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Cluster Computing

, Volume 20, Issue 3, pp 1937–1950 | Cite as

A cloud-based enhanced differential evolution algorithm for parameter estimation problems in computational systems biology

  • Diego Teijeiro
  • Xoán C. Pardo
  • David R. Penas
  • Patricia González
  • Julio R. Banga
  • Ramón Doallo
Article

Abstract

Metaheuristics are gaining increasing recognition in many research areas, computational systems biology among them. Recent advances in metaheuristics can be helpful in locating the vicinity of the global solution in reasonable computation times, with Differential Evolution (DE) being one of the most popular methods. However, for most realistic applications, DE still requires excessive computation times. With the advent of Cloud Computing effortless access to large number of distributed resources has become more feasible, and new distributed frameworks, like Spark, have been developed to deal with large scale computations on commodity clusters and cloud resources. In this paper we propose a parallel implementation of an enhanced DE using Spark. The proposal drastically reduces the execution time, by means of including a selected local search and exploiting the available distributed resources. The performance of the proposal has been thoroughly assessed using challenging parameter estimation problems from the domain of computational systems biology. Two different platforms have been used for the evaluation, a local cluster and the Microsoft Azure public cloud. Additionally, it has been also compared with other parallel approaches, another cloud-based solution (a MapReduce implementation) and a traditional HPC solution (a MPI implementation)

Keywords

Parallel metaheuristics Differential evolution Local search Cloud computing Spark 

Notes

Acknowledgements

This research received financial support from the Spanish Government (and the FEDER) through the projects DPI2014-55276-C5-2-R, TIN2013-42148-P and TIN2016-75845-P, and from the Galician Government under the Consolidation Program of Competitive Research Units (Network Ref. R2016/045 and Project Ref. GRC2013/055), all of them cofunded by FEDER funds of the EU. We also acknowledge Microsoft Research for being awarded with a sponsored Azure account.

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Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  1. 1.Grupo de Arquitectura de ComputadoresUniversidade da CoruñaA CoruñaSpain
  2. 2.BioProcess Engineering GroupIIM-CSICVigoSpain

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