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Deadline Sensitive Lease Scheduling Using Hungarian Genetic Algorithm in Cloud Computing Environment

Duraksha Ali1 , Manoj Kumar Gupta2

Section:Research Paper, Product Type: Journal Paper
Volume-7 , Issue-12 , Page no. 7-15, Dec-2019

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v7i12.715

Online published on Dec 31, 2019

Copyright © Duraksha Ali, Manoj Kumar Gupta . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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IEEE Style Citation: Duraksha Ali, Manoj Kumar Gupta, “Deadline Sensitive Lease Scheduling Using Hungarian Genetic Algorithm in Cloud Computing Environment,” International Journal of Computer Sciences and Engineering, Vol.7, Issue.12, pp.7-15, 2019.

MLA Style Citation: Duraksha Ali, Manoj Kumar Gupta "Deadline Sensitive Lease Scheduling Using Hungarian Genetic Algorithm in Cloud Computing Environment." International Journal of Computer Sciences and Engineering 7.12 (2019): 7-15.

APA Style Citation: Duraksha Ali, Manoj Kumar Gupta, (2019). Deadline Sensitive Lease Scheduling Using Hungarian Genetic Algorithm in Cloud Computing Environment. International Journal of Computer Sciences and Engineering, 7(12), 7-15.

BibTex Style Citation:
@article{Ali_2019,
author = {Duraksha Ali, Manoj Kumar Gupta},
title = {Deadline Sensitive Lease Scheduling Using Hungarian Genetic Algorithm in Cloud Computing Environment},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {12 2019},
volume = {7},
Issue = {12},
month = {12},
year = {2019},
issn = {2347-2693},
pages = {7-15},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=4966},
doi = {https://doi.org/10.26438/ijcse/v7i12.715}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v7i12.715}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=4966
TI - Deadline Sensitive Lease Scheduling Using Hungarian Genetic Algorithm in Cloud Computing Environment
T2 - International Journal of Computer Sciences and Engineering
AU - Duraksha Ali, Manoj Kumar Gupta
PY - 2019
DA - 2019/12/31
PB - IJCSE, Indore, INDIA
SP - 7-15
IS - 12
VL - 7
SN - 2347-2693
ER -

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Abstract

OpenNebula, a cloud platform handles a variety of leases employing scheduler, Haizea and majority of them are deadline-sensitive in real time. As existing Backfilling AHP model for deadline-sensitive lease scheduling suffers from lease rejection and do not scrutinize the estimations for waiting leases. In our proposed work, to overcome this pitfall we have devised Hungarian-Genetic Algorithm (HGA). Time Estimations for leases are performed using optimized Hungarian Algorithm to optimally render resources to available leases but it executes boundlessly. Thus, it’s blended with Genetic Algorithm to set bounds to it by utilizing fitness function. Output of HGA is a scheduling structure with optimal lease combination which consumes minimum time. Finally HGA is compared with Backfilling AHP model and HGA schedules greater quota of leases and minimizes lease ostracism comparatively. Also proposed model works fine on increasing number of leases as computational time is not directly proportional to number of leases scheduled.

Key-Words / Index Term

Deadline sensitive, Resource allocation, Leases, Lease scheduling, Cloud computing

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