Evolutionary Inheritance in Workflow Scheduling Algorithms within Dynamically Changing Heterogeneous Environments

Nikolay Butakov, Denis Nasonov, Alexander Boukhanovsky

Abstract

State-of-the-art distributed computational environments requires increasingly flexible and efficient workflow scheduling procedures in order to satisfy the increasing requirements of the scientific community. In this paper, we present a novel, nature-inspired scheduling approach based on the leveraging of inherited populations in order to increase the quality of generated planning solutions for the occurrence of system events such as a computational resources crash or a task delay with the rescheduling phase .The proposed approach is based on a hybrid algorithm which was described in our previous work and includes strong points of list-based heuristics and evolutionary meta-heuristics principles. In this paper we also experimentally show that the proposed extension of hybrid algorithms generates more effective solutions than the basic one in dynamically heterogeneous computational changing environments.

References

  1. Arabnejad, Hamid. "List Based Task Scheduling Algorithms on Heterogeneous Systems-An overview." (2013)
  2. Casanova, Henri, et al. "Heuristics for scheduling parameter sweep applications in grid environments." Heterogeneous Computing Workshop, 2000.(HCW 2000) Proceedings. 9th. IEEE, 2000.
  3. Cochran, Jeffery K., Shwu-Min Horng, and John W. Fowler. "A multi-population genetic algorithm to solve multi-objective scheduling problems for parallel machines." Computers & Operations Research 30.7 (2003): 1087-1102.
  4. Deelman, Ewa, et al. "Pegasus: Mapping scientific workflows onto the grid." Grid Computing. Springer Berlin Heidelberg, 2004.
  5. Graham, R.W. Dinosaurs: old bones and living animals. Living Museum 56:35-37. 1994.
  6. Jakob, Wilfried, et al. "Fast rescheduling of multiple workflows to constrained heterogeneous resources using multi-criteria memetic computing." Algorithms 6.2 (2013): 245-277.
  7. Liu, Xiao, et al. "Handling recoverable temporal violations in scientific workflow systems: a workflow rescheduling based strategy." Proceedings of the 2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing. IEEE Computer Society, 2010.
  8. Nasonov, Denis, et al. "Hybrid Evolutionary Workflow Scheduling Algorithm for Dynamic Heterogeneous Distributed Computational Environment." International Joint Conference SOCO'14-CISIS'14- ICEUTE'14. Springer International Publishing, 2014.
  9. Nguyen, Trung Thanh, Shengxiang Yang, and Juergen Branke. "Evolutionary dynamic optimization: A survey of the state of the art." Swarm and Evolutionary Computation 6 (2012): 1-24.
  10. Rahman, Mustafizur, et al. "Adaptive workflow scheduling for dynamic grid and cloud computing environment." Concurrency and Computation: Practice and Experience 25.13 (2013): 1816-1842.
  11. Rohlfshagen, Philipp, and Xin Yao. "On the role of modularity in evolutionary dynamic optimisation." Evolutionary Computation (CEC), 2010 IEEE Congress on. IEEE, 2010.
  12. Singh L., Singh. S A Survey of Workflow Scheduling Algorithms and Research Issues. - International Journal of Computer Applications. -V.74, No 15. - 2013.
  13. Sinnen, Oliver. Task scheduling for parallel systems. Vol. 60. John Wiley & Sons, 2007.- p. 108.
  14. Topcuoglu, Haluk, Salim Hariri, and Min-you Wu. "Performance-effective and low-complexity task scheduling for heterogeneous computing." Parallel and Distributed Systems, IEEE Transactions on 13.3 (2002): 260-274.
  15. Xhafa, Fatos, et al. "Efficient batch job scheduling in grids using cellular memetic algorithms." Metaheuristics for Scheduling in Distributed Computing Environments. Springer Berlin Heidelberg, 2008. 273-299.
  16. Yang, Shengxiang, and Xin Yao. "Population-based incremental learning with associative memory for dynamic environments." Evolutionary Computation, IEEE Transactions on 12.5 (2008): 542-561.
  17. Yu, Jia, and Rajkumar Buyya. "Scheduling scientific workflow applications with deadline and budget constraints using genetic algorithms." Scientific Programming 14.3 (2006): 217-230.
  18. Zimmer Carl, and Douglas John Emlen. Evolution: Making Sense of Life. Roberts, 2013.
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Paper Citation


in Harvard Style

Butakov N., Nasonov D. and Boukhanovsky A. (2014). Evolutionary Inheritance in Workflow Scheduling Algorithms within Dynamically Changing Heterogeneous Environments . In Proceedings of the International Conference on Evolutionary Computation Theory and Applications - Volume 1: ECTA, (IJCCI 2014) ISBN 978-989-758-052-9, pages 160-168. DOI: 10.5220/0005035201600168


in Bibtex Style

@conference{ecta14,
author={Nikolay Butakov and Denis Nasonov and Alexander Boukhanovsky},
title={Evolutionary Inheritance in Workflow Scheduling Algorithms within Dynamically Changing Heterogeneous Environments},
booktitle={Proceedings of the International Conference on Evolutionary Computation Theory and Applications - Volume 1: ECTA, (IJCCI 2014)},
year={2014},
pages={160-168},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005035201600168},
isbn={978-989-758-052-9},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Evolutionary Computation Theory and Applications - Volume 1: ECTA, (IJCCI 2014)
TI - Evolutionary Inheritance in Workflow Scheduling Algorithms within Dynamically Changing Heterogeneous Environments
SN - 978-989-758-052-9
AU - Butakov N.
AU - Nasonov D.
AU - Boukhanovsky A.
PY - 2014
SP - 160
EP - 168
DO - 10.5220/0005035201600168