Cloud migration framework clustering method for social decision support in modernizing the legacy system

• Main Authors: Aslam, M., Rahim, L.A.B., Watada, J., Rubab, S., Khan, M.A., AlQahtani, S.A., Gadekallu, T.R.
• Format: Article
• Published: 2023
• Online Access: http://scholars.utp.edu.my/id/eprint/38076/; https://www.scopus.com/inward/record.uri?eid=2-s2.0-85173429435&doi=10.1002%2fett.4863&partnerID=40&md5=3808171081faa08303c9411265a57d5f

Cloud solutions accelerate the large-scale acceptance of IoT projects. By diminishing the need for maintaining on-premises infrastructure, the cloud has enabled corporations to surpass the traditional applications of IoT (e.g., in-home appliances) and opened the doors for large-scale deployment of IoT applications on the cloud. However, shifting legacy systems to the cloud environment can be considerably difficult. Accordingly, this article proposes a method that may support organizations in deciding to modernize their legacy systems. The main concept of this study is to discuss the modernization strategies in detail and to support organizations in selecting the most accurate and appropriate cloud migration strategy, based on their requirements of the legacy system. This article introduces a novel research process, called the K-means cosine cloud clustering method (K3CM). K3CM is a statistical knowledge-based method for identifying and clustering the most relevant and similar cloud migration strategies. The quality of a cluster is evaluated by measuring intra-cohesiveness. Simulation experiments statistically analyzed, evaluated, and verified the quality of K3CM clusters. Correspondence analysis explored the similarity and relationship among cloud migration frameworks and validated the proposed technique. The statistical and simulation results of this study focus on the analytics and decision support system implementation that provides a reliable, valid, and robust clustering method for modernizing the legacy system. © 2023 John Wiley & Sons Ltd.