Efficient Edge Resource Allocation Using Deep Neural Network-XGBoost for Distributed Computing in Edge Networks

Key Points

• This research aims to enhance resource allocation efficiency in edge networks using advanced algorithms.
• Utilized deep neural networks and XGBoost for optimizing resource distribution.
• Analyzed various edge network configurations to assess performance.
• Applied metrics for measuring efficiency gains across different scenarios.
• Demonstrated a 25% increase in resource allocation efficiency compared to traditional methods.
• Achieved an average response time reduction of 30 milliseconds with the new approach.
• Showed that the combination of algorithms improved overall throughput by 15% in distributed environments.