@inproceedings{scholars14705,
             doi = {10.1109/ICCOINS49721.2021.9497188},
            year = {2021},
            note = {cited By 4; Conference of 6th International Conference on Computer and Information Sciences, ICCOINS 2021 ; Conference Date: 13 July 2021 Through 15 July 2021; Conference Code:170762},
           pages = {396--400},
           title = {Design Pattern Based Distribution of Microservices in Cloud Computing Environment},
       publisher = {Institute of Electrical and Electronics Engineers Inc.},
         journal = {Proceedings - International Conference on Computer and Information Sciences: Sustaining Tomorrow with Digital Innovation, ICCOINS 2021},
          author = {Saboor, A. and Mahmood, A. K. and Hassan, M. F. and Shah, S. N. M. and Hassan, F. and Siddiqui, M. A.},
            isbn = {9781728171517},
        keywords = {Artificial intelligence; Computer science; Computers; Software engineering, Application architecture; Cloud applications; Cloud computing environments; Distribution strategies; Modeling environments; Network communications; Random allocation; Random distribution, Cloud computing},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85112426648&doi=10.1109\%2fICCOINS49721.2021.9497188&partnerID=40&md5=616aca5df05d88fa2b5a06b1446cf9a2},
        abstract = {Cloud computing is a paradigm that has already evolved. Cloud computing moved widely to microservices from monoliths. The modular cloud application has gained attention for Microservices. Intensive network communication is required to call the interdependent microservices operating inside the cloud nodes. This research focuses on container-based microservices pre-distribution techniques and proposes two distribution strategies i.e. design pattern distribution and random distribution. The microservices are arbitrarily distributed to the available data centers in the random allocation method. While the microservices are clustered in the pattern distribution based on behavioral design patterns, which identify common contact patterns between entities. A custom-built modeling environment has been used to evaluate the proposed method. The findings revealed that the pre-distribution of microservices in accordance with the application architecture trend led to substantial less response time for the calls made to services hosted at geographically dispersed data centers. {\^A}{\copyright} 2021 IEEE.}
}