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QoS-Aware Cost Minimization Strategy for AMI Applications in Smart Grid Using Cloud Computing.

Asfandyar Khan1, Arif Iqbal Umar1, Syed Hamad Shirazi1

  • 1Department of Information Technology, Hazara University Mansehra, Mansehra 21120, Pakistan.

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|July 9, 2022
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Summary
This summary is machine-generated.

A new QoS-aware Hybrid Queue Scheduling (HQS) model optimizes cloud services for smart grids. This approach reduces costs for processing, memory, and bandwidth for advanced metering infrastructure applications.

Keywords:
Internet of ThingsSmart Gridadvanced metering infrastructurecloud computinglatencyquality of serviceschedulingvirtual machine

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Area of Science:

  • Cloud computing
  • Internet of Things (IoT)
  • Smart Grid technology

Background:

  • Smart grids generate vast data from smart meters, requiring efficient resource management.
  • Cloud computing and IoT offer on-demand services but pose cost challenges for utility providers.
  • Efficient utilization of buffer, storage, processing, and bandwidth is crucial for smart grid networks.

Purpose of the Study:

  • To introduce a Quality of Service (QoS)-aware Hybrid Queue Scheduling (HQS) model for IoT-integrated cloud environments in smart grids.
  • To optimize resource utilization for advanced metering infrastructure (AMI) traffic.
  • To minimize the cost associated with buffer, processing power, and network bandwidth.

Main Methods:

  • Developed a QoS-aware Hybrid Queue Scheduling (HQS) model.
  • Integrated the model within an IoT-based cloud environment for smart grid networks.
  • Utilized the CloudSim simulator with a mathematical model for optimization.
  • Investigated the impact of cloudlets on virtual machine resource costs (RAM, CPU, bandwidth).

Main Results:

  • The proposed HQS model effectively classifies and prioritizes AMI application traffic based on QoS levels.
  • Simulation results demonstrate significant cost reduction in processing, memory, and bandwidth compared to existing schemes.
  • The model proved realistic and performed as expected for AMI traffic in smart grid cloud environments.

Conclusions:

  • The QoS-aware HQS model offers a cost-effective solution for managing smart grid data in cloud environments.
  • This approach enhances resource efficiency for advanced metering infrastructure applications.
  • The model provides a practical framework for utility providers to optimize cloud service implementation in smart grids.