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Best-KFF: a multi-objective preemptive resource allocation policy for cloud computing systems

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Abstract

Resource provisioning is a key issue in large-scale distributed systems such as cloud computing systems. Several resource provider systems utilized preemptive resource allocation techniques to maintain a high quality of service level. When there is a lack of resources for high-priority requests, leases/jobs with higher priority can run by suspending or canceling leases/jobs with lower priority to release the required resources. The state-of-the-art preemptive resource allocation methods are classified into two classes, namely, (1) heuristic and (2) brute force. The heuristic-based methods are fast, but they can’t maintain the system performance, while brute force-based methods are vice versa. In this work, we proposed a new multi-objective preemptive resource allocation policy that benefits from these two classes. We proposed a new heuristic called Best K-First-Fit (Best-KFF). The Best-KFF searches for the first k preemption choices at each physical machine (PM) and then sorts these preemption choices obtained from the PMs with respect to several objectives (e.g., resource utilization). Then, the Best-KFF selects the best choice maintaining the cloud computing system performance. Thus, the Best-KFF algorithm is a compromise between the heuristic and brute force classes. The higher the value of k is, the larger the search space is. The Best-KFF method maximizes the resource utilization of the physical machines and minimizes the average waiting time of advanced-reservation requests, the number of lease preemption, the preemption time, and energy consumption. The proposed method was thoroughly examined and compared against the state-of-the-art methods. The experimental results on various cloud computing systems demonstrated that the proposed preemption policy outperforms the state-of-the-art methods.

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Data availability

The data that support the findings of this study are openly available in http://www.cs.huji.ac.il/labs/parallel/workload/.

Notes

  1. http://www.cs.huji.ac.il/labs/parallel/workload/

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Acknowledgements

This work is supported in part by Foundation of China under Grant Nos. 61702170, 61602350, 61602170, and 61750110531.

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Author notes

  1. Ahmed Fathalla, Kenli Li, and Ahmad Salah contributted equally.

Authors and Affiliations

  1. College of Information Science and Engineering, Hunan University, and the National Supercomputing Center in Changsha, Changsha, Hunan, China

    Ahmed Fathalla & Kenli Li

  2. Department of Mathematics, Faculty of Science, Suez Canal University, Ismailia, Egypt

    Ahmed Fathalla

  3. Department of Computer Science, Faculty of Computers and Informatics, Zagazig University, Sharkia, Egypt

    Ahmad Salah

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  1. Ahmed Fathalla
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  2. Kenli Li
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  3. Ahmad Salah
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Correspondence to Ahmed Fathalla or Kenli Li.

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Fathalla, A., Li, K. & Salah, A. Best-KFF: a multi-objective preemptive resource allocation policy for cloud computing systems. Cluster Comput 25, 321–336 (2022). https://doi.org/10.1007/s10586-021-03407-z

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  • DOI: https://doi.org/10.1007/s10586-021-03407-z

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