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International Journal of Advanced Computer Science and Applications(IJACSA), Volume 12 Issue 2, 2021.
Abstract: In present days cloud computing is most famous innovation and has a great deal of research potential in different zones like allocation of resource, scheduling of data transfer, security as well as privacy and so on. Data transfer Scheduling is one of the significant issues for improving the proficiency of all cloud based administrations. In cloud computing, data transfer scheduling is utilized to allot the task to best reasonable asset for execution. There are various types of data transfer scheduling algorithms. A few issues like execution time, execution cost, high delay time, complexity, and high data transfer cost as well as various optimization problems have been measured in existing papers. To tackle all the above problems, in this paper, a new Adaptive approach are introduced which is a combination of Monarch Butterfly and Genetic (AMBA) Algorithm based data transfer scheduling is proposed. So here the concept is to develop an optimal algorithm for scheduling the data transfer in an efficient way which helps in reducing the data transfer time. The performance of proposed methodology analyzed in terms of evaluation metrics.
D. Sugumaran and C. R. Bharathi, “AMBA: Adaptive Monarch Butterfly Algorithm based Information of Transfer Scheduling in Cloud for Big Information Application” International Journal of Advanced Computer Science and Applications(IJACSA), 12(2), 2021. http://dx.doi.org/10.14569/IJACSA.2021.0120215
@article{Sugumaran2021,
title = {AMBA: Adaptive Monarch Butterfly Algorithm based Information of Transfer Scheduling in Cloud for Big Information Application},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2021.0120215},
url = {http://dx.doi.org/10.14569/IJACSA.2021.0120215},
year = {2021},
publisher = {The Science and Information Organization},
volume = {12},
number = {2},
author = {D. Sugumaran and C. R. Bharathi}
}
Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.