Multi-objective Optimal Grid Workflow Scheduling with QoS Constraints

Jin-tao, Zeng; Jie-wu, Xia; Li, Jinzhong; Man-hua, LI

doi:10.1007/978-3-642-03664-4_91

Cited by 1 publication

(1 citation statement)

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“…This proposal optimises for makespan and energy consumption in Cloud systems. In [26], an enhanced version of the clasical multi-objective genetic algorithm, NSGA-II, is presented. This proposal includes the use of Quality of Service (QoS) constraints and is targeted to optimize five different QoS parameters.…”

Section: Related Workmentioning

confidence: 99%

Multi-objective workflow scheduling in Amazon EC2

2013

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Nowadays, scientists and companies are confronted with multiple competing goals such as makespan in high-performance computing and economic cost in Clouds that have to be simultaneously optimised. Multi-objective scheduling of scientific applications in these systems is therefore receiving increasing research attention. Most existing approaches typically aggregate all objectives in a single function, defined a-priori without any knowledge about the problem being solved, which negatively impacts the quality of the solutions. In contrast, Pareto-based approaches having as outcome a set of (nearly) optimal solutions that represent a tradeoff among the different objectives, have been scarcely studied. In this paper, we analyse MOHEFT, a Pareto-based list scheduling heuristic that provides the user with a set of tradeoff optimal solutions from which the one that better suits the user requirements can be manually selected. We demonstrate the potential of our method for multiobjective workflow scheduling on the commercial Amazon EC2 Cloud. We compare the quality of the MOHEFT tradeoff solutions with two state-of-theart approaches using different synthetic and real-world workflows: the classical HEFT algorithm for single-objective scheduling and the SPEA2* genetic algorithm used in multi-objective optimisation problems. The results demonstrate that our approach is able to compute solutions of higher quality than SPEA2*. In addition, we show that MOHEFT is more suitable than SPEA2* for workflow scheduling in the context of commercial Clouds, since the genetic-based approach is unable of dealing with some of the constraints imposed by these systems.

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Section: Related Workmentioning

confidence: 99%