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DFARM: a deadline-aware fault-tolerant scheduler for cloud computing

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Abstract

Cloud computing has become popular for small businesses due to its cost-effectiveness and the ability to acquire necessary on-demand services, including software, hardware, network, etc., anytime around the globe. Efficient job scheduling in the Cloud is essential to optimize operational costs in data centers. Therefore, scheduling should consider assigning tasks to Virtual Machines (VMs) in a Cloud environment in such a manner that could speed up execution, maximize resource utilization, and meet users’ SLA and other constraints such as deadlines. For this purpose, the tasks can be prioritized based on their deadlines and task lengths, and the resources could be provisioned and released as needed. Moreover, to cope with unexpected execution situations or hardware failures, a fault-tolerance mechanism could be employed based on hybrid replication and the re-submission method. Most of the existing techniques tend to improve performance. However, their pitfall lies in certain aspects such as either those techniques prioritize tasks based on a singular value (e.g., usually deadline), only utilize a singular fault tolerance mechanism, or try to release resources that cause more overhead immediately. This research work proposes a new scheduler called the Deadline and fault-aware task Adjusting and Resource Managing (DFARM) scheduler, the scheduler dynamically acquires resources and schedules deadline-constrained tasks by considering both their length and deadlines while providing fault tolerance through the hybrid replication–resubmission method. Besides acquiring resources, it also releases resources based on their boot time to lessen costs due to reboots. The performance of the DFARM scheduler is compared to other scheduling algorithms, such as Random Selection, Round Robin, Minimum Completion Time, RALBA, and OG-RADL. With a comparable execution performance, the proposed DFARM scheduler reduces task-rejection rates by 2.34–9.53 times compared to the state-of-the-art schedulers using two benchmark datasets.

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No datasets were generated or analysed during the current study.

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Acknowledgements

The European Union (Horizon Europe Graph-Massivizer, 101093202) and the Austrian Research Promotion Agency (FFG Kärntner Fog, 888098) funded this work.

Funding

The European Union (Horizon Europe Graph-Massivizer, 101093202) and the Austrian Research Promotion Agency (FFG Kärntner Fog, 888098) funded this work.

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Authors and Affiliations

  1. Department of Computer Science, National University of Computer and Emerging Sciences, Islamabad, Pakistan

    Ahmad Awan & Muhammad Aleem

  2. Department of Computer Science, KICSIT Kahuta Campus, Institute of Space Technology, Islamabad, Pakistan

    Altaf Hussain

  3. Institute of Information Technology, Alpen-Adria-Universität Klagenfurt, Klagenfurt, Austria

    Radu Prodan

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  1. Ahmad Awan
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  2. Muhammad Aleem
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  3. Altaf Hussain
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  4. Radu Prodan
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Contributions

Ahmad Awan: literature review, implementation, detailed solution design, experimentations. Muhammad Aleem: idea formulation, supervision, detailed solution design. Altaf Hussain: proposed architecture, draft review, experimentation validation. Radu Prodan: proposed architecture, experimental plan and design, writeup review.

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Correspondence to Muhammad Aleem.

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Awan, A., Aleem, M., Hussain, A. et al. DFARM: a deadline-aware fault-tolerant scheduler for cloud computing. Cluster Comput (2024). https://doi.org/10.1007/s10586-024-04419-1

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