Abstract: In this paper we present a method of synthesis of adaptive schedulers for power aware real-time embedded software. We assume that the system is specified as a task graph, which should be scheduled on multi-core embedded processor with low-power processing capabilities. First, the developmental genetic programming is used to generate the scheduler and the initial schedule. The scheduler and the initial schedule are optimized taking into consideration power consumption as well as self-adaptivity capabilities. 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.
B I B L I O G R A F I A1. big.LITTLE processing with ARMCortexTM
- A15 & Cortex-A7, ARM holdings, September 2013. http://www.arm.com/files/downloads/big.LITTLE_Final.pdf
2.
Deniziak, S., Ciopinski, L.: Synthesis of power aware adaptive schedulers for embedded systems using developmental genetic programming. In: Federated Conference on Computer Science and Information Systems (FedCSIS). IEEE (2015). http://dx.doi.org/10.15439/2015F313
3.
Luo, J., Jha, N.K.: Low power distributed embedded systems: dynamic voltage scaling and synthesis. In: Proceedings of the 9th International Conference on High Performance Computing - HiPC 2002. Lecture Notes in Computer Science, vol. 2552, pp. 679–693 (2002). http://dx.doi.org/10.1007/3-540-36265-7_63
4.
Hartmann, S., Briskorn, D.: A survey of variants and extensions of the resource-constrained project scheduling problem. Eur. J. Oper. Res.: EJOR. vol. 207, 1 (16.11.), pp. 1–15. Elsevier, Amsterdam (2010). http://dx.doi.org/10.1016/j.ejor.2009.11.005
5.
Hartmann, S.: An competitive genetic algorithm for resource-constrained project scheduling. Nav. Res. Logist. 45(7), 733–750 (1998). http://dx.doi.org/10.1002/(SICI)1520-6750(199810)45:7%3C733::AID-NAV5%3E3.3.CO
2-7
6.
Li, X., Kang, L., Tan, W.: Optimized research of resource constrained project scheduling problem based on genetic algorithms. Lecture Notes in Computer Science, vol. 4683, pp. 177–186 (2007). http://dx.doi.org/10.1007/978-3-540-74581-5_19
7.
Zoulfaghari, H., Nematian, J., Mahmoudi, N., Khodabandeh, M.: A new genetic algorithm for the RCPSP in large scale. Int. J. Appl. Evol. Comput. 4(2), 29–40 (2013). http://dx.doi.org/10.4018/jaec.2013040103
8.
Calhoun, K.M., Deckro, R.F., Moore, J.T., Chrissis, J.W., Hove, J.C.V.: Planning and re-planning in project and production scheduling, Omega Int. J. Manag. Sci. 30(3), 155–170 (2002). http://dx.doi.org/10.1016/S0305-0483(02)00024-5
9.
Van de Vonder, S., Demeulemeester, E.L., Herroelen, W.S.: A classification of predictive-reactive project scheduling procedures. J. Sched. 10(3), 195–207 (2007). http://dx.doi.org/10.1007/s10951-007-0011-2
10.
Sakkout, H., Wallace, M.: Probe backtrack search for minimal perturbation in dynamic scheduling. Constraints 5(4), 359–388 (2000). http://dx.doi.org/10.1023/A:1009856210543
11.
Al-Fawzan, M., Haouari, M.: A bi-objective model for robust resourceconstrained project scheduling. Int. J. Prod. Econ. 96, 175–187 (2005). http://dx.doi.org/10.1016/j.ijpe.2004.04.002
12.
Jeff, B.: 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
13.
Michalewicz, Z.: Genetic Algorithms + Data Structures = Evolution Programs. Springer, Berlin (1996). http://dx.doi.org/10.1007/978-3-662-03315-9
14.
Dick, R.P., Jha, N.K.: MOGAC: A multiobjective genetic algorithm for the cosynthesis of hardware-software embedded systems. IEEE Trans. Comput.Aided Des. Integr. Circuits Syst. 17(10), 920–935 (1998). http://dx.doi.org/10.1109/43.728914
15.
Koza, J., Bennett III, F. H., Andre, D., Keane, M. A.: Evolutionary design of analog electrical circuits using genetic programming. In: Parmee, I.C. (ed.) Adaptive Computing in Design and Manufacture (1998). http://dx.doi.org/10.1007/978-1-4471-1589-2_14
16.
Koza, J.R., Poli, R.: Genetic programming. In: Burke, E., Kendal, G. (eds.) Search Methodologies: Introductory Tutorials in Optimization and Decision Support Techniques. Springer, New York (2005). http://dx.doi.org/10.1007/0-387-28356-0_5
17.
Deniziak, S., Górski, A.: Hardware/Software Co-Synthesis of Distributed Embedded Systems Using Genetic Programming. Lecture Notes in Computer Science, pp. 83–93. Springer, New York (2008). http://dx.doi.org/10.1007/978-3-540-85857-7_8
18.
Deniziak, S., Ciopiński, L., Pawiński, G., Wieczorek, K., Bak, S.: Cost optimization of real-time cloud applications using developmental genetic programing. In: Proceedings of the 7th IEEE/ACM International Conference on Utility and Cloud Computing, pp. 774–779 (2014). http://dx.doi.org/10.1109/UCC.2014.126
19.
Sapiecha, K., Ciopiński, L., Deniziak, S.: An application of developmental genetic programming for automatic creation of supervisors of multi-task real-time object-oriented systems. In: IEEE Federated Conference on Computer Science and Information Systems (FedCSIS) (2014). http://dx.doi.org/10.15439/2014F208
20.
Hu, J., Marculescu, R.: Energy-and performance-aware mapping for regular NoC architectures. IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst. 24(4), 551–562 (2005). http://dx.doi.org/10.1109/TCAD.2005.844106
21.
Han, S., Park, M.: Predictability of least laxity first scheduling algorithm on multiprocessor real-time systems. In: Proceedings of EUC Workshops. Lecture Notes in Computer Science, vol. 4097, pp. 755–764 (2006). http://dx.doi.org/10.1007/11807964_76
22.
Sitek, P.: A hybrid CP/MP approach to supply chain modelling, optimization and analysis. In: Federated Conference on Computer Science and Information Systems (FedCSIS). IEEE (2014). http://dx.doi.org/10.15439/2014F89 
Pełny tekst 
DOI 
Web of Science