The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Awan Infrastruktur sebagai Perkhidmatan (IaaS) muncul sebagai platform yang menjanjikan untuk melaksanakan aplikasi aliran kerja intensif yang memerlukan sumber dan pengiraan. Menjadualkan pelaksanaan aplikasi saintifik yang dinyatakan sebagai aliran kerja pada IaaS Clouds melibatkan banyak ketidakpastian disebabkan oleh prestasi sumber Cloud yang berubah-ubah dan tidak dapat diramalkan. Ketidakpastian ini dimodelkan oleh fungsi taburan kebarangkalian dalam penyelidikan lepas atau diabaikan sama sekali dalam beberapa kes. Dalam makalah ini, kami mencadangkan algoritma penjadualan aliran kerja kekangan tarikh akhir yang teguh yang mengendalikan ketidakpastian dalam penjadualan aliran kerja dalam persekitaran Awan IaaS. Cadangan kami ialah algoritma penjadualan statik yang bertujuan untuk menangani ketidakpastian yang berkaitan dengan: anggaran masa pelaksanaan tugas; dan, kelewatan dalam menyediakan sumber Awan pengiraan. Masalah penjadualan aliran kerja dianggap sebagai masalah pengoptimuman yang dioptimumkan kos, dikekang oleh tarikh akhir. Strategi pengendalian ketidakpastian kami adalah berdasarkan pertimbangan pengetahuan tentang selang ketidakpastian, yang kami gunakan untuk memodelkan masa pelaksanaan daripada menggunakan fungsi pengedaran kebarangkalian yang diketahui atau anggaran tepat yang diketahui sangat sensitif kepada variasi. Penilaian eksperimen menggunakan CloudSim dengan aliran kerja sintetik pelbagai saiz menunjukkan bahawa cadangan kami adalah teguh kepada turun naik dalam anggaran masa jalan tugas dan mampu menghasilkan jadual berkualiti tinggi yang mempunyai jaminan tarikh akhir dengan pertukaran kos penalti yang minimum bergantung pada panjang selang masa ketidakpastian. Penyelesaian penjadualan untuk pelbagai tahap ketidakpastian menentang pelanggaran tarikh akhir pada masa jalan berbanding algoritma IC-PCP statik yang tidak dapat menjamin kekangan tarikh akhir dalam menghadapi ketidakpastian.
Bilkisu Larai MUHAMMAD-BELLO
Kumamoto University,Information & Media Technology Dept. Federal University of Technology Minna
Masayoshi ARITSUGI
Kumamoto University
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Salinan
Bilkisu Larai MUHAMMAD-BELLO, Masayoshi ARITSUGI, "A Robust Algorithm for Deadline Constrained Scheduling in IaaS Cloud Environment" in IEICE TRANSACTIONS on Information,
vol. E101-D, no. 12, pp. 2942-2957, December 2018, doi: 10.1587/transinf.2018PAP0016.
Abstract: The Infrastructure as a Service (IaaS) Clouds are emerging as a promising platform for the execution of resource demanding and computation intensive workflow applications. Scheduling the execution of scientific applications expressed as workflows on IaaS Clouds involves many uncertainties due to the variable and unpredictable performance of Cloud resources. These uncertainties are modeled by probability distribution functions in past researches or totally ignored in some cases. In this paper, we propose a novel robust deadline constrained workflow scheduling algorithm which handles the uncertainties in scheduling workflows in the IaaS Cloud environment. Our proposal is a static scheduling algorithm aimed at addressing the uncertainties related to: the estimation of task execution times; and, the delay in provisioning computational Cloud resources. The workflow scheduling problem was considered as a cost-optimized, deadline-constrained optimization problem. Our uncertainty handling strategy was based on the consideration of knowledge of the interval of uncertainty, which we used to modeling the execution times rather than using a known probability distribution function or precise estimations which are known to be very sensitive to variations. Experimental evaluations using CloudSim with synthetic workflows of various sizes show that our proposal is robust to fluctuations in estimates of task runtimes and is able to produce high quality schedules that have deadline guarantees with minimal penalty cost trade-off depending on the length of the interval of uncertainty. Scheduling solutions for varying degrees of uncertainty resisted against deadline violations at runtime as against the static IC-PCP algorithm which could not guarantee deadline constraints in the face of uncertainty.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2018PAP0016/_p
Salinan
@ARTICLE{e101-d_12_2942,
author={Bilkisu Larai MUHAMMAD-BELLO, Masayoshi ARITSUGI, },
journal={IEICE TRANSACTIONS on Information},
title={A Robust Algorithm for Deadline Constrained Scheduling in IaaS Cloud Environment},
year={2018},
volume={E101-D},
number={12},
pages={2942-2957},
abstract={The Infrastructure as a Service (IaaS) Clouds are emerging as a promising platform for the execution of resource demanding and computation intensive workflow applications. Scheduling the execution of scientific applications expressed as workflows on IaaS Clouds involves many uncertainties due to the variable and unpredictable performance of Cloud resources. These uncertainties are modeled by probability distribution functions in past researches or totally ignored in some cases. In this paper, we propose a novel robust deadline constrained workflow scheduling algorithm which handles the uncertainties in scheduling workflows in the IaaS Cloud environment. Our proposal is a static scheduling algorithm aimed at addressing the uncertainties related to: the estimation of task execution times; and, the delay in provisioning computational Cloud resources. The workflow scheduling problem was considered as a cost-optimized, deadline-constrained optimization problem. Our uncertainty handling strategy was based on the consideration of knowledge of the interval of uncertainty, which we used to modeling the execution times rather than using a known probability distribution function or precise estimations which are known to be very sensitive to variations. Experimental evaluations using CloudSim with synthetic workflows of various sizes show that our proposal is robust to fluctuations in estimates of task runtimes and is able to produce high quality schedules that have deadline guarantees with minimal penalty cost trade-off depending on the length of the interval of uncertainty. Scheduling solutions for varying degrees of uncertainty resisted against deadline violations at runtime as against the static IC-PCP algorithm which could not guarantee deadline constraints in the face of uncertainty.},
keywords={},
doi={10.1587/transinf.2018PAP0016},
ISSN={1745-1361},
month={December},}
Salinan
TY - JOUR
TI - A Robust Algorithm for Deadline Constrained Scheduling in IaaS Cloud Environment
T2 - IEICE TRANSACTIONS on Information
SP - 2942
EP - 2957
AU - Bilkisu Larai MUHAMMAD-BELLO
AU - Masayoshi ARITSUGI
PY - 2018
DO - 10.1587/transinf.2018PAP0016
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E101-D
IS - 12
JA - IEICE TRANSACTIONS on Information
Y1 - December 2018
AB - The Infrastructure as a Service (IaaS) Clouds are emerging as a promising platform for the execution of resource demanding and computation intensive workflow applications. Scheduling the execution of scientific applications expressed as workflows on IaaS Clouds involves many uncertainties due to the variable and unpredictable performance of Cloud resources. These uncertainties are modeled by probability distribution functions in past researches or totally ignored in some cases. In this paper, we propose a novel robust deadline constrained workflow scheduling algorithm which handles the uncertainties in scheduling workflows in the IaaS Cloud environment. Our proposal is a static scheduling algorithm aimed at addressing the uncertainties related to: the estimation of task execution times; and, the delay in provisioning computational Cloud resources. The workflow scheduling problem was considered as a cost-optimized, deadline-constrained optimization problem. Our uncertainty handling strategy was based on the consideration of knowledge of the interval of uncertainty, which we used to modeling the execution times rather than using a known probability distribution function or precise estimations which are known to be very sensitive to variations. Experimental evaluations using CloudSim with synthetic workflows of various sizes show that our proposal is robust to fluctuations in estimates of task runtimes and is able to produce high quality schedules that have deadline guarantees with minimal penalty cost trade-off depending on the length of the interval of uncertainty. Scheduling solutions for varying degrees of uncertainty resisted against deadline violations at runtime as against the static IC-PCP algorithm which could not guarantee deadline constraints in the face of uncertainty.
ER -