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HFTO: Hybrid Firebug Tunicate Optimizer for Fault Tolerance and Dynamic Task Scheduling in Cloud Computing

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Abstract

Task scheduling is an important issue in cloud computing when it comes to achieving multiple goals and satisfying different user needs. The increasing demand and users urge the necessity to minimize the task completion time and enhance the load balancing capacity. To achieve this goal, this article proposes a Hybrid firebug and Tunicate Optimization (HFTO) algorithm. Based on the previous scheduling information, the HFTO classifier classifies the task and creates different variants of Virtual Machine (VM). This step helps to minimize the time taken for VM creation. The proposed HFTO task scheduling framework aims at optimizing different Quality of Service (QoS) parameters such as fault tolerance, response time, efficiency, and makespan. The optimization algorithm helps to expand the search space of the solutions and frames an optimal task scheduling strategy for the virtual machines. The HFTO optimization method has several advantages, including enhanced search capability and faster convergence. The HFTO algorithm improves the fault tolerance capability by allocating the tasks to appropriate resources based on the resource load peak. The lightweight tasks can be allocated to the resources with high CPU utilization and the computation-intensive tasks can be allocated to the resources with low CPU utilization. The response time and execution time are improved by task pre-emption. Hence the time complexity and computational complexity can be improved by the HFTO algorithm even with limited resource capability. The experiments are conducted using the CloudSim experimental platform and the results are compared to the state-of-art techniques. The performance of the proposed methodology is evaluated in terms of different performance metrics namely makespan, load balancing, and average execution time. The results show that, when compared to existing techniques, the proposed methodology provides higher load balancing efficiency and improved cloud task scheduling performance.

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Data sharing is not applicable to this article as no new data were created or analyzed in this study.

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Authors and Affiliations

  1. Department of Data science and Business Systems, School of Computing, College of Engineering and Technology, Faculty of Engineering and Technology, SRM Institute of Science and Technology, SRM Nagar, Kattankulathur, Chennai, Tamilnadu, India

    Manikandan Nanjappan

  2. Department of Information Technology, Sri Venkateswara College of Engineering, Chennai, Tamilnadu, India

    Gobalakrishnan Natesan

  3. School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, Tamilnadu, India

    Pradeep Krishnadoss

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  1. Manikandan Nanjappan
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  2. Gobalakrishnan Natesan
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  3. Pradeep Krishnadoss
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Contributions

All authors agreed on the content of the study. MN,GN and PK collected all the data for analysis. MN agreed on the methodology. MN,GN and PK completed the analysis based on agreed steps. Results and conclusions arediscussed and written together. The author read and approved the final manuscript.

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Correspondence to Manikandan Nanjappan.

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Nanjappan, M., Natesan, G. & Krishnadoss, P. HFTO: Hybrid Firebug Tunicate Optimizer for Fault Tolerance and Dynamic Task Scheduling in Cloud Computing. Wireless Pers Commun 129, 323–344 (2023). https://doi.org/10.1007/s11277-022-10099-0

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