Multi-objective workflow scheduling in Amazon EC2

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 increasin...

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Vydáno v:Cluster computing Ročník 17; číslo 2; s. 169 - 189
Hlavní autoři: Durillo, Juan J., Prodan, Radu
Médium: Journal Article
Jazyk:angličtina
Vydáno: Boston Springer US 01.06.2014
Springer Nature B.V
Témata:
Algorithms
Cloud computing
Computer Communication Networks
Computer Science
Distributed processing
Economic impact
Genetic algorithms
Multiple objective analysis
Objectives
Operating Systems
Pareto optimization
Processor Architectures
Schedules
Scheduling
Tradeoffs
User requirements
Workflow
List-based heuristics
Workflow scheduling
Cloud
Multi-objective optimisation
ISSN:1386-7857, 1573-7543
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Shrnutí: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 multi-objective workflow scheduling on the commercial Amazon EC2 Cloud. We compare the quality of the MOHEFT tradeoff solutions with two state-of-the-art 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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ISSN:1386-7857
1573-7543
DOI:10.1007/s10586-013-0325-0