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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.
Sensors (Basel, Switzerland)
|February 26, 2022
Summary
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.
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.
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