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Dynamic Backfilling Algorithm to Increase Resource Utilization in Cloud Computing

Dynamic Backfilling Algorithm to Increase Resource Utilization in Cloud Computing

Suvendu Chandan Nayak, Sasmita Parida, Chitaranjan Tripathy, Prasant Kumar Pattnaik
Copyright: © 2019 |Volume: 14 |Issue: 1 |Pages: 26
ISSN: 1554-1045|EISSN: 1554-1053|EISBN13: 9781522564485|DOI: 10.4018/IJITWE.2019010101
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MLA

Nayak, Suvendu Chandan, et al. "Dynamic Backfilling Algorithm to Increase Resource Utilization in Cloud Computing." IJITWE vol.14, no.1 2019: pp.1-26. http://doi.org/10.4018/IJITWE.2019010101

APA

Nayak, S. C., Parida, S., Tripathy, C., & Pattnaik, P. K. (2019). Dynamic Backfilling Algorithm to Increase Resource Utilization in Cloud Computing. International Journal of Information Technology and Web Engineering (IJITWE), 14(1), 1-26. http://doi.org/10.4018/IJITWE.2019010101

Chicago

Nayak, Suvendu Chandan, et al. "Dynamic Backfilling Algorithm to Increase Resource Utilization in Cloud Computing," International Journal of Information Technology and Web Engineering (IJITWE) 14, no.1: 1-26. http://doi.org/10.4018/IJITWE.2019010101

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Abstract

In this article, the authors propose a novel backfilling-based task scheduling algorithm to schedule deadline-based tasks. The existing backfilling algorithm has some performance issues in comparison with the number of task scheduling in OpenNebula cloud platform. A lease could not be scheduled if it is not sorted with respect to its start time. In backfilling, a lease is selected in First Come First Serve (FCFS) to be backfilled from the queue in which some ideal resources can be found out and allocated to other leases. However, the scheduling performance is not better if there are similar types of leases to backfill. It requires a decision maker to resolve conflicts. The proposed approach schedules the number of tasks without any decision maker. An additional queue and the current time of the system is implemented to improve the scheduling performance. It performs quite satisfactorily in terms of number of a leases scheduling, and resource utilization. The performance result is compared with the existing backfilling algorithms.

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