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An Intelligent Chimp Optimizer for Scheduling of IoT Application Tasks in Fog Computing

Author

Listed:
  • Ibrahim Attiya

    (Department of Mathematics, Faculty of Science, Zagazig University, Zagazig 44519, Egypt)

  • Laith Abualigah

    (Faculty of Computer Sciences and Informatics, Amman Arab University, Amman 11953, Jordan
    School of Computer Sciences, Universiti Sains Malaysia, Pulau Pinang, George Town 11800, Malaysia)

  • Doaa Elsadek

    (Department of Mathematics, Faculty of Science, Zagazig University, Zagazig 44519, Egypt)

  • Samia Allaoua Chelloug

    (Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia)

  • Mohamed Abd Elaziz

    (Faculty of Computer Science & Engineering, Galala University, Suze 435611, Egypt
    Artificial Intelligence Research Center (AIRC), Ajman University, Ajman 346, United Arab Emirates
    Faculty of Science, Zagazig University, Zagazig 44519, Egypt)

Abstract

The cloud computing paradigm is evolving rapidly to address the challenges of new emerging paradigms, such as the Internet of Things (IoT) and fog computing. As a result, cloud services usage is increasing dramatically with the recent growth of IoT-based applications. To successfully fulfill application requirements while efficiently harnessing cloud computing power, intelligent scheduling approaches are required to optimize the scheduling of IoT application tasks on computing resources. In this paper, the chimp optimization algorithm (ChOA) is incorporated with the marine predators algorithm (MPA) and disruption operator to determine the optimal solution to IoT applications’ task scheduling. The developed algorithm, called CHMPAD, aims to avoid entrapment in the local optima and improve the exploitation capability of the basic ChOA as its main drawbacks. Experiments are conducted using synthetic and real workloads collected from the Parallel Workload Archive to demonstrate the applicability and efficiency of the presented CHMPAD method. The simulation findings reveal that CHMPAD can achieve average makespan time improvements of 1.12–43.20% (for synthetic workloads), 1.00–43.43% (for NASA iPSC workloads), and 2.75–42.53% (for HPC2N workloads) over peer scheduling algorithms. Further, our evaluation results suggest that our proposal can improve the throughput performance of fog computing.

Suggested Citation

  • Ibrahim Attiya & Laith Abualigah & Doaa Elsadek & Samia Allaoua Chelloug & Mohamed Abd Elaziz, 2022. "An Intelligent Chimp Optimizer for Scheduling of IoT Application Tasks in Fog Computing," Mathematics, MDPI, vol. 10(7), pages 1-18, March.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:7:p:1100-:d:782132
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    References listed on IDEAS

    as
    1. Giulio Biondi & Valentina Franzoni, 2020. "Discovering Correlation Indices for Link Prediction Using Differential Evolution," Mathematics, MDPI, vol. 8(11), pages 1-10, November.
    2. Shiyong Li & Wenzhe Li & Huan Liu & Wei Sun, 2021. "A Stackelberg Game Approach toward Migration of Enterprise Applications to the Cloud," Mathematics, MDPI, vol. 9(19), pages 1-18, September.
    3. Harwant Singh Arri & Ramandeep Singh & Sudan Jha & Deepak Prashar & Gyanendra Prasad Joshi & Ill Chul Doo, 2021. "Optimized Task Group Aggregation-Based Overflow Handling on Fog Computing Environment Using Neural Computing," Mathematics, MDPI, vol. 9(19), pages 1-15, October.
    Full references (including those not matched with items on IDEAS)

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    Cited by:

    1. Ibrahim Attiya & Laith Abualigah & Samah Alshathri & Doaa Elsadek & Mohamed Abd Elaziz, 2022. "Dynamic Jellyfish Search Algorithm Based on Simulated Annealing and Disruption Operators for Global Optimization with Applications to Cloud Task Scheduling," Mathematics, MDPI, vol. 10(11), pages 1-23, June.
    2. Raj, Saurav & Mahapatra, Sheila & Babu, Rohit & Verma, Sumit, 2023. "Hybrid intelligence strategy for techno-economic reactive power dispatch approach to ensure system security," Chaos, Solitons & Fractals, Elsevier, vol. 170(C).
    3. Laith Abualigah & Ali Diabat & Raed Abu Zitar, 2022. "Orthogonal Learning Rosenbrock’s Direct Rotation with the Gazelle Optimization Algorithm for Global Optimization," Mathematics, MDPI, vol. 10(23), pages 1-42, November.
    4. Prabhdeep Singh & Rajbir Kaur & Junaid Rashid & Sapna Juneja & Gaurav Dhiman & Jungeun Kim & Mariya Ouaissa, 2022. "A Fog-Cluster Based Load-Balancing Technique," Sustainability, MDPI, vol. 14(13), pages 1-14, June.
    5. Mohammad H. Nadimi-Shahraki & Shokooh Taghian & Seyedali Mirjalili & Laith Abualigah, 2022. "Binary Aquila Optimizer for Selecting Effective Features from Medical Data: A COVID-19 Case Study," Mathematics, MDPI, vol. 10(11), pages 1-24, June.

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