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Greedy Firefly Algorithm for Optimizing Job Scheduling in IoT Grid Computing.

Adil Yousif¹, Samar M Alqhtani², Mohammed Bakri Bashir^3,4

¹Department of Computer Science, College of Science and Arts-Sharourah, Najran University, Sharourah 68341, Saudi Arabia.

Sensors (Basel, Switzerland)

|February 15, 2022

Summary

A new Greedy Firefly Algorithm (GFA) optimizes job scheduling for the Internet of Things (IoT) integrated with grid computing. This method enhances computational efficiency and speeds up convergence for large-scale IoT environments.

Keywords:

IoT firefly algorithm greedy grid job scheduling

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Area of Science:

Computer Science
Distributed Computing
Artificial Intelligence

Background:

The Internet of Things (IoT) involves interconnected devices transmitting data, necessitating efficient integration with grid computing for complex problem-solving.
Job scheduling in heterogeneous IoT grid environments is an NP-hard challenge, requiring advanced computational techniques.
Existing scheduling methods face limitations in managing the scale and complexity of IoT grid resources.

Purpose of the Study:

To propose and evaluate a novel Greedy Firefly Algorithm (GFA) for optimizing job scheduling in IoT grid environments.
To enhance the convergence rate and efficiency of scheduling solutions by incorporating a greedy local search mechanism.
To assess the performance of GFA against existing methods using various grid computing workload traces.

Main Methods:

Development of the Greedy Firefly Algorithm (GFA), integrating a greedy approach into the standard firefly algorithm for local search.
Implementation and simulation using the GridSim toolkit to evaluate GFA performance.
Testing with diverse workload sizes, including lightweight (500 jobs), typical (3000-7000 jobs), and heavy load (8000-10,000 jobs).

Main Results:

The Greedy Firefly Algorithm (GFA) demonstrated a significant reduction in makespan and execution times for IoT grid scheduling.
GFA exhibited faster convergence in large search spaces compared to other evaluated scheduling methods.
The proposed algorithm proved effective in handling large-scale IoT grid environments with complex computational demands.

Conclusions:

The Greedy Firefly Algorithm (GFA) offers an effective solution for optimizing job scheduling in integrated IoT and grid computing systems.
GFA's enhanced convergence and efficiency make it suitable for large-scale, resource-intensive IoT applications.
The study highlights the potential of metaheuristic algorithms like GFA in addressing complex scheduling challenges in distributed computing environments.