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

被引:1
|
作者
Aslam, Mubeen [1 ]
Rahim, Lukman A. B. [2 ]
Watada, Junzo [3 ]
Rubab, Saddaf [4 ]
Khan, Muhammad Attique [5 ,6 ]
Alqahtani, Salman A. [7 ]
Gadekallu, Thippa Reddy [8 ,9 ,10 ,11 ]
机构
[1] Quest Int Univ, Fac Comp & Engn, Sch Comp, Ipoh, Perak, Malaysia
[2] Univ Teknol PETRONAS, Dept Comp & Informat Sci, High Performance Cloud Comp Ctr, Tronoh, Malaysia
[3] Waseda Univ, Grad Sch Informat Prod & Syst, Tokyo, Japan
[4] Univ Sharjah, Coll Comp & Informat, Dept Comp Engn, Sharjah, U Arab Emirates
[5] HITEC Univ, Dept CS, Taxila, Pakistan
[6] Lebanese Amer Univ, Dept Comp Sci & Math, Beirut, Lebanon
[7] King Saud Univ, Coll Comp & Informat Sci, Dept Comp Engn, Riyadh, Saudi Arabia
[8] Zhongda Grp, Jiaxing 314312, Zhejiang, Peoples R China
[9] Lebanese Amer Univ, Dept Elect & Comp Engn, Byblos, Lebanon
[10] Lovely Profess Univ, Div Res & Dev, Phagwara, India
[11] Jiaxing Univ, Coll Informat Sci & Engn, Jiaxing, Peoples R China
关键词
Compendex;
D O I
10.1002/ett.4863
中图分类号
TN [电子技术、通信技术];
学科分类号
0809 ;
摘要
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. 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.image
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页数:21
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