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A fusion deep Q-learning and particle swarm optimization algorithm for adaptive resource allocation in cloud

Ahmed Hadi Ali Al-Jumaili1, Mohammed E Seno2, Waleed Kareem Awad3

  • 1College of Information Technology, University of Fallujah, Al Anbar, Al Fallujah, 31002, Iraq. ahmed_hadi@uofallujah.edu.iq.

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|April 16, 2026
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Summary
This summary is machine-generated.

This study introduces a hybrid Deep Q-Learning and Particle Swarm Optimization framework for adaptive cloud resource scheduling. The approach significantly reduces task execution time and improves resource utilization while minimizing energy consumption and SLA violations.

Keywords:
Cloud computingDeep Q-Learning (DQL)Hybrid algorithmsParticle swarm optimization (PSO)Reinforcement learningResource allocationSLATask scheduling

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Cloud Computing

Background:

  • Effective resource allocation in cloud computing is challenging due to dynamic loads and service-level expectations.
  • Balancing execution time, energy consumption, and cost is crucial for cloud environments.

Purpose of the Study:

  • To develop an adaptive, multi-objective scheduling framework for cloud resource allocation.
  • To integrate Deep Q-Learning (DQL) with Particle Swarm Optimization (PSO) for enhanced decision-making.

Main Methods:

  • A hybrid framework combining Deep Q-Learning for strategy learning and Particle Swarm Optimization for global search and convergence acceleration.
  • Utilized CloudSim with real and synthetic workloads (Google Cluster, Planet Lab traces) for simulation and evaluation.

Main Results:

  • Achieved a 35% reduction in average task execution time (245s to 159s).
  • Increased resource utilization by approximately 40% (60.1% to 84.6%).
  • Reduced Service Level Agreement (SLA) violations from 28 to 8 and lowered energy consumption to 6.3 kWh.

Conclusions:

  • Coupling reinforcement learning with swarm intelligence yields adaptive, high-quality decisions for real-time cloud resource scheduling.
  • The proposed DQL-PSO framework significantly outperforms standalone and existing hybrid models.