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On-line adaptive algorithms in autonomic restart control

Publication ,  Journal Article
Okamura, H; Dohi, T; Trivedi, KS
Published in: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
December 15, 2010

Restarts or retries are typical control schemes to meet a deadline in real-time systems, and are regarded as significant environmental diversity techniques in dependable computing. This paper reconsiders a restart control studied by van Moorsel and Wolter (2006), and refines their result from theoretical and statistical points of views. Based on the optimality principle, we show that the time-fixed restart time is best even in non-stationary control setting under the assumption of unbounded restart opportunities. Next we study statistical inference for the restart time interval and develop on-line adaptive algorithms for estimating the optimal restart time interval via non-parametric estimation and reinforcement learning. Finally, these algorithms are compared in a simulation study. © 2010 Springer-Verlag.

Duke Scholars

Published In

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

DOI

EISSN

1611-3349

ISSN

0302-9743

Publication Date

December 15, 2010

Volume

6407 LNCS

Start / End Page

32 / 46

Related Subject Headings

  • Artificial Intelligence & Image Processing
  • 46 Information and computing sciences
 

Citation

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Okamura, H., Dohi, T., & Trivedi, K. S. (2010). On-line adaptive algorithms in autonomic restart control. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 6407 LNCS, 32–46. https://doi.org/10.1007/978-3-642-16576-4_3
Okamura, H., T. Dohi, and K. S. Trivedi. “On-line adaptive algorithms in autonomic restart control.” Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 6407 LNCS (December 15, 2010): 32–46. https://doi.org/10.1007/978-3-642-16576-4_3.
Okamura H, Dohi T, Trivedi KS. On-line adaptive algorithms in autonomic restart control. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2010 Dec 15;6407 LNCS:32–46.
Okamura, H., et al. “On-line adaptive algorithms in autonomic restart control.” Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 6407 LNCS, Dec. 2010, pp. 32–46. Scopus, doi:10.1007/978-3-642-16576-4_3.
Okamura H, Dohi T, Trivedi KS. On-line adaptive algorithms in autonomic restart control. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2010 Dec 15;6407 LNCS:32–46.

Published In

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

DOI

EISSN

1611-3349

ISSN

0302-9743

Publication Date

December 15, 2010

Volume

6407 LNCS

Start / End Page

32 / 46

Related Subject Headings

  • Artificial Intelligence & Image Processing
  • 46 Information and computing sciences