Notice: Undefined index: linkPowrot in C:\wwwroot\wwwroot\publikacje\publikacje.php on line 1275
Publikacje
Pomoc (F2)
[38920] Artykuł:

A synthesis of adaptive, low-power real-time embedded systems for ARM big.LITTLE technology

Czasopismo: Measurement Automation Monitoring   Tom: 7, Zeszyt: 61, Strony: 340-342
ISSN:  0032-4140
Opublikowano: 2015
Liczba arkuszy wydawniczych:  0.07
 
  Autorzy / Redaktorzy / Twórcy
Imię i nazwisko Wydział Katedra Procent
udziału
Liczba
punktów
Leszek Ciopiński orcid logoWEAiIKatedra Systemów Informatycznych *505.50  
Roman Stanisław Deniziak orcid logoWEAiIKatedra Systemów Informatycznych *505.50  

Grupa MNiSW:  Publikacja w recenzowanym czasopiśmie wymienionym w wykazie ministra MNiSzW (część B)
Punkty MNiSW: 11


Pełny tekstPełny tekst    
Keywords:

self-adaptive system  real-time embedded system  adaptive scheduler  Developmental Genetic Programming  ARM big.LITTLE  



Abstract:

In this paper, we present a method of a synthesis of adaptive schedulers for real-time embedded systems. We assume that the system is implemented using a 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 the execution time of the recently finished task has been shorter or longer than expected. The goal of rescheduling is to minimize the power consumption while all time constraints will be satisfied. We present a real-life example as well as some experimental results showing the advantages of the method.



B   I   B   L   I   O   G   R   A   F   I   A
[1] big.LITTLE Processing with ARM Cortex™-A15 & Cortex-A7,
ARM Holdings, September 2013, http://www.arm.com/files/
downloads/big.LITTLE_Final.pdf.
[2] Luo J., Jha N.K.: 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.
[3] 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).
[4] 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.
[5] Hossein Zoulfaghari, Javad Nematian, Nader Mahmoudi, and Mehdi
Khodabandeh: A New Genetic Algorithm for the RCPSP in Large
Scale. Int. J. Appl. Evol. Comput. 4, 2 (April 2013), 29-40.
[6] Van de Vonder S., Demeulemeester E.L., Herroelen W.S.:
A classification of predictive-reactive project scheduling procedures.
Journal of Scheduling 10 (3) (2007) 195–207.
[7] Al-Fawzan M., Haouari M.: A bi-objective model for robust resource
constrained project scheduling. International Journal of Production
Economics 96 (2005) pp.175-187.
[8] Michalewicz Z.: Genetic Algorithms + Data Structures = Evolution
Programs. Springer-Verlag Berlin Heidelberg, 1996.
[9] Koza J.R., Poli R.: Genetic Programming. In Edmund Burke and
Graham Kendal, editors: Search Methodologies: Introductory
Tutorials in Optimization and Decision Support Techniques. Chapter
5. Springer, 2005.
[10] Deniziak S., Ciopiński L., Pawiński G.: Design of Real-Time
Computer-Based Systems using Developmental Genetic
Programming. In Handbook of Genetic Programming Applications,
eds. Amir H. Gandomi, Amir H. Alavi, and Conor Ryan, Springer,
2015, in print.
[11] Sapiecha K., Ciopiński L., and Deniziak S.: 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.
[12] Deniziak S. and Ciopiński L.: Synthesis of Power Aware Adaptive
Schedulers for Embedded Systems using Developmental Genetic
Programming. IEEE Federated Conference on Computer Science and
Information Systems (FedCSIS), 2015.