Synapse
⌘+K
Synapse
PulseExploreClubsResearchersJournals
Instagram
HomeClubsExplore
September 19, 2025Journal of Advanced College of Engineering and ManagementOpen Access

AI-Driven Intelligent Auto-Scaling for Cloud Resource Optimization

View Full Paper
Ask AI
Bookmark
Share

Authors

SPSudip PoudelKMKushal Sharma MarasiniLBLaxmi Prasad Bhatt

Discussion

Loading...

Member takes

Overview

This analysis introduces an AI-driven auto-scaling solution for AWS, optimizing resource management and performance.

Key Points

  • The AI-driven auto-scaling framework reduces operational costs and improves performance reliability in cloud environments.
  • Using LSTM neural networks, the system analyzes historical data to forecast resource demands effectively.
  • The framework evaluates multiple performance metrics such as CPU usage and memory availability to make real-time scaling decisions.
  • Experimental results indicate that the proposed solution is efficient for dynamic applications, enhancing resource responsiveness.

Cite This Study

Poudel et al. (2025) studied this question.

synapsesocial.com/papers/68d464ff31b076d99fa64c90https://doi.org/10.3126/jacem.v11i1.84521
View Full Paper
Ask AI
Bookmark
Share

Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1ML-Based Predictive Autoscaling on Aws2026
  2. 2AI-Based Automated Load Testing and Resource Scaling in Cloud Environments Using Self-Learning Agents2024
  3. 3Efficient resource allocation in cloud computing environments using AI-driven predictive analytics2024 · 38 citations
  4. 4Enhancing Cloud Scalability with AI-Driven Resource Management2024 · 11 citations
  5. 5AI-Based Load Forecasting And Resource Optimization For Energy-Efficient Cloud Computing2025