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Related Experiment Videos

An optimized resource allocation in cloud using prediction enabled reinforcement learning.

S Kayalvili1, R Senthilkumar2, S Yasotha3

  • 1Kongu Engineering College, Erode, Tamil Nadu, India. skayalvili10@gmail.com.

Scientific Reports
|October 15, 2025
PubMed
Summary

This study introduces a novel Prediction-enabled Cloud Resource Allocation (PCRA) framework using reinforcement learning. PCRA enhances adaptive resource allocation, reducing costs and SLA violations in dynamic cloud environments.

Keywords:
Cloud computingOptimization, reinforcement learning (RL), whale optimization algorithmResource allocation

Related Experiment Videos

Area of Science:

  • Computer Science
  • Cloud Computing
  • Artificial Intelligence

Background:

  • Cloud computing offers accessible, on-demand resource sharing.
  • Adaptive resource allocation (RA) is crucial for Quality-of-Service (QoS) and cost reduction in cloud environments.
  • Existing RA methods struggle with dynamic workloads and fluctuating system states, often lacking adaptability.

Purpose of the Study:

  • To propose a Prediction-enabled feedback system for adaptive resource allocation (PCRA) in cloud computing.
  • To address the challenges of dynamic workloads and improve RA adaptability.
  • To enhance QoS and reduce resource costs in cloud-based package facilities.

Main Methods:

  • Developed a reinforcement learning-based RA framework (PCRA) with Q-value prediction.
  • Utilized Q-learning for accurate Q-value prediction to forecast management value.
  • Employed the Feature Selection Whale Optimization Algorithm (FSWOA) for impartial resource allocation.

Main Results:

  • PCRA achieved 94.7% Q-value prediction accuracy.
  • Demonstrated significant reduction in Service Level Agreement (SLA) violations by 17.4%.
  • Reduced resource costs by 17.4% compared to traditional methods.

Conclusions:

  • The PCRA framework effectively enables real-time, adaptive resource allocation in cloud environments.
  • PCRA significantly improves prediction accuracy and reduces both SLA violations and resource costs.
  • The proposed system offers a robust solution for managing dynamic workloads in cloud computing.