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Fault-tolerant allocation of deadline-constrained tasks through preemptive migration in heterogeneous cloud environments

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Abstract

In recent years, the occurrence of task failures are becoming prevalent in cloud computing due to various factors such as the increasing complexity of cloud environments, heterogeneity of resources, resource limitations and inadequate allocation. Task failure due to insufficient allocation poses a significant challenge in cloud computing. When tasks are not allocated effectively, they may not be completed within their deadlines which ultimately leads to failure. Hence, effective allocation strategies combined with appropriate fault tolerance measures are vital for addressing these challenges and mitigating the risk of task failures. This paper proposes a fault-tolerant task allocation algorithm (FTTA) for independent tasks with deadline through preemptive migration in heterogeneous cloud environments to reduce task failure. The proposed algorithm involves three phases: the initial phase decides the priority of tasks in the ready list to minimize the execution time and meet task deadlines, the second phase includes the selection of a suitable virtual machine with minimum execution time and the last phase assigns task on available or non-available (which may available in future) virtual machines to find the best execution time within the deadline limit. During the task allocation process, the algorithm adopts fault-tolerant strategy that includes preemptive migration if necessary which allows the migration of tasks to identify the best suitable virtual machine. An analysis of the proposed algorithm reveals that the overall time complexity is \(O(n\log n + n m^2)\) where n is the number of tasks and m is the number of virtual machines. Further, the performance of the algorithm is evaluated for different sets of tasks (small to large) while varying the number of virtual machines. The experimental results demonstrate that FTTA outperforms First Come First Served (FCFS), Priority based algorithm, Shortest Job First (SJF), Dynamic Maximum Minimum (Dy max min) and RADL algorithms in terms of number of rejected tasks, makespan, speedup and efficiency.

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  1. Department of Computer Science and Engineering, Motilal Nehru National Institute of Technology Allahabad, Prayagraj, 211004, India

    Medha Kirti, Ashish Kumar Maurya & Rama Shankar Yadav

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  1. Medha Kirti
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Kirti, M., Maurya, A.K. & Yadav, R.S. Fault-tolerant allocation of deadline-constrained tasks through preemptive migration in heterogeneous cloud environments. Cluster Comput (2024). https://doi.org/10.1007/s10586-024-04538-9

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