Abstract
In this work we apply the copula theory for modeling task dependence in a stochastic scheduling algorithm. Our previous work, as well as the majority of the existing related works, assume independence between the tasks involved, but this is not very realistic in many cases. In this paper we prove that, when task dependence exists, better results can be obtained when it is modeled. Our results show that the performance of the stochastic scheduler is significantly improved if we assume a certain level of task dependence: on average \(18\,\%\) of the energy consumption can be saved compared to the results of the deterministic scheduler, along with \(81\,\%\) of improved test cases, versus \(2.44\,\%\) average savings when task independence is assumed, along with \(50\,\%\) of improved test cases.
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References
Mathematica. http://www.wolfram.com/mathematica/
Abedi S, Riahy GH, Hosseinian SH, Farhadkhani M (2013) Improved stochastic modeling: an essential tool for power system scheduling in the presence of uncertain renewables
Banković Z, Lopez-Garcia P (2014) Stochastic vs. deterministic evolutionary algorithm-based allocation and scheduling for XMOS Chips. Neurocomputing 82–89
Davies R (2013) Random distributions. http://www.robertnz.net/
Deb K, Pratap A, Sameer A, Meyarivan T (2000) A fast elitist multi-objective genetic algorithm: Nsga-ii. IEEE Trans Evol Comput 6:182–197
Diana T (2011) Improving schedule reliability based on copulas: an application to five of the most congested US airports. J Air Transp Manag 17(5):284–287
Gong C, Wang X, Xu W, Tajer A (2013) Distributed real-time energy scheduling in smart grid: stochastic model and fast optimization. IEEE Trans Smart Grid 4(3):1476–1489
Gonzalez-Fernandez Y, Soto M (2014) copulaedas: an r package for estimation of distribution algorithms based on copulas. J Stat Softw 58(9):1–34
Mathematica. MathLink Reference Guide, 1993
McNeil AJ, Frey R, Embrechts P (2010) Quantitative risk management: concepts, techniques, and tools. Princeton Series in Finance. Princeton University Press, Princeton
McNeil AJ et al (2009) Multivariate archimedean copulas, \(d\)-monotone functions and \(l_1\)-norm symmetric distributions
Nelsen RB (2003) Properties and applications of copulas: a brief survey. In: First Brazilian conference on statistical modelling in insurance and finance, pp 10–28
Wu D, Song H, Li M, Cai C, Li J (2010) Modeling risk factors dependence using copula method for assessing software schedule risk. In: 2010 2nd International conference on software engineering and data mining (SEDM), pp 571–574
Acknowledgments
The research leading to these results has received funding from the European Union 7th Framework Programme under grant agreement 318337, ENTRA - Whole-Systems Energy Transparency, Spanish MINECO TIN’12-39391 StrongSoft and TIN’08-05624 DOVES projects, and Madrid TIC-1465 PROMETIDOS-CM project.
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Banković, Z., López-García, P. (2015). Improved Energy-Aware Stochastic Scheduling Based on Evolutionary Algorithms via Copula-Based Modeling of Task Dependences. In: Herrero, Á., Sedano, J., Baruque, B., Quintián, H., Corchado, E. (eds) 10th International Conference on Soft Computing Models in Industrial and Environmental Applications. Advances in Intelligent Systems and Computing, vol 368. Springer, Cham. https://doi.org/10.1007/978-3-319-19719-7_14
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DOI: https://doi.org/10.1007/978-3-319-19719-7_14
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