Fuzzy System Modeling for Task Scheduling in Computational Grids

Moura, Bruno; Soares, Yan; Sampaio, Leticia; Reiser, Renata; Yamin, Adenauer; Pilla, Maurı́cio L.

doi:10.1142/9789813146976_0125

Cited by 3 publications

(1 citation statement)

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“…The fuzzy approach uses fuzzy set theory to model imprecision in task scheduling. Moura et al [13] proposed fGrid using fuzzy logic to treat uncertainty, and Fayad et al proposed a fuzzy grid scheduler using tabu search algorithm [14]. Also, the TVM-Fuzzy scheduler described in [15] minimizes the application makespan under uncertain bandwidth condition.…”

Section: Related Workmentioning

confidence: 99%

A Robust Algorithm for Deadline Constrained Scheduling in IaaS Cloud Environment

Muhammad-Bello

Aritsugi

2018

IEICE Trans. Inf. & Syst.

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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 costoptimized, 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. key words:handling resource performance uncertainty, deadlineconstrained workflow scheduling, IaaS cloud environment, robust scheduling, uncertain processing time

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Section: Related Workmentioning

confidence: 99%