Version 1
: Received: 9 May 2020 / Approved: 10 May 2020 / Online: 10 May 2020 (16:15:14 CEST)
How to cite:
Zakeri, S.; Krishnasamy, K.; Gomathi, B. Multi-Variable Stress Balancing Wireless Method Based on the Status of the Machines in the Cloud Spaces by Neural Networks. Preprints2020, 2020050174. https://doi.org/10.20944/preprints202005.0174.v1
Zakeri, S.; Krishnasamy, K.; Gomathi, B. Multi-Variable Stress Balancing Wireless Method Based on the Status of the Machines in the Cloud Spaces by Neural Networks . Preprints 2020, 2020050174. https://doi.org/10.20944/preprints202005.0174.v1
Zakeri, S.; Krishnasamy, K.; Gomathi, B. Multi-Variable Stress Balancing Wireless Method Based on the Status of the Machines in the Cloud Spaces by Neural Networks. Preprints2020, 2020050174. https://doi.org/10.20944/preprints202005.0174.v1
APA Style
Zakeri, S., Krishnasamy, K., & Gomathi, B. (2020). Multi-Variable Stress Balancing Wireless Method Based on the Status of the Machines in the Cloud Spaces by Neural Networks<strong><em> </em></strong>. Preprints. https://doi.org/10.20944/preprints202005.0174.v1
Chicago/Turabian Style
Zakeri, S., Karthikeyan Krishnasamy and Bernard Gomathi. 2020 "Multi-Variable Stress Balancing Wireless Method Based on the Status of the Machines in the Cloud Spaces by Neural Networks<strong><em> </em></strong>" Preprints. https://doi.org/10.20944/preprints202005.0174.v1
Abstract
Cloud computations are based on the computer networks such as Internet which presents a new pattern to provide, consume and deliver services such as infrastructure, software, ground and other resources using network. The inappropriate timing of assigning loads to the virtual machines in the computational space could lead to unbalance in the system. One of the challenging planning problems. In the cloud data centers is considering both assigning and migration (transfer) of the virtual machines with the ability of reconfiguration and the integrated features of the hosting physical machines. In this article, we introduce an integrated and dynamic timing algorithm based on the Genetic evolution algorithm. The suggested method was evaluated based on these factors and different inputs. Our suggested method is done using Java programming language and cloud-SME simulation. The results show that the execution time and the response time were improved by 12 and 1 percent respectively.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
