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2015 | 5 | 449--459
Tytuł artykułu

Synthesis of Power Aware Adaptive Schedulers for Embedded Systems using Developmental Genetic Programming

Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
In this paper we present a method of synthesis of adaptive schedulers for real-time embedded systems. We assume that the system is implemented using multi-core embedded processor with low-power processing capabilities. First, the developmental genetic programming is used to generate the scheduler and the initial schedule. Then, during the system execution the scheduler modifies the schedule whenever execution time of the recently finished task occurred shorter or longer than expected. The goal of rescheduling is to minimize the power consumption while all time constraints will be satisfied. We present real-life example as well as some experimental results showing advantages of our method.(original abstract)
Słowa kluczowe
Rocznik
Tom
5
Strony
449--459
Opis fizyczny
Twórcy
  • Department of Computer Science Kielce University of Technology Kielce, Poland
  • Department of Computer Science Kielce University of Technology Kielce, Poland
Bibliografia
  • big.LITTLE Processing with ARMCortexTM - A15 & Cortex-A7, ARM Holdings, September 2013, http://www.arm.com/files/downloads/ big.LITTLE_Final.pdf.
  • J.Luo, N.K. Jha, Low Power Distributed Embedded Systems: Dynamic Voltage Scaling and Synthesis, Proc. 9th Int. Conference High Performance Computing - HiPC 2002, Lecture Notes in Computer Science, vol. 2552, 2002, pp. 679-693. http://dx.doi.org/10.1007/3-540-36265-7_ 63
  • Hartmann S., Briskorn D., A survey of variants and extensions of the resource-constrained project scheduling problem, European journal of operational research : EJOR. - Amsterdam : Elsevier, Vol. 207., 1 (16.11.), pp. 1-15 (2010). http://dx.doi.org/10.1016/j.ejor.2009.11.005
  • Hartmann, S. (1998). An competitive genetic algorithm for resourceconstrained project scheduling. Naval Research Logistics, 45(7), 733- 750. http://dx.doi.org/10.1002/(SICI)1520-6750(199810)45:7 733:: AID-NAV5 3.3.CO;2-7
  • Xiang Li, Lishan Kang, Wei Tan, "Optimized Research of Resource Constrained Project Scheduling Problem Based on Genetic Algorithms", Lecture Notes in Computer Science, Vol. 4683, 2007, pp 177-186. http: //dx.doi.org/10.1007/978-3-540-74581-5_19
  • Hossein Zoulfaghari, Javad Nematian, Nader Mahmoudi, and Mehdi Khodabandeh. 2013. A New Genetic Algorithm for the RCPSP in Large Scale. Int. J. Appl. Evol. Comput. 4, 2 (April 2013), 29-40. http://dx.doi.org/10.4018/jaec.2013040103
  • K.M. Calhoun, R.F. Deckro, J.T. Moore, J.W. Chrissis, J.C.V. Hove, Planning and re-planning in project and production scheduling, Omega The international Journal of Management Science 30 (3) (2002) 155- 170. http://dx.doi.org/10.1016/S0305-0483(02)00024-5
  • S. Van de Vonder, E.L. Demeulemeester,W.S. Herroelen, A classification of predictive-reactive project scheduling procedures, Journal of Scheduling 10 (3) (2007) 195-207. http://dx.doi.org/10.1007/s10951-007-0011-2
  • H. Sakkout, M. Wallace, Probe backtrack search for minimal perturbation in dynamic scheduling, Constraints 5 (4) (2000) 359-388. http://dx.doi.org/10.1023/A:1009856210543
  • M. Al-Fawzan, M. Haouari, A bi-objective model for robust resourceconstrained project scheduling, International Journal of Production Economics 96 (2005) pp.175-187. http://dx.doi.org/10.1016/j.ijpe.2004.04. 002
  • Brian Jeff, "Ten Things to Know About big.LITTLE". ARM Holdings, 2013,http://community.arm.com/groups/processors/blog/2013/ 06/18/ten-things-to-know-about-biglittle
  • Z. Michalewicz, Genetic Algorithms + Data Structures = Evolution Programs, Springer-Verlag Berlin Heidelberg, 1996. http://dx.doi.org/10. 1007/978-3-662-03315-9
  • Dick, R.P., Jha, N.K.: MOGAC: A Multiobjective Genetic Algorithm for the CoSynthesis of Hardware-Software Embedded Systems. IEEE Trans. on ComputerAided Design of Integrated Circuits and Systems 17(10), 920-935 (1998). http://dx.doi.org/10.1109/43.728914
  • Koza, J., Bennett III , F. H., Andre, D., Keane, M. A., 1998. Evolutionary Design of Analog Electrical Circuits Using Genetic Programming. In: I. C. Parmee (ed.), Adaptive Computing in Design and Manufacture. http://dx.doi.org/10.1007/978-1-4471-1589-2_14
  • J.R.Koza, R.Poli, "Genetic Programming", In Edmund Burke and Graham Kendal, editors. "Search Methodologies: Introductory Tutorials in Optimization and Decision Support Techniques", Chapter 5. Springer, 2005. http://dx.doi.org/10.1007/0-387-28356-0_5
  • S.Deniziak, A.Górski, "Hardware/Software Co-Synthesis of Distributed Embedded Systems Using Genetic Programming", Lecture Notes in Computer Science, Springer-Verlag, 2008, pp.83-93. http://dx.doi.org/ 10.1007/978-3-540-85857-7_8
  • S. Deniziak, L. Ciopin´ski, G. Pawiński, K.Wieczorek and S. Bąk "Cost Optimization of Real-Time Cloud Applications Using Developmental Genetic Programing", Proc. of the 7th IEEE/ACM International Conference on Utility and Cloud Computing, 2014, pp.774-779. http://dx.doi.org/10.1109/UCC.2014.126
  • K.Sapiecha, L. Ciopiński, and S. Deniziak. "An application of developmental genetic programming for automatic creation of supervisors of multi-task real-time object-oriented systems." IEEE Federated Conference on Computer Science and Information Systems (FedCSIS), 2014. http://dx.doi.org/10.15439/2014F208
  • Hu, Jingcao, and Radu Marculescu. "Energy-and performance-aware mapping for regular NoC architectures." Computer-Aided Design of Integrated Circuits and Systems, IEEE Transactions on 24.4 (2005): 551- 562. http://dx.doi.org/10.1109/TCAD.2005.844106
  • Han, Sangchul and Park, Minkyu, Predictability of Least Laxity First Scheduling Algorithm on Multiprocessor Real-Time Systems, Proc. of EUC Workshops, Lecture Notes in Computer Science, vol.4097, 2006, pp.755-764 http://dx.doi.org/10.1007/11807964_76
  • Sitek, P. "A hybrid CP/MP approach to supply chain modelling, optimization and analysis." Computer Science and Information Systems (FedCSIS), 2014 Federated Conference on. IEEE, 2014. http://dx.doi.org/10.15439/2014F89
Typ dokumentu
Bibliografia
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Identyfikator YADDA
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