Version 1
: Received: 7 October 2024 / Approved: 7 October 2024 / Online: 8 October 2024 (17:37:30 CEST)
How to cite:
Sathupadi, K.; Avula, R.; Velayutham, A.; Achar, S. RAP-Optimizer: Resource-Aware Predictive Model for Cost Optimization of Cloud AIaaS Applications. Preprints2024, 2024100504. https://doi.org/10.20944/preprints202410.0504.v1
Sathupadi, K.; Avula, R.; Velayutham, A.; Achar, S. RAP-Optimizer: Resource-Aware Predictive Model for Cost Optimization of Cloud AIaaS Applications. Preprints 2024, 2024100504. https://doi.org/10.20944/preprints202410.0504.v1
Sathupadi, K.; Avula, R.; Velayutham, A.; Achar, S. RAP-Optimizer: Resource-Aware Predictive Model for Cost Optimization of Cloud AIaaS Applications. Preprints2024, 2024100504. https://doi.org/10.20944/preprints202410.0504.v1
APA Style
Sathupadi, K., Avula, R., Velayutham, A., & Achar, S. (2024). RAP-Optimizer: Resource-Aware Predictive Model for Cost Optimization of Cloud AIaaS Applications. Preprints. https://doi.org/10.20944/preprints202410.0504.v1
Chicago/Turabian Style
Sathupadi, K., Arunkumar Velayutham and Sandesh Achar. 2024 "RAP-Optimizer: Resource-Aware Predictive Model for Cost Optimization of Cloud AIaaS Applications" Preprints. https://doi.org/10.20944/preprints202410.0504.v1
Abstract
AI-driven applications are rapidly growing, and more applications are joining the market competition. As a result, the AI-as-a-Service (AIaaS) model is experiencing rapid growth. Many of these AIaas-based applications are not properly optimized initially. Once they start experiencing a large volume of traffic, different challenges start revealing themselves. One of these challenges is maintaining a profit margin for the sustainability of the AIaaS application-based business model, which depends on the proper utilization of computing resources. This paper introduces the Resource Award Predictive (RAP) model for AIaaS cost optimization called RAP-Optimizer. It is developed by combining a Deep Neural Network (DNN) with the simulated annealing optimization algorithm. It is designed to reduce resource underutilization and minimize the number of active hosts in cloud environments. It dynamically allocates resources and handles API requests efficiently. The RAP-Optimizer reduces the number of active physical hosts by an average of 5 per day, leading to a 45% decrease in server costs. The impact of the RAP-Optimizer has been observed over a 12-month period. The observational data show a significant improvement in resource utilization. It effectively reduces operational costs from $2,600 to $1,250 per month. Furthermore, the RAP-Optimizer increases the profit margin by 179%, from $600 to $1,675 per month. The inclusion of the Dynamic Dropout Control (DDC) algorithm in the DNN training process mitigates overfitting, achieving a 97.48% validation accuracy and a validation loss of 2.82%. These results indicate that the RAP-Optimizer effectively enhances resource management and cost-efficiency in AIaaS application, making it a valuable solution for modern cloud environments.
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.
