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".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
As nuvens de infraestrutura como serviço (IaaS) estão emergindo como uma plataforma promissora para a execução de aplicativos de fluxo de trabalho que exigem recursos e exigem muita computação. Agendar a execução de aplicações científicas expressas como fluxos de trabalho em nuvens IaaS envolve muitas incertezas devido ao desempenho variável e imprevisível dos recursos da nuvem. Estas incertezas são modeladas por funções de distribuição de probabilidade em pesquisas anteriores ou totalmente ignoradas em alguns casos. Neste artigo, propomos um novo algoritmo robusto de agendamento de fluxo de trabalho com prazo limitado que lida com as incertezas no agendamento de fluxos de trabalho no ambiente IaaS Cloud. Nossa proposta é um algoritmo de escalonamento estático que visa abordar as incertezas relacionadas: à estimativa de tempos de execução de tarefas; e, o atraso no provisionamento de recursos computacionais da Nuvem. O problema de agendamento de fluxo de trabalho foi considerado um problema de otimização com custo otimizado e prazo limitado. Nossa estratégia de tratamento da incerteza baseou-se na consideração do conhecimento do intervalo de incerteza, que usamos para modelar os tempos de execução, em vez de usar uma função de distribuição de probabilidade conhecida ou estimativas precisas que são conhecidas por serem muito sensíveis a variações. Avaliações experimentais usando CloudSim com fluxos de trabalho sintéticos de vários tamanhos mostram que nossa proposta é robusta a flutuações nas estimativas de tempos de execução de tarefas e é capaz de produzir cronogramas de alta qualidade que possuem garantias de prazo com compensação mínima de custos de penalidade dependendo da duração do intervalo de incerteza. Soluções de agendamento para vários graus de incerteza resistiram a violações de prazos em tempo de execução, em oposição ao algoritmo estático IC-PCP que não poderia garantir restrições de prazos em face da incerteza.
Bilkisu Larai MUHAMMAD-BELLO
Kumamoto University,Information & Media Technology Dept. Federal University of Technology Minna
Masayoshi ARITSUGI
Kumamoto University
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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
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@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},}
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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 -