Statistical Comparison of Architecture Driven Modernization with other Cloud Migration Frameworks and Formation of Clusters

Mubeen Aslam, Lukman AB Rahim, Manzoor Hashmani, Junzo Watada


Corporations are migrating their legacy software systems towards the cloud environment for amelioration, to avail benefits of the cloud. Long term success of modernizing a legacy software depends on the characteristics of the chosen cloud migration approach. Organizations must think over how strategically imperative is the chosen cloud migration framework to their business? Thus, the Object Management Group (OMG) has defined standards for the modernization process based on Architecture Driven Modernization (ADM) framework. ADM serves as a vehicle for facilitating the arrangement of information technology with business stratagem and its architecture. Until now, it seems that there is no systematic mapping among ADM and other cloud migration frameworks, highlighting the demanding features. This research aims to give an in-depth study of similar cloud migration frameworks. Thus, the researchers introduced the clusters containing cloud migration frameworks having similar features to ADM. This systematic mapping can be seen as a valuable asset for those who are interested in choosing the best migration framework from the pool of cloud modernization frameworks, according to their legacy software requirements. The clustering technique is used to appraise and compare ADM with some of the other cloud migration frameworks for highlighting the similarities and key differences. The quality of clusters is evaluated by the Rand index and Silhouette measurements. The study distills the record and yields a sound and healthy catalog for essential events and concerns that are communal in cloud migration frameworks. This research offers the one-stop-shop convenience that the industry desperately desires. 


cloud migration frameworks; Architecture Driven Modernization (ADM); statistical analysis; clustering techniques.

Full Text:



A. Alkhalil, R. Sahandi, and D. John, “An exploration of the determinants for decision to migrate existing resources to cloud computing using an integrated TOE-DOI model,†J. Cloud Comp., vol. 6, no. 1, p. 2, Dec. 2017.

P. K. Senyo, E. Addae, and R. Boateng, “Cloud computing research: A review of research themes, frameworks, methods and future research directions,†Int. J. Inf. Manage., vol. 38, no. 1, pp. 128–139, Feb. 2018.

M. Aslam, L. bin AB Rahim, M. Hashmani, and J. Watada, “Domain specific modelling language of PIM for OSSS on infrastructure cloud service model,†in 2018 4th International Conference on Computer and Information Sciences (ICCOINS), 2018, pp. 1–6.

M. Aslam, L. bin A. Rahim, J. Watada, and M. Hashmani, “Clustering-based cloud migration strategies,†J. Adv. Comput. Intell. Intell. Informatics, 2018.

N. C. Chung, B. Mirza, H. Choi, J. Wang, D. Wang, P. Ping, and W. Wang, “Unsupervised classification of multi-omics data during cardiac remodeling using deep learning.,†Methods, vol. 166, pp. 66–73, Aug. 2019.

H. Yu, B. Chapman, A. Di Florio, E. Eischen, D. Gotz, M. Jacob, and R. H. Blair, “Bootstrapping estimates of stability for clusters, observations and model selection,†Comput. Stat., vol. 34, no. 1, pp. 349–372, Aug. 2018.

H. Nguyen, X.-N. Bui, Q.-H. Tran, and N.-L. Mai, “A new soft computing model for estimating and controlling blast-produced ground vibration based on Hierarchical K-means clustering and Cubist algorithms,†Appl. Soft Comput., vol. 77, pp. 376–386, Apr. 2019.

M. Ellison, R. Calinescu, and R. F. Paige, “Evaluating cloud database migration options using workload models,†J. Cloud Comp., vol. 7, no. 1, p. 6, Dec. 2018.

K. Sabiri, F. Benabbou, H. Moutachaouik, and M. Hain, “Towards a cloud migration framework,†in 2015 Third World Conference on Complex Systems (WCCS), 2015, pp. 1–6.

G. Shreelekhya, Yazhini, U. Senthilkumaran, and N. Manikandan, “Methods for evaluating software architecture-A survey,†Int. J. Pharm. Technol., vol. 8, no. 4, pp. 25720–25733, Dec. 2016.

D. Ardagna et al., “MODAClouds: A model-driven approach for the design and execution of applications on multiple clouds,†in 2012 4th International Workshop on Modeling in Software Engineering, MiSE 2012 - Proceedings, 2012.

“Enhance Your Model-driven Modernization Process with Agile Practices,†in Proceedings of the 1st International Workshop in Software Evolution and Modernization, 2013, pp. 95–102.

L. Favre, “A Framework for Modernizing Non-Mobile Software: A Model-Driven Engineering Approach,†in Protocols and applications for the industrial internet of things, C. González García, V. García-Díaz, B. C. P. García-Bustelo, and J. M. C. Lovelle, Eds. IGI Global, 2018, pp. 192–224.

S. Frey and W. Hasselbring, “The CloudMIG Approach: Model-Based Migration of Software Systems to Cloud-Optimized Applications,†Internati J. Adv. Softw., 2011.

P. Mohagheghi and T. Sæther, “Software engineering challenges for migration to the service cloud paradigm: ongoing work in the REMICS project,†in 2011 IEEE World Congress on Services, 2011, pp. 507–514.

K. Sabiri, F. Benabbou, M. Hain, H. Moutachaouik, and K. Akodadi, “A survey of cloud migration methods: A comparison and proposition,†ijacsa, vol. 7, no. 5, 2016.

J. Troya, H. Bruneliere, M. Fleck, M. Wimmer, L. Orue-Echevarria, and J. Gorroñogoitia, “ARTIST: Model-based stairway to the cloud,†in CEUR Workshop Proceedings, 2015.

I. Krasteva, S. Stavru, and S. Ilieva, “Agile Model-Driven Modernization to the Service Cloud,†Proc. Eighth Int. Conf. Internet Web Appl. Serv. (ICIW 2013), 2013.

G. A. Lewis, E. J. Morris, D. Smith, and S. Simanta, “SMART: Analyzing the Reuse Potential of Legacy Systems in Service- Oriented Architecture (SOA) Environments,†Tech. Rep. C. Softw. Eng. Institute, Carnegie Mellon Univ. Pittsburgh, PA, 2008.

REMICS, “REMICS,†Reuse and Migration of legacy applications to Interoperable Cloud Services, 2016. .

S. Wang, M. Zafer, and K. K. Leung, “Online Placement of Multi-Component Applications in Edge Computing Environments,†IEEE Access, 2017.

C. Chatfield and C. Chatfield, “Multidimensional scaling and cluster analysis,†in Introduction to Multivariate Analysis, 2018.

Data Mining and Knowledge Discovery Handbook. 2005.

T. Kim, I. R. Chen, Y. Lin, A. Y.-Y. Wang, J. Y. H. Yang, and P. Yang, “Impact of similarity metrics on single-cell RNA-seq data clustering.,†Brief. Bioinformatics, Aug. 2018.

A. Saxena et al., “A review of clustering techniques and developments,†Neurocomputing, 2017.

J. Irani, N. Pise, and M. Phatak, “Clustering Techniques and the Similarity Measures used in Clustering: A Survey,†IJCA, vol. 134, no. 7, pp. 9–14, Jan. 2016.

M. Fogaça, A. B. Kahng, R. Reis, and L. Wang, “Finding placement-relevant clusters with fast modularity-based clustering,†in Proceedings of the 24th Asia and South Pacific Design Automation Conference on - ASPDAC ’19, New York, New York, USA, 2019, pp. 569–576.

A. Tandon, A. Albeshri, V. Thayananthan, W. Alhalabi, and S. Fortunato, “Fast consensus clustering in complex networks.,†Phys. Rev. E, vol. 99, no. 4–1, p. 042301, Apr. 2019.

W. Chen, C. Chen, X. Jiang, and L. Liu, “Multi-controller placement towards SDN based on louvain heuristic algorithm,†IEEE Access, 2018.

M. Aslam, “Enhancing Information Security Management by STOPE View with Six Sigma Approach,†Int. J. Eng. Technol., 2014.

C. Li, M. Cerrada, D. Cabrera, R. V. Sanchez, F. Pacheco, G. Ulutagay, and J. Valente de Oliveira, “A comparison of fuzzy clustering algorithms for bearing fault diagnosis,†IFS, vol. 34, no. 6, pp. 3565–3580, Jun. 2018.

M. A. Fitriani, A. Musdholifah, and S. Hartati, “Adaptive Unified Differential Evolution for Clustering,†IJCCS (Indonesian J. Comput. Cybern. Syst., 2018.

A. Rasid Mamat, F. Susilawati Mohamed, M. Afendee Mohamed, N. Mohd Rawi, and M. Isa Awang, “Silhouette index for determining optimal k-means clustering on images in different color models,†IJET, vol. 7, no. 2.14, p. 105, Apr. 2018.



  • There are currently no refbacks.

Published by INSIGHT - Indonesian Society for Knowledge and Human Development