for mitigating sustainable cloud computing challenges using ISM-ANN.
Journal:
PloS one
PMID:
39348369
Abstract
The significance of cloud computing methods in everyday life is growing as a result of the exponential advancement and refinement of artificial technology. As cloud computing makes more progress, it will bring with it new opportunities and threats that affect the long-term health of society and the environment. Many questions remain unanswered regarding sustainability, such as, "How will widely available computing systems affect environmental equilibrium"? When hundreds of millions of microcomputers are invisible to each other, what will society look like? What does this mean for social sustainability? This paper empirically investigates the ethical challenges and practices of cloud computing about sustainable development. We conducted a systematic literature review followed by a questionnaire survey and identified 11 sustainable cloud computing challenges (SCCCs) and 66 practices for addressing the identified challenges. Interpretive structural modeling (ISM) and Artificial Neural Networks (ANN) were then used to identify and analyze the interrelationship between the SCCCs. Then, based on the results of the ISM, 11 process areas were determined to develop the proposed sustainable cloud computing challenges mitigation model (SCCCMM). The SCCCMM includes four main categories: Requirements specification, Quality of Service (QoS) and Service Legal Agreement (SLA), Complexity and Cyber security, and Trust. The model was subsequently tested with a real-world case study that was connected to the environment. In a sustainable cloud computing organization, the results demonstrate that the proposed SCCCMM aids in estimating the level of mitigation. The participants in the case study also appreciated the suggested SCCCMM for its practicality, user-friendliness, and overall usefulness. When it comes to the sustainability of their software products, we believe that organizations involved in cloud computing can benefit from the suggested SCCCMM. Additionally, researchers and industry practitioners can expect the proposed model to provide a strong foundation for developing new sustainable methods and tools for cloud computing.