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Optimizing multi-objective task scheduling in fog computing with GA-PSO algorithm for big data application.

Muhammad Saad1,2, Rabia Noor Enam1, Rehan Qureshi3

  • 1Computer Engineering Department, Sir Syed University of Engineering and Technology, Karachi, Pakistan.

Frontiers in Big Data
|March 7, 2024
PubMed
Summary
This summary is machine-generated.

A new hybrid Genetic Algorithm (GA)-Particle Swarm Optimization (PSO) effectively schedules tasks in fog computing environments. This approach significantly reduces execution, response, and completion times for big data processing.

Keywords:
cloud computingfog computingfog computing (FC)genetic algorithmhybrid GA-PSOhybrid algorithmparticle swarm optimizationtask scheduling

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Area of Science:

  • Computer Science
  • Distributed Systems
  • Artificial Intelligence

Background:

  • Traditional cloud computing faces challenges with Big Data's increasing volume and velocity, particularly in real-time processing and low latency.
  • Fog computing offers a distributed solution by leveraging edge devices, but efficient task scheduling remains complex due to multi-objective optimization needs.
  • Key challenges include balancing execution time, response time, and resource utilization in fog environments.

Purpose of the Study:

  • To propose a novel hybrid algorithm for optimizing multi-objective task scheduling in fog computing.
  • To enhance the performance of task scheduling by combining the strengths of Genetic Algorithm (GA) and Particle Swarm Optimization (PSO).
  • To address the limitations of traditional single-algorithm approaches in complex fog computing scenarios.

Main Methods:

  • Development of a hybrid Genetic Algorithm (GA)-Particle Swarm Optimization (PSO) algorithm.
  • Implementation of the hybrid algorithm for multi-objective task scheduling in simulated fog computing environments.
  • Comparative analysis of the hybrid algorithm against standalone GA, PSO, and a Hybrid PWOA algorithm.

Main Results:

  • The hybrid GA-PSO algorithm demonstrated significant improvements in execution time (up to 85.68% vs. GA, 84% vs. Hybrid PWOA, 51.03% vs. PSO).
  • Substantial reductions in response time were observed (up to 67.28% vs. GA, 54.24% vs. Hybrid PWOA, 75.40% vs. PSO).
  • Marked improvements in completion time were achieved (up to 68.69% vs. GA, 98.91% vs. Hybrid PWOA, 75.90% vs. PSO) across various task inputs and fog nodes.

Conclusions:

  • The hybrid GA-PSO algorithm effectively optimizes multi-objective task scheduling in fog computing.
  • This approach offers superior performance compared to traditional single-algorithm methods, addressing Big Data processing demands.
  • The findings suggest a promising direction for efficient resource management in distributed edge computing environments.