IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i19p11982-d922183.html
   My bibliography  Save this article

Optimization of Load Balancing and Task Scheduling in Cloud Computing Environments Using Artificial Neural Networks-Based Binary Particle Swarm Optimization (BPSO)

Author

Listed:
  • Mohammed I. Alghamdi

    (College of Computer Science and Information Technology, Department of Engineering and Computer Science, Al-Baha University, Al-Baha City 1988, Saudi Arabia)

Abstract

As more people utilize the cloud, more employment opportunities become available. With constraints such as a limited make-span, a high utilization rate of available resources, minimal execution costs, and a rapid turnaround time for scheduling, this becomes an NP-hard optimization issue. The number of solutions/combinations increases exponentially with the magnitude of the challenge, such as the number of tasks and the number of computing resources, making the task scheduling problem NP-hard. As a result, achieving the optimum scheduling of user tasks is difficult. An intelligent resource allocation system can significantly cut down the costs and waste of resources. For instance, binary particle swarm optimization (BPSO) was created to combat ineffective heuristic approaches. However, the optimal solution will not be produced if these algorithms are not paired with additional heuristic or meta-heuristic algorithms. Due to the high temporal complexity of these algorithms, they are less useful in real-world settings. For the NP problem, the binary variation of PSO is presented for workload scheduling and balancing in cloud computing. Considering the updating and optimization constraints stated in this research, our objective function determines if heterogeneous virtual machines (VMs) Phave the most significant difference in completion time. In conjunction with load balancing, we developed a method for updating the placements of particles. According to the experiment results, the proposed method surpasses existing metaheuristic and heuristic algorithms regarding work scheduling and load balancing. This level of success has been attainable because of the application of Artificial Neural Networks (ANN). ANN has demonstrated promising outcomes in resource distribution. ANN is more accurate and faster than multilayer perceptron networks at predicting targets.

Suggested Citation

  • Mohammed I. Alghamdi, 2022. "Optimization of Load Balancing and Task Scheduling in Cloud Computing Environments Using Artificial Neural Networks-Based Binary Particle Swarm Optimization (BPSO)," Sustainability, MDPI, vol. 14(19), pages 1-20, September.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:19:p:11982-:d:922183
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/19/11982/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/14/19/11982/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Mohammed Shuaib & Sumit Badotra & Muhammad Irfan Khalid & Abeer D. Algarni & Syed Sajid Ullah & Sami Bourouis & Jawaid Iqbal & Salil Bharany & Lokesh Gundaboina, 2022. "A Novel Optimization for GPU Mining Using Overclocking and Undervolting," Sustainability, MDPI, vol. 14(14), pages 1-15, July.
    2. Shi, Jihao & Li, Junjie & Usmani, Asif Sohail & Zhu, Yuan & Chen, Guoming & Yang, Dongdong, 2021. "Probabilistic real-time deep-water natural gas hydrate dispersion modeling by using a novel hybrid deep learning approach," Energy, Elsevier, vol. 219(C).
    3. Salil Bharany & Sandeep Sharma & Surbhi Bhatia & Mohammad Khalid Imam Rahmani & Mohammed Shuaib & Saima Anwar Lashari, 2022. "Energy Efficient Clustering Protocol for FANETS Using Moth Flame Optimization," Sustainability, MDPI, vol. 14(10), pages 1-22, May.
    4. Salil Bharany & Sandeep Sharma & Sumit Badotra & Osamah Ibrahim Khalaf & Youseef Alotaibi & Saleh Alghamdi & Fawaz Alassery, 2021. "Energy-Efficient Clustering Scheme for Flying Ad-Hoc Networks Using an Optimized LEACH Protocol," Energies, MDPI, vol. 14(19), pages 1-20, September.
    5. Salil Bharany & Sandeep Sharma & Osamah Ibrahim Khalaf & Ghaida Muttashar Abdulsahib & Abeer S. Al Humaimeedy & Theyazn H. H. Aldhyani & Mashael Maashi & Hasan Alkahtani, 2022. "A Systematic Survey on Energy-Efficient Techniques in Sustainable Cloud Computing," Sustainability, MDPI, vol. 14(10), pages 1-89, May.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Arindita Saha & Puja Dash & Naladi Ram Babu & Tirumalasetty Chiranjeevi & Mudadla Dhananjaya & Łukasz Knypiński, 2022. "Dynamic Stability Evaluation of an Integrated Biodiesel-Geothermal Power Plant-Based Power System with Spotted Hyena Optimized Cascade Controller," Sustainability, MDPI, vol. 14(22), pages 1-26, November.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Akashdeep Bhardwaj & Keshav Kaushik & Mashael S. Maashi & Mohammed Aljebreen & Salil Bharany, 2022. "Alternate Data Stream Attack Framework to Perform Stealth Attacks on Active Directory Hosts," Sustainability, MDPI, vol. 14(19), pages 1-19, September.
    2. Keshav Kaushik & Akashdeep Bhardwaj & Salil Bharany & Naif Alsharabi & Ateeq Ur Rehman & Elsayed Tag Eldin & Nivin A. Ghamry, 2022. "A Machine Learning-Based Framework for the Prediction of Cervical Cancer Risk in Women," Sustainability, MDPI, vol. 14(19), pages 1-15, September.
    3. Manreet Sohal & Salil Bharany & Sandeep Sharma & Mashael S. Maashi & Mohammed Aljebreen, 2022. "A Hybrid Multi-Cloud Framework Using the IBBE Key Management System for Securing Data Storage," Sustainability, MDPI, vol. 14(20), pages 1-24, October.
    4. Edeh Michael Onyema & M. Anand Kumar & Sundaravadivazhagn Balasubaramanian & Salil Bharany & Ateeq Ur Rehman & Elsayed Tag Eldin & Muhammad Shafiq, 2022. "A Security Policy Protocol for Detection and Prevention of Internet Control Message Protocol Attacks in Software Defined Networks," Sustainability, MDPI, vol. 14(19), pages 1-19, September.
    5. Mohammed Shuaib & Sumit Badotra & Muhammad Irfan Khalid & Abeer D. Algarni & Syed Sajid Ullah & Sami Bourouis & Jawaid Iqbal & Salil Bharany & Lokesh Gundaboina, 2022. "A Novel Optimization for GPU Mining Using Overclocking and Undervolting," Sustainability, MDPI, vol. 14(14), pages 1-15, July.
    6. Supreet Kaur & Sandeep Sharma & Ateeq Ur Rehman & Elsayed Tag Eldin & Nivin A. Ghamry & Muhammad Shafiq & Salil Bharany, 2022. "Predicting Infection Positivity, Risk Estimation, and Disease Prognosis in Dengue Infected Patients by ML Expert System," Sustainability, MDPI, vol. 14(20), pages 1-20, October.
    7. Salil Bharany & Sandeep Sharma & Osamah Ibrahim Khalaf & Ghaida Muttashar Abdulsahib & Abeer S. Al Humaimeedy & Theyazn H. H. Aldhyani & Mashael Maashi & Hasan Alkahtani, 2022. "A Systematic Survey on Energy-Efficient Techniques in Sustainable Cloud Computing," Sustainability, MDPI, vol. 14(10), pages 1-89, May.
    8. Amit Sundas & Sumit Badotra & Salil Bharany & Ahmad Almogren & Elsayed M. Tag-ElDin & Ateeq Ur Rehman, 2022. "HealthGuard: An Intelligent Healthcare System Security Framework Based on Machine Learning," Sustainability, MDPI, vol. 14(19), pages 1-16, September.
    9. Akashdeep Bhardwaj & Keshav Kaushik & Salil Bharany & Ateeq Ur Rehman & Yu-Chen Hu & Elsayed Tag Eldin & Nivin A. Ghamry, 2022. "IIoT: Traffic Data Flow Analysis and Modeling Experiment for Smart IoT Devices," Sustainability, MDPI, vol. 14(21), pages 1-18, November.
    10. Shadab Alam & Mohammed Shuaib & Sadaf Ahmad & Dushantha Nalin K. Jayakody & Ammar Muthanna & Salil Bharany & Ibrahim A. Elgendy, 2022. "Blockchain-Based Solutions Supporting Reliable Healthcare for Fog Computing and Internet of Medical Things (IoMT) Integration," Sustainability, MDPI, vol. 14(22), pages 1-17, November.
    11. Sanjay Kumar & Rafeeq Ahmed & Salil Bharany & Mohammed Shuaib & Tauseef Ahmad & Elsayed Tag Eldin & Ateeq Ur Rehman & Muhammad Shafiq, 2022. "Exploitation of Machine Learning Algorithms for Detecting Financial Crimes Based on Customers’ Behavior," Sustainability, MDPI, vol. 14(21), pages 1-24, October.
    12. Satheeshkumar Palanisamy & Balakumaran Thangaraju & Osamah Ibrahim Khalaf & Youseef Alotaibi & Saleh Alghamdi & Fawaz Alassery, 2021. "A Novel Approach of Design and Analysis of a Hexagonal Fractal Antenna Array (HFAA) for Next-Generation Wireless Communication," Energies, MDPI, vol. 14(19), pages 1-18, September.
    13. Salil Bharany & Sandeep Sharma & Surbhi Bhatia & Mohammad Khalid Imam Rahmani & Mohammed Shuaib & Saima Anwar Lashari, 2022. "Energy Efficient Clustering Protocol for FANETS Using Moth Flame Optimization," Sustainability, MDPI, vol. 14(10), pages 1-22, May.
    14. Mudassir Khan & A. Ilavendhan & C. Nelson Kennedy Babu & Vishal Jain & S. B. Goyal & Chaman Verma & Calin Ovidiu Safirescu & Traian Candin Mihaltan, 2022. "Clustering Based Optimal Cluster Head Selection Using Bio-Inspired Neural Network in Energy Optimization of 6LowPAN," Energies, MDPI, vol. 15(13), pages 1-14, June.
    15. Chen, Jian & Song, Ye & Yu, Yueyang & Xiao, Guoqing & Tam, Wai Cheong & Kong, Depeng, 2022. "The influence of a plate obstacle on the burning behavior of small scale pool fires: An experimental study," Energy, Elsevier, vol. 254(PB).
    16. Na Huang & Xiang Li & Quanming Xu & Ronghao Chen & Huidong Chen & Aidong Chen, 2023. "Artificial Intelligence-Based Temperature Twinning and Pre-Control for Data Center Airflow Organization," Energies, MDPI, vol. 16(16), pages 1-15, August.
    17. Bi, Yubo & Wu, Qiulan & Wang, Shilu & Shi, Jihao & Cong, Haiyong & Ye, Lili & Gao, Wei & Bi, Mingshu, 2023. "Hydrogen leakage location prediction at hydrogen refueling stations based on deep learning," Energy, Elsevier, vol. 284(C).
    18. Du, Qiuwan & Yang, Like & Li, Liangliang & Liu, Tianyuan & Zhang, Di & Xie, Yonghui, 2022. "Aerodynamic design and optimization of blade end wall profile of turbomachinery based on series convolutional neural network," Energy, Elsevier, vol. 244(PA).
    19. Hemavathi & Sreenatha Reddy Akhila & Youseef Alotaibi & Osamah Ibrahim Khalaf & Saleh Alghamdi, 2022. "Authentication and Resource Allocation Strategies during Handoff for 5G IoVs Using Deep Learning," Energies, MDPI, vol. 15(6), pages 1-27, March.
    20. Kuruva Lakshmanna & Neelakandan Subramani & Youseef Alotaibi & Saleh Alghamdi & Osamah Ibrahim Khalafand & Ashok Kumar Nanda, 2022. "Improved Metaheuristic-Driven Energy-Aware Cluster-Based Routing Scheme for IoT-Assisted Wireless Sensor Networks," Sustainability, MDPI, vol. 14(13), pages 1-19, June.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:14:y:2022:i:19:p:11982-:d:922183. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.