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A Cloud Computing-Based Modified Symbiotic Organisms Search Algorithm (AI) for Optimal Task Scheduling.

Ajoze Abdulraheem Zubair1, Shukor Abd Razak1, Md Asri Ngadi1

  • 1Faculty of Engineering, School of Computing, Universiti Teknologi Malaysia (UTM), Johor Bahru 81310, Malaysia.

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Summary
This summary is machine-generated.

This study introduces a modified symbiotic organisms search (G_SOS) algorithm for efficient cloud task scheduling. G_SOS optimizes resource allocation, reducing task execution time and costs for large-scale computing tasks.

Keywords:
cloud computingcloud resource managementconvergence speedecosystemgeometric meansymbiotic organisms search algorithmtask scheduling

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Cloud Computing

Background:

  • Task scheduling is an NP-complete problem, challenging for large-scale systems.
  • Bio-inspired swarm intelligence algorithms offer novel optimization approaches.
  • Symbiotic Organisms Search (SOS) simulates ecological interactions for problem-solving.

Purpose of the Study:

  • To develop an efficient task scheduling algorithm for Infrastructure as a Service (IaaS) clouds.
  • To improve the mapping of heterogeneous tasks to diverse cloud resources.
  • To minimize key performance indicators like makespan, cost, and response time.

Main Methods:

  • A modified Symbiotic Organisms Search (SOS) algorithm, termed G_SOS, is proposed.
  • The mutualism process in SOS is simplified using equity and a geometric mean.
  • CloudSim toolkit is used to simulate and evaluate the algorithm's performance.

Main Results:

  • G_SOS demonstrated significant improvements in makespan minimization compared to classical SOS and Particle Swarm Optimization with Simulated Annealing (PSO-SA).
  • Improvements ranged from 0.61-20.08% over SOS and 1.92-25.68% over PSO-SA for large-scale tasks (100-1000 Million Instructions).
  • The modified algorithm enhances convergence speed and solution optimality.

Conclusions:

  • The G_SOS algorithm offers a superior approach to cloud task scheduling.
  • It effectively balances resource utilization and minimizes execution time and costs.
  • This bio-inspired method provides a competitive alternative to existing scheduling techniques.