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A hybrid cloud load balancing and host utilization prediction method using deep learning and optimization techniques.

Sarita Simaiya1, Umesh Kumar Lilhore2, Yogesh Kumar Sharma3

  • 1Department of Computer Science and Engineering, Chandigarh University, Gharuan, Mohali, Punjab, 140413, India. saritasimaiya@gmail.com.

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Summary
This summary is machine-generated.

This study introduces a novel DPSO-GA hybrid model for dynamic workload provisioning in cloud computing, improving resource distribution and load balancing accuracy.

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Cloud Computing

Background:

  • Virtual machine integration optimizes cloud data center load balancing but faces challenges in cost, quality of service, and resource utilization.
  • Existing deep learning methods for cloud load balancing struggle with noisy workload fluctuations due to limited resource provisioning.
  • Long short-term memory (LSTM) models are crucial for predicting server load and workload provisioning.

Purpose of the Study:

  • To propose a novel hybrid deep learning model, DPSO-GA, for dynamic workload provisioning in cloud computing.
  • To address the trade-offs in cost, quality of service, performance, and resource utilization in cloud load balancing.
  • To overcome limitations in predicting noisy workload fluctuations and improve resource-level provisioning.

Main Methods:

  • A two-phase hybrid model: Phase 1 uses Particle Swarm Intelligence (PSO) and Genetic Algorithm (GA) for hyperparameter tuning. Phase 2 employs Convolutional Neural Network (CNN) and LSTM (CNN-LSTM) for resource consumption forecasting, trained with the PSO-GA approach.
  • Utilizes a one-dimensional CNN to extract features from VM workload statistics and LSTM to simulate temporal information for predicting upcoming VM workloads.
  • Integrates multi-resource utilization to address load balancing and over-provisioning issues.

Main Results:

  • The proposed DPSO-GA model demonstrates enhanced precision, accuracy, and load allocation in cloud environments.
  • Simulations using Google cluster traces benchmarks validate the model's efficiency in resource distribution and load balancing.
  • The hybrid approach effectively overcomes limitations in predicting noisy workload fluctuations and improves resource provisioning.

Conclusions:

  • The DPSO-GA hybrid model offers a significant advancement in dynamic workload provisioning for cloud computing.
  • It effectively balances cost, quality of service, and resource utilization, mitigating over-provisioning issues.
  • The model provides a robust solution for optimizing load balancing and resource distribution in cloud data centers.