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Deep Reinforcement Learning for Workload Prediction in Federated Cloud Environments.

Zaakki Ahamed1, Maher Khemakhem1, Fathy Eassa1

  • 1Department of Computer Science, Faculty of Computing and Information Technology, King Abdulaziz University (KAU), Jeddah 21589, Saudi Arabia.

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|August 12, 2023
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Summary
This summary is machine-generated.

Federated Cloud Workload Prediction with Deep Q-Learning (FEDQWP) optimizes resource allocation for cloud service providers. This novel approach enhances CPU utilization, reduces energy consumption, and minimizes service level agreement violations in federated cloud environments.

Keywords:
Deep Q learningDeep Reinforcement LearningFederated Cloud ComputingMachine LearningVirtual Machine placementenergy efficiencyworkload prediction

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Cloud Computing

Background:

  • Federated Cloud Computing (FCC) offers scalability but faces challenges in energy efficiency and Service Level Agreement (SLA) adherence.
  • Existing research often prioritizes Virtual Machine (VM) placement over holistic performance optimization.

Purpose of the Study:

  • To introduce a novel solution, Federated Cloud Workload Prediction with Deep Q-Learning (FEDQWP), for optimizing FCC environments.
  • To address VM placement, energy efficiency, and SLA preservation simultaneously.

Main Methods:

  • Development of the FEDQWP model utilizing Deep Q-Learning (DQL) for workload prediction and resource allocation.
  • Extensive evaluation using real-world workloads to compare FEDQWP against existing solutions.

Main Results:

  • FEDQWP demonstrated superior performance in CPU utilization (median 29.02%), migration time (avg. 0.31 units), and task completion (avg. 699 tasks).
  • The DQL model achieved the lowest energy consumption (avg. 1.85 kWh) and minimal SLA violations (avg. 0.03 violations).

Conclusions:

  • The FEDQWP model significantly outperforms existing algorithms in key performance metrics within FCC settings.
  • FEDQWP offers a comprehensive approach for optimizing resource allocation, energy efficiency, and SLA adherence in federated clouds.