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Utilizing Electroencephalography Measurements for Comparison of Task-Specific Neural Efficiencies: Spatial Intelligence Tasks
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Performance comparison of heuristic algorithms for task scheduling in IaaS cloud computing environment.

Syed Hamid Hussain Madni1,2, Muhammad Shafie Abd Latiff1, Mohammed Abdullahi1,3

  • 1Faculty of Computing, Universiti Teknologi Malaysia, Skudai, Johor, Malaysia.

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Summary

This study compares six heuristic algorithms for cloud computing task scheduling. It analyzes their performance in terms of cost, imbalance, makespan, and throughput for optimal resource allocation.

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Area of Science:

  • Cloud Computing
  • Distributed Systems
  • Algorithm Analysis

Background:

  • Cloud computing infrastructure efficiently handles large computational tasks.
  • Task scheduling in cloud environments is an NP-complete problem, necessitating heuristic approaches.
  • Existing heuristic algorithms have varied assumptions, making optimal algorithm selection challenging.

Purpose of the Study:

  • To implement and compare the performance of six rule-based heuristic algorithms for autonomous task scheduling.
  • To evaluate algorithm effectiveness in both homogeneous and heterogeneous cloud environments.
  • To analyze performance based on key metrics: cost, degree of imbalance, makespan, and throughput.

Main Methods:

  • Implementation of six heuristic algorithms: First Come First Serve (FCFS), Minimum Completion Time (MCT), Minimum Execution Time (MET), Max-min, Min-min, and Sufferage.
  • Task scheduling simulation in both homogeneous and heterogeneous cloud computing environments.
  • Comparative performance analysis using metrics such as cost, load balancing (degree of imbalance), makespan (total completion time), and throughput (tasks completed per unit time).

Main Results:

  • Performance variations observed among the six heuristic algorithms across different cloud environments.
  • Identification of algorithms that perform better for specific metrics (e.g., cost-effectiveness, makespan reduction).
  • The study provides empirical data on the trade-offs associated with each scheduling algorithm.

Conclusions:

  • No single heuristic algorithm universally outperforms others across all metrics and environments.
  • Algorithm selection should be based on specific cloud computing objectives (e.g., minimizing cost vs. minimizing makespan).
  • The comparative analysis offers valuable insights for optimizing task scheduling in cloud infrastructure.