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AI-Enabled Dynamic Edge-Cloud Resource Allocation for Smart Cities and Smart Buildings.

Marian-Cosmin Dumitru1, Simona-Iuliana Caramihai1, Alexandru Dumitrascu1

  • 1Faculty of Automatic Control and Computers, National University of Science and Technology Politehnica Bucharest, RO-060042 Bucharest, Romania.

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Summary
This summary is machine-generated.

This study introduces a Seasonal Auto Regressive Integrated Moving Average (SARIMA) model for seamless cloud-edge switching in IoT. It ensures continuous operation and improved resource prediction accuracy, outperforming traditional methods.

Keywords:
edge computingresource allocationsmart buildingsmart city

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Data Science

Background:

  • The proliferation of Internet of Things (IoT) devices in smart cities generates vast data volumes, posing challenges for cloud-based analysis due to bandwidth limitations.
  • Edge computing offers a solution by processing data closer to the source, optimizing resource allocation and improving cloud performance.
  • Dynamic resource management is crucial for edge and cloud nodes, requiring task prioritization based on real-time load conditions.

Purpose of the Study:

  • To propose a predictive model for dynamic resource management in cloud-edge environments.
  • To ensure continuous operation and maintain command loop integrity during cloud-edge connection disruptions.
  • To enhance the accuracy of resource utilization predictions for efficient allocation in smart city applications.

Main Methods:

  • Development of a Seasonal Auto Regressive Integrated Moving Average (SARIMA) model for predicting resource needs.
  • Implementation of a seamless switching mechanism between cloud and edge nodes during connection loss.
  • Evaluation of the SARIMA model against a Simple Moving Average (SMA) baseline using real-world resource utilization data.

Main Results:

  • The SARIMA model demonstrated significant improvements in prediction accuracy compared to the SMA baseline.
  • Achieved up to 64% improvement in CPU usage prediction and 35% in RAM usage prediction.
  • Validated the effectiveness of incorporating seasonality and autoregressive components for predictive modeling in edge computing.

Conclusions:

  • The proposed SARIMA-based approach effectively manages resources in dynamic cloud-edge environments.
  • Ensures uninterrupted service and prediction integrity, crucial for smart city applications.
  • Highlights the potential of advanced time-series models for optimizing edge computing performance and resource allocation.